experiment design in psychology

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2.2 Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behavior

Learning objectives.

• Differentiate the goals of descriptive, correlational, and experimental research designs and explain the advantages and disadvantages of each.
• Explain the goals of descriptive research and the statistical techniques used to interpret it.
• Summarize the uses of correlational research and describe why correlational research cannot be used to infer causality.
• Review the procedures of experimental research and explain how it can be used to draw causal inferences.

Psychologists agree that if their ideas and theories about human behavior are to be taken seriously, they must be backed up by data. However, the research of different psychologists is designed with different goals in mind, and the different goals require different approaches. These varying approaches, summarized in Table 2.2 “Characteristics of the Three Research Designs” , are known as research designs . A research design is the specific method a researcher uses to collect, analyze, and interpret data . Psychologists use three major types of research designs in their research, and each provides an essential avenue for scientific investigation. Descriptive research is research designed to provide a snapshot of the current state of affairs . Correlational research is research designed to discover relationships among variables and to allow the prediction of future events from present knowledge . Experimental research is research in which initial equivalence among research participants in more than one group is created, followed by a manipulation of a given experience for these groups and a measurement of the influence of the manipulation . Each of the three research designs varies according to its strengths and limitations, and it is important to understand how each differs.

Table 2.2 Characteristics of the Three Research Designs

Stangor, C. (2011). Research methods for the behavioral sciences (4th ed.). Mountain View, CA: Cengage.

Descriptive Research: Assessing the Current State of Affairs

Descriptive research is designed to create a snapshot of the current thoughts, feelings, or behavior of individuals. This section reviews three types of descriptive research: case studies , surveys , and naturalistic observation .

Sometimes the data in a descriptive research project are based on only a small set of individuals, often only one person or a single small group. These research designs are known as case studies — descriptive records of one or more individual’s experiences and behavior . Sometimes case studies involve ordinary individuals, as when developmental psychologist Jean Piaget used his observation of his own children to develop his stage theory of cognitive development. More frequently, case studies are conducted on individuals who have unusual or abnormal experiences or characteristics or who find themselves in particularly difficult or stressful situations. The assumption is that by carefully studying individuals who are socially marginal, who are experiencing unusual situations, or who are going through a difficult phase in their lives, we can learn something about human nature.

Sigmund Freud was a master of using the psychological difficulties of individuals to draw conclusions about basic psychological processes. Freud wrote case studies of some of his most interesting patients and used these careful examinations to develop his important theories of personality. One classic example is Freud’s description of “Little Hans,” a child whose fear of horses the psychoanalyst interpreted in terms of repressed sexual impulses and the Oedipus complex (Freud (1909/1964).

Political polls reported in newspapers and on the Internet are descriptive research designs that provide snapshots of the likely voting behavior of a population.

Another well-known case study is Phineas Gage, a man whose thoughts and emotions were extensively studied by cognitive psychologists after a railroad spike was blasted through his skull in an accident. Although there is question about the interpretation of this case study (Kotowicz, 2007), it did provide early evidence that the brain’s frontal lobe is involved in emotion and morality (Damasio et al., 2005). An interesting example of a case study in clinical psychology is described by Rokeach (1964), who investigated in detail the beliefs and interactions among three patients with schizophrenia, all of whom were convinced they were Jesus Christ.

In other cases the data from descriptive research projects come in the form of a survey — a measure administered through either an interview or a written questionnaire to get a picture of the beliefs or behaviors of a sample of people of interest . The people chosen to participate in the research (known as the sample ) are selected to be representative of all the people that the researcher wishes to know about (the population ). In election polls, for instance, a sample is taken from the population of all “likely voters” in the upcoming elections.

The results of surveys may sometimes be rather mundane, such as “Nine out of ten doctors prefer Tymenocin,” or “The median income in Montgomery County is $36,712.” Yet other times (particularly in discussions of social behavior), the results can be shocking: “More than 40,000 people are killed by gunfire in the United States every year,” or “More than 60% of women between the ages of 50 and 60 suffer from depression.” Descriptive research is frequently used by psychologists to get an estimate of the prevalence (or incidence ) of psychological disorders.

A final type of descriptive research—known as naturalistic observation —is research based on the observation of everyday events . For instance, a developmental psychologist who watches children on a playground and describes what they say to each other while they play is conducting descriptive research, as is a biopsychologist who observes animals in their natural habitats. One example of observational research involves a systematic procedure known as the strange situation , used to get a picture of how adults and young children interact. The data that are collected in the strange situation are systematically coded in a coding sheet such as that shown in Table 2.3 “Sample Coding Form Used to Assess Child’s and Mother’s Behavior in the Strange Situation” .

Table 2.3 Sample Coding Form Used to Assess Child’s and Mother’s Behavior in the Strange Situation

The results of descriptive research projects are analyzed using descriptive statistics — numbers that summarize the distribution of scores on a measured variable . Most variables have distributions similar to that shown in Figure 2.5 “Height Distribution” , where most of the scores are located near the center of the distribution, and the distribution is symmetrical and bell-shaped. A data distribution that is shaped like a bell is known as a normal distribution .

Table 2.4 Height and Family Income for 25 Students

Figure 2.5 Height Distribution

The distribution of the heights of the students in a class will form a normal distribution. In this sample the mean ( M ) = 67.12 and the standard deviation ( s ) = 2.74.

A distribution can be described in terms of its central tendency —that is, the point in the distribution around which the data are centered—and its dispersion , or spread. The arithmetic average, or arithmetic mean , is the most commonly used measure of central tendency . It is computed by calculating the sum of all the scores of the variable and dividing this sum by the number of participants in the distribution (denoted by the letter N ). In the data presented in Figure 2.5 “Height Distribution” , the mean height of the students is 67.12 inches. The sample mean is usually indicated by the letter M .

In some cases, however, the data distribution is not symmetrical. This occurs when there are one or more extreme scores (known as outliers ) at one end of the distribution. Consider, for instance, the variable of family income (see Figure 2.6 “Family Income Distribution” ), which includes an outlier (a value of $3,800,000). In this case the mean is not a good measure of central tendency. Although it appears from Figure 2.6 “Family Income Distribution” that the central tendency of the family income variable should be around $70,000, the mean family income is actually $223,960. The single very extreme income has a disproportionate impact on the mean, resulting in a value that does not well represent the central tendency.

The median is used as an alternative measure of central tendency when distributions are not symmetrical. The median is the score in the center of the distribution, meaning that 50% of the scores are greater than the median and 50% of the scores are less than the median . In our case, the median household income ($73,000) is a much better indication of central tendency than is the mean household income ($223,960).

Figure 2.6 Family Income Distribution

The distribution of family incomes is likely to be nonsymmetrical because some incomes can be very large in comparison to most incomes. In this case the median or the mode is a better indicator of central tendency than is the mean.

A final measure of central tendency, known as the mode , represents the value that occurs most frequently in the distribution . You can see from Figure 2.6 “Family Income Distribution” that the mode for the family income variable is $93,000 (it occurs four times).

In addition to summarizing the central tendency of a distribution, descriptive statistics convey information about how the scores of the variable are spread around the central tendency. Dispersion refers to the extent to which the scores are all tightly clustered around the central tendency, like this:

Or they may be more spread out away from it, like this:

One simple measure of dispersion is to find the largest (the maximum ) and the smallest (the minimum ) observed values of the variable and to compute the range of the variable as the maximum observed score minus the minimum observed score. You can check that the range of the height variable in Figure 2.5 “Height Distribution” is 72 – 62 = 10. The standard deviation , symbolized as s , is the most commonly used measure of dispersion . Distributions with a larger standard deviation have more spread. The standard deviation of the height variable is s = 2.74, and the standard deviation of the family income variable is s = $745,337.

An advantage of descriptive research is that it attempts to capture the complexity of everyday behavior. Case studies provide detailed information about a single person or a small group of people, surveys capture the thoughts or reported behaviors of a large population of people, and naturalistic observation objectively records the behavior of people or animals as it occurs naturally. Thus descriptive research is used to provide a relatively complete understanding of what is currently happening.

Despite these advantages, descriptive research has a distinct disadvantage in that, although it allows us to get an idea of what is currently happening, it is usually limited to static pictures. Although descriptions of particular experiences may be interesting, they are not always transferable to other individuals in other situations, nor do they tell us exactly why specific behaviors or events occurred. For instance, descriptions of individuals who have suffered a stressful event, such as a war or an earthquake, can be used to understand the individuals’ reactions to the event but cannot tell us anything about the long-term effects of the stress. And because there is no comparison group that did not experience the stressful situation, we cannot know what these individuals would be like if they hadn’t had the stressful experience.

Correlational Research: Seeking Relationships Among Variables

In contrast to descriptive research, which is designed primarily to provide static pictures, correlational research involves the measurement of two or more relevant variables and an assessment of the relationship between or among those variables. For instance, the variables of height and weight are systematically related (correlated) because taller people generally weigh more than shorter people. In the same way, study time and memory errors are also related, because the more time a person is given to study a list of words, the fewer errors he or she will make. When there are two variables in the research design, one of them is called the predictor variable and the other the outcome variable . The research design can be visualized like this, where the curved arrow represents the expected correlation between the two variables:

Figure 2.2.2

One way of organizing the data from a correlational study with two variables is to graph the values of each of the measured variables using a scatter plot . As you can see in Figure 2.10 “Examples of Scatter Plots” , a scatter plot is a visual image of the relationship between two variables . A point is plotted for each individual at the intersection of his or her scores for the two variables. When the association between the variables on the scatter plot can be easily approximated with a straight line, as in parts (a) and (b) of Figure 2.10 “Examples of Scatter Plots” , the variables are said to have a linear relationship .

When the straight line indicates that individuals who have above-average values for one variable also tend to have above-average values for the other variable, as in part (a), the relationship is said to be positive linear . Examples of positive linear relationships include those between height and weight, between education and income, and between age and mathematical abilities in children. In each case people who score higher on one of the variables also tend to score higher on the other variable. Negative linear relationships , in contrast, as shown in part (b), occur when above-average values for one variable tend to be associated with below-average values for the other variable. Examples of negative linear relationships include those between the age of a child and the number of diapers the child uses, and between practice on and errors made on a learning task. In these cases people who score higher on one of the variables tend to score lower on the other variable.

Relationships between variables that cannot be described with a straight line are known as nonlinear relationships . Part (c) of Figure 2.10 “Examples of Scatter Plots” shows a common pattern in which the distribution of the points is essentially random. In this case there is no relationship at all between the two variables, and they are said to be independent . Parts (d) and (e) of Figure 2.10 “Examples of Scatter Plots” show patterns of association in which, although there is an association, the points are not well described by a single straight line. For instance, part (d) shows the type of relationship that frequently occurs between anxiety and performance. Increases in anxiety from low to moderate levels are associated with performance increases, whereas increases in anxiety from moderate to high levels are associated with decreases in performance. Relationships that change in direction and thus are not described by a single straight line are called curvilinear relationships .

Figure 2.10 Examples of Scatter Plots

Some examples of relationships between two variables as shown in scatter plots. Note that the Pearson correlation coefficient ( r ) between variables that have curvilinear relationships will likely be close to zero.

Adapted from Stangor, C. (2011). Research methods for the behavioral sciences (4th ed.). Mountain View, CA: Cengage.

The most common statistical measure of the strength of linear relationships among variables is the Pearson correlation coefficient , which is symbolized by the letter r . The value of the correlation coefficient ranges from r = –1.00 to r = +1.00. The direction of the linear relationship is indicated by the sign of the correlation coefficient. Positive values of r (such as r = .54 or r = .67) indicate that the relationship is positive linear (i.e., the pattern of the dots on the scatter plot runs from the lower left to the upper right), whereas negative values of r (such as r = –.30 or r = –.72) indicate negative linear relationships (i.e., the dots run from the upper left to the lower right). The strength of the linear relationship is indexed by the distance of the correlation coefficient from zero (its absolute value). For instance, r = –.54 is a stronger relationship than r = .30, and r = .72 is a stronger relationship than r = –.57. Because the Pearson correlation coefficient only measures linear relationships, variables that have curvilinear relationships are not well described by r , and the observed correlation will be close to zero.

It is also possible to study relationships among more than two measures at the same time. A research design in which more than one predictor variable is used to predict a single outcome variable is analyzed through multiple regression (Aiken & West, 1991). Multiple regression is a statistical technique, based on correlation coefficients among variables, that allows predicting a single outcome variable from more than one predictor variable . For instance, Figure 2.11 “Prediction of Job Performance From Three Predictor Variables” shows a multiple regression analysis in which three predictor variables are used to predict a single outcome. The use of multiple regression analysis shows an important advantage of correlational research designs—they can be used to make predictions about a person’s likely score on an outcome variable (e.g., job performance) based on knowledge of other variables.

Figure 2.11 Prediction of Job Performance From Three Predictor Variables

Multiple regression allows scientists to predict the scores on a single outcome variable using more than one predictor variable.

An important limitation of correlational research designs is that they cannot be used to draw conclusions about the causal relationships among the measured variables. Consider, for instance, a researcher who has hypothesized that viewing violent behavior will cause increased aggressive play in children. He has collected, from a sample of fourth-grade children, a measure of how many violent television shows each child views during the week, as well as a measure of how aggressively each child plays on the school playground. From his collected data, the researcher discovers a positive correlation between the two measured variables.

Although this positive correlation appears to support the researcher’s hypothesis, it cannot be taken to indicate that viewing violent television causes aggressive behavior. Although the researcher is tempted to assume that viewing violent television causes aggressive play,

there are other possibilities. One alternate possibility is that the causal direction is exactly opposite from what has been hypothesized. Perhaps children who have behaved aggressively at school develop residual excitement that leads them to want to watch violent television shows at home:

Although this possibility may seem less likely, there is no way to rule out the possibility of such reverse causation on the basis of this observed correlation. It is also possible that both causal directions are operating and that the two variables cause each other:

Still another possible explanation for the observed correlation is that it has been produced by the presence of a common-causal variable (also known as a third variable ). A common-causal variable is a variable that is not part of the research hypothesis but that causes both the predictor and the outcome variable and thus produces the observed correlation between them . In our example a potential common-causal variable is the discipline style of the children’s parents. Parents who use a harsh and punitive discipline style may produce children who both like to watch violent television and who behave aggressively in comparison to children whose parents use less harsh discipline:

In this case, television viewing and aggressive play would be positively correlated (as indicated by the curved arrow between them), even though neither one caused the other but they were both caused by the discipline style of the parents (the straight arrows). When the predictor and outcome variables are both caused by a common-causal variable, the observed relationship between them is said to be spurious . A spurious relationship is a relationship between two variables in which a common-causal variable produces and “explains away” the relationship . If effects of the common-causal variable were taken away, or controlled for, the relationship between the predictor and outcome variables would disappear. In the example the relationship between aggression and television viewing might be spurious because by controlling for the effect of the parents’ disciplining style, the relationship between television viewing and aggressive behavior might go away.

Common-causal variables in correlational research designs can be thought of as “mystery” variables because, as they have not been measured, their presence and identity are usually unknown to the researcher. Since it is not possible to measure every variable that could cause both the predictor and outcome variables, the existence of an unknown common-causal variable is always a possibility. For this reason, we are left with the basic limitation of correlational research: Correlation does not demonstrate causation. It is important that when you read about correlational research projects, you keep in mind the possibility of spurious relationships, and be sure to interpret the findings appropriately. Although correlational research is sometimes reported as demonstrating causality without any mention being made of the possibility of reverse causation or common-causal variables, informed consumers of research, like you, are aware of these interpretational problems.

In sum, correlational research designs have both strengths and limitations. One strength is that they can be used when experimental research is not possible because the predictor variables cannot be manipulated. Correlational designs also have the advantage of allowing the researcher to study behavior as it occurs in everyday life. And we can also use correlational designs to make predictions—for instance, to predict from the scores on their battery of tests the success of job trainees during a training session. But we cannot use such correlational information to determine whether the training caused better job performance. For that, researchers rely on experiments.

Experimental Research: Understanding the Causes of Behavior

The goal of experimental research design is to provide more definitive conclusions about the causal relationships among the variables in the research hypothesis than is available from correlational designs. In an experimental research design, the variables of interest are called the independent variable (or variables ) and the dependent variable . The independent variable in an experiment is the causing variable that is created (manipulated) by the experimenter . The dependent variable in an experiment is a measured variable that is expected to be influenced by the experimental manipulation . The research hypothesis suggests that the manipulated independent variable or variables will cause changes in the measured dependent variables. We can diagram the research hypothesis by using an arrow that points in one direction. This demonstrates the expected direction of causality:

Figure 2.2.3

Research Focus: Video Games and Aggression

Consider an experiment conducted by Anderson and Dill (2000). The study was designed to test the hypothesis that viewing violent video games would increase aggressive behavior. In this research, male and female undergraduates from Iowa State University were given a chance to play with either a violent video game (Wolfenstein 3D) or a nonviolent video game (Myst). During the experimental session, the participants played their assigned video games for 15 minutes. Then, after the play, each participant played a competitive game with an opponent in which the participant could deliver blasts of white noise through the earphones of the opponent. The operational definition of the dependent variable (aggressive behavior) was the level and duration of noise delivered to the opponent. The design of the experiment is shown in Figure 2.17 “An Experimental Research Design” .

Figure 2.17 An Experimental Research Design

Two advantages of the experimental research design are (1) the assurance that the independent variable (also known as the experimental manipulation) occurs prior to the measured dependent variable, and (2) the creation of initial equivalence between the conditions of the experiment (in this case by using random assignment to conditions).

Experimental designs have two very nice features. For one, they guarantee that the independent variable occurs prior to the measurement of the dependent variable. This eliminates the possibility of reverse causation. Second, the influence of common-causal variables is controlled, and thus eliminated, by creating initial equivalence among the participants in each of the experimental conditions before the manipulation occurs.

The most common method of creating equivalence among the experimental conditions is through random assignment to conditions , a procedure in which the condition that each participant is assigned to is determined through a random process, such as drawing numbers out of an envelope or using a random number table . Anderson and Dill first randomly assigned about 100 participants to each of their two groups (Group A and Group B). Because they used random assignment to conditions, they could be confident that, before the experimental manipulation occurred, the students in Group A were, on average, equivalent to the students in Group B on every possible variable, including variables that are likely to be related to aggression, such as parental discipline style, peer relationships, hormone levels, diet—and in fact everything else.

Then, after they had created initial equivalence, Anderson and Dill created the experimental manipulation—they had the participants in Group A play the violent game and the participants in Group B play the nonviolent game. Then they compared the dependent variable (the white noise blasts) between the two groups, finding that the students who had viewed the violent video game gave significantly longer noise blasts than did the students who had played the nonviolent game.

Anderson and Dill had from the outset created initial equivalence between the groups. This initial equivalence allowed them to observe differences in the white noise levels between the two groups after the experimental manipulation, leading to the conclusion that it was the independent variable (and not some other variable) that caused these differences. The idea is that the only thing that was different between the students in the two groups was the video game they had played.

Despite the advantage of determining causation, experiments do have limitations. One is that they are often conducted in laboratory situations rather than in the everyday lives of people. Therefore, we do not know whether results that we find in a laboratory setting will necessarily hold up in everyday life. Second, and more important, is that some of the most interesting and key social variables cannot be experimentally manipulated. If we want to study the influence of the size of a mob on the destructiveness of its behavior, or to compare the personality characteristics of people who join suicide cults with those of people who do not join such cults, these relationships must be assessed using correlational designs, because it is simply not possible to experimentally manipulate these variables.

Key Takeaways

• Descriptive, correlational, and experimental research designs are used to collect and analyze data.
• Descriptive designs include case studies, surveys, and naturalistic observation. The goal of these designs is to get a picture of the current thoughts, feelings, or behaviors in a given group of people. Descriptive research is summarized using descriptive statistics.
• Correlational research designs measure two or more relevant variables and assess a relationship between or among them. The variables may be presented on a scatter plot to visually show the relationships. The Pearson Correlation Coefficient ( r ) is a measure of the strength of linear relationship between two variables.
• Common-causal variables may cause both the predictor and outcome variable in a correlational design, producing a spurious relationship. The possibility of common-causal variables makes it impossible to draw causal conclusions from correlational research designs.
• Experimental research involves the manipulation of an independent variable and the measurement of a dependent variable. Random assignment to conditions is normally used to create initial equivalence between the groups, allowing researchers to draw causal conclusions.

Exercises and Critical Thinking

• There is a negative correlation between the row that a student sits in in a large class (when the rows are numbered from front to back) and his or her final grade in the class. Do you think this represents a causal relationship or a spurious relationship, and why?
• Think of two variables (other than those mentioned in this book) that are likely to be correlated, but in which the correlation is probably spurious. What is the likely common-causal variable that is producing the relationship?
• Imagine a researcher wants to test the hypothesis that participating in psychotherapy will cause a decrease in reported anxiety. Describe the type of research design the investigator might use to draw this conclusion. What would be the independent and dependent variables in the research?

Aiken, L., & West, S. (1991). Multiple regression: Testing and interpreting interactions . Newbury Park, CA: Sage.

Ainsworth, M. S., Blehar, M. C., Waters, E., & Wall, S. (1978). Patterns of attachment: A psychological study of the strange situation . Hillsdale, NJ: Lawrence Erlbaum Associates.

Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life. Journal of Personality and Social Psychology, 78 (4), 772–790.

Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., Damasio, A. R., Cacioppo, J. T., & Berntson, G. G. (2005). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. In Social neuroscience: Key readings. (pp. 21–28). New York, NY: Psychology Press.

Freud, S. (1964). Analysis of phobia in a five-year-old boy. In E. A. Southwell & M. Merbaum (Eds.), Personality: Readings in theory and research (pp. 3–32). Belmont, CA: Wadsworth. (Original work published 1909)

Kotowicz, Z. (2007). The strange case of Phineas Gage. History of the Human Sciences, 20 (1), 115–131.

Rokeach, M. (1964). The three Christs of Ypsilanti: A psychological study . New York, NY: Knopf.

Introduction to Psychology Copyright © 2015 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

5.1 Experiment Basics

Learning objectives.

• Explain what an experiment is and recognize examples of studies that are experiments and studies that are not experiments.
• Distinguish between the manipulation of the independent variable and control of extraneous variables and explain the importance of each.
• Recognize examples of confounding variables and explain how they affect the internal validity of a study.

What Is an Experiment?

As we saw earlier in the book, an  experiment  is a type of study designed specifically to answer the question of whether there is a causal relationship between two variables. In other words, whether changes in an independent variable  cause  a change in a dependent variable. Experiments have two fundamental features. The first is that the researchers manipulate, or systematically vary, the level of the independent variable. The different levels of the independent variable are called conditions . For example, in Darley and Latané’s experiment, the independent variable was the number of witnesses that participants believed to be present. The researchers manipulated this independent variable by telling participants that there were either one, two, or five other students involved in the discussion, thereby creating three conditions. For a new researcher, it is easy to confuse  these terms by believing there are three independent variables in this situation: one, two, or five students involved in the discussion, but there is actually only one independent variable (number of witnesses) with three different levels or conditions (one, two or five students). The second fundamental feature of an experiment is that the researcher controls, or minimizes the variability in, variables other than the independent and dependent variable. These other variables are called extraneous variables . Darley and Latané tested all their participants in the same room, exposed them to the same emergency situation, and so on. They also randomly assigned their participants to conditions so that the three groups would be similar to each other to begin with. Notice that although the words  manipulation  and  control  have similar meanings in everyday language, researchers make a clear distinction between them. They manipulate  the independent variable by systematically changing its levels and control  other variables by holding them constant.

Manipulation of the Independent Variable

Again, to  manipulate  an independent variable means to change its level systematically so that different groups of participants are exposed to different levels of that variable, or the same group of participants is exposed to different levels at different times. For example, to see whether expressive writing affects people’s health, a researcher might instruct some participants to write about traumatic experiences and others to write about neutral experiences. As discussed earlier in this chapter, the different levels of the independent variable are referred to as  conditions , and researchers often give the conditions short descriptive names to make it easy to talk and write about them. In this case, the conditions might be called the “traumatic condition” and the “neutral condition.”

Notice that the manipulation of an independent variable must involve the active intervention of the researcher. Comparing groups of people who differ on the independent variable before the study begins is not the same as manipulating that variable. For example, a researcher who compares the health of people who already keep a journal with the health of people who do not keep a journal has not manipulated this variable and therefore has not conducted an experiment. This distinction  is important because groups that already differ in one way at the beginning of a study are likely to differ in other ways too. For example, people who choose to keep journals might also be more conscientious, more introverted, or less stressed than people who do not. Therefore, any observed difference between the two groups in terms of their health might have been caused by whether or not they keep a journal, or it might have been caused by any of the other differences between people who do and do not keep journals. Thus the active manipulation of the independent variable is crucial for eliminating potential alternative explanations for the results.

Of course, there are many situations in which the independent variable cannot be manipulated for practical or ethical reasons and therefore an experiment is not possible. For example, whether or not people have a significant early illness experience cannot be manipulated, making it impossible to conduct an experiment on the effect of early illness experiences on the development of hypochondriasis. This caveat does not mean it is impossible to study the relationship between early illness experiences and hypochondriasis—only that it must be done using nonexperimental approaches. We will discuss this type of methodology in detail later in the book.

Independent variables can be manipulated to create two conditions and experiments involving a single independent variable with two conditions is often referred to as a  single factor two-level design.  However, sometimes greater insights can be gained by adding more conditions to an experiment. When an experiment has one independent variable that is manipulated to produce more than two conditions it is referred to as a single factor multi level design.  So rather than comparing a condition in which there was one witness to a condition in which there were five witnesses (which would represent a single-factor two-level design), Darley and Latané’s used a single factor multi-level design, by manipulating the independent variable to produce three conditions (a one witness, a two witnesses, and a five witnesses condition).

Control of Extraneous Variables

As we have seen previously in the chapter, an  extraneous variable  is anything that varies in the context of a study other than the independent and dependent variables. In an experiment on the effect of expressive writing on health, for example, extraneous variables would include participant variables (individual differences) such as their writing ability, their diet, and their gender. They would also include situational or task variables such as the time of day when participants write, whether they write by hand or on a computer, and the weather. Extraneous variables pose a problem because many of them are likely to have some effect on the dependent variable. For example, participants’ health will be affected by many things other than whether or not they engage in expressive writing. This influencing factor can make it difficult to separate the effect of the independent variable from the effects of the extraneous variables, which is why it is important to  control  extraneous variables by holding them constant.

Extraneous Variables as “Noise”

Extraneous variables make it difficult to detect the effect of the independent variable in two ways. One is by adding variability or “noise” to the data. Imagine a simple experiment on the effect of mood (happy vs. sad) on the number of happy childhood events people are able to recall. Participants are put into a negative or positive mood (by showing them a happy or sad video clip) and then asked to recall as many happy childhood events as they can. The two leftmost columns of  Table 5.1 show what the data might look like if there were no extraneous variables and the number of happy childhood events participants recalled was affected only by their moods. Every participant in the happy mood condition recalled exactly four happy childhood events, and every participant in the sad mood condition recalled exactly three. The effect of mood here is quite obvious. In reality, however, the data would probably look more like those in the two rightmost columns of  Table 5.1 . Even in the happy mood condition, some participants would recall fewer happy memories because they have fewer to draw on, use less effective recall strategies, or are less motivated. And even in the sad mood condition, some participants would recall more happy childhood memories because they have more happy memories to draw on, they use more effective recall strategies, or they are more motivated. Although the mean difference between the two groups is the same as in the idealized data, this difference is much less obvious in the context of the greater variability in the data. Thus one reason researchers try to control extraneous variables is so their data look more like the idealized data in  Table 5.1 , which makes the effect of the independent variable easier to detect (although real data never look quite  that  good).

One way to control extraneous variables is to hold them constant. This technique can mean holding situation or task variables constant by testing all participants in the same location, giving them identical instructions, treating them in the same way, and so on. It can also mean holding participant variables constant. For example, many studies of language limit participants to right-handed people, who generally have their language areas isolated in their left cerebral hemispheres. Left-handed people are more likely to have their language areas isolated in their right cerebral hemispheres or distributed across both hemispheres, which can change the way they process language and thereby add noise to the data.

In principle, researchers can control extraneous variables by limiting participants to one very specific category of person, such as 20-year-old, heterosexual, female, right-handed psychology majors. The obvious downside to this approach is that it would lower the external validity of the study—in particular, the extent to which the results can be generalized beyond the people actually studied. For example, it might be unclear whether results obtained with a sample of younger heterosexual women would apply to older homosexual men. In many situations, the advantages of a diverse sample (increased external validity) outweigh the reduction in noise achieved by a homogeneous one.

Extraneous Variables as Confounding Variables

The second way that extraneous variables can make it difficult to detect the effect of the independent variable is by becoming confounding variables. A confounding variable  is an extraneous variable that differs on average across  levels of the independent variable (i.e., it is an extraneous variable that varies systematically with the independent variable). For example, in almost all experiments, participants’ intelligence quotients (IQs) will be an extraneous variable. But as long as there are participants with lower and higher IQs in each condition so that the average IQ is roughly equal across the conditions, then this variation is probably acceptable (and may even be desirable). What would be bad, however, would be for participants in one condition to have substantially lower IQs on average and participants in another condition to have substantially higher IQs on average. In this case, IQ would be a confounding variable.

To confound means to confuse , and this effect is exactly why confounding variables are undesirable. Because they differ systematically across conditions—just like the independent variable—they provide an alternative explanation for any observed difference in the dependent variable.  Figure 5.1  shows the results of a hypothetical study, in which participants in a positive mood condition scored higher on a memory task than participants in a negative mood condition. But if IQ is a confounding variable—with participants in the positive mood condition having higher IQs on average than participants in the negative mood condition—then it is unclear whether it was the positive moods or the higher IQs that caused participants in the first condition to score higher. One way to avoid confounding variables is by holding extraneous variables constant. For example, one could prevent IQ from becoming a confounding variable by limiting participants only to those with IQs of exactly 100. But this approach is not always desirable for reasons we have already discussed. A second and much more general approach—random assignment to conditions—will be discussed in detail shortly.

Figure 5.1 Hypothetical Results From a Study on the Effect of Mood on Memory. Because IQ also differs across conditions, it is a confounding variable.

Key Takeaways

• An experiment is a type of empirical study that features the manipulation of an independent variable, the measurement of a dependent variable, and control of extraneous variables.
• An extraneous variable is any variable other than the independent and dependent variables. A confound is an extraneous variable that varies systematically with the independent variable.
• Practice: List five variables that can be manipulated by the researcher in an experiment. List five variables that cannot be manipulated by the researcher in an experiment.
• Effect of parietal lobe damage on people’s ability to do basic arithmetic.
• Effect of being clinically depressed on the number of close friendships people have.
• Effect of group training on the social skills of teenagers with Asperger’s syndrome.
• Effect of paying people to take an IQ test on their performance on that test.

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Research Methods in Psychology

5. factorial designs ¶.

We have usually no knowledge that any one factor will exert its effects independently of all others that can be varied, or that its effects are particularly simply related to variations in these other factors. —Ronald Fisher

In Chapter 1 we briefly described a study conducted by Simone Schnall and her colleagues, in which they found that washing one’s hands leads people to view moral transgressions as less wrong [SBH08] . In a different but related study, Schnall and her colleagues investigated whether feeling physically disgusted causes people to make harsher moral judgments [SHCJ08] . In this experiment, they manipulated participants’ feelings of disgust by testing them in either a clean room or a messy room that contained dirty dishes, an overflowing wastebasket, and a chewed-up pen. They also used a self-report questionnaire to measure the amount of attention that people pay to their own bodily sensations. They called this “private body consciousness”. They measured their primary dependent variable, the harshness of people’s moral judgments, by describing different behaviors (e.g., eating one’s dead dog, failing to return a found wallet) and having participants rate the moral acceptability of each one on a scale of 1 to 7. They also measured some other dependent variables, including participants’ willingness to eat at a new restaurant. Finally, the researchers asked participants to rate their current level of disgust and other emotions. The primary results of this study were that participants in the messy room were in fact more disgusted and made harsher moral judgments than participants in the clean room—but only if they scored relatively high in private body consciousness.

The research designs we have considered so far have been simple—focusing on a question about one variable or about a statistical relationship between two variables. But in many ways, the complex design of this experiment undertaken by Schnall and her colleagues is more typical of research in psychology. Fortunately, we have already covered the basic elements of such designs in previous chapters. In this chapter, we look closely at how and why researchers combine these basic elements into more complex designs. We start with complex experiments—considering first the inclusion of multiple dependent variables and then the inclusion of multiple independent variables. Finally, we look at complex correlational designs.

5.1. Multiple Dependent Variables ¶

5.1.1. learning objectives ¶.

Explain why researchers often include multiple dependent variables in their studies.

Explain what a manipulation check is and when it would be included in an experiment.

Imagine that you have made the effort to find a research topic, review the research literature, formulate a question, design an experiment, obtain approval from teh relevant institutional review board (IRB), recruit research participants, and manipulate an independent variable. It would seem almost wasteful to measure a single dependent variable. Even if you are primarily interested in the relationship between an independent variable and one primary dependent variable, there are usually several more questions that you can answer easily by including multiple dependent variables.

5.1.2. Measures of Different Constructs ¶

Often a researcher wants to know how an independent variable affects several distinct dependent variables. For example, Schnall and her colleagues were interested in how feeling disgusted affects the harshness of people’s moral judgments, but they were also curious about how disgust affects other variables, such as people’s willingness to eat in a restaurant. As another example, researcher Susan Knasko was interested in how different odors affect people’s behavior [Kna92] . She conducted an experiment in which the independent variable was whether participants were tested in a room with no odor or in one scented with lemon, lavender, or dimethyl sulfide (which has a cabbage-like smell). Although she was primarily interested in how the odors affected people’s creativity, she was also curious about how they affected people’s moods and perceived health—and it was a simple enough matter to measure these dependent variables too. Although she found that creativity was unaffected by the ambient odor, she found that people’s moods were lower in the dimethyl sulfide condition, and that their perceived health was greater in the lemon condition.

When an experiment includes multiple dependent variables, there is again a possibility of carryover effects. For example, it is possible that measuring participants’ moods before measuring their perceived health could affect their perceived health or that measuring their perceived health before their moods could affect their moods. So the order in which multiple dependent variables are measured becomes an issue. One approach is to measure them in the same order for all participants—usually with the most important one first so that it cannot be affected by measuring the others. Another approach is to counterbalance, or systematically vary, the order in which the dependent variables are measured.

5.1.3. Manipulation Checks ¶

When the independent variable is a construct that can only be manipulated indirectly—such as emotions and other internal states—an additional measure of that independent variable is often included as a manipulation check. This is done to confirm that the independent variable was, in fact, successfully manipulated. For example, Schnall and her colleagues had their participants rate their level of disgust to be sure that those in the messy room actually felt more disgusted than those in the clean room.

Manipulation checks are usually done at the end of the procedure to be sure that the effect of the manipulation lasted throughout the entire procedure and to avoid calling unnecessary attention to the manipulation. Manipulation checks become especially important when the manipulation of the independent variable turns out to have no effect on the dependent variable. Imagine, for example, that you exposed participants to happy or sad movie music—intending to put them in happy or sad moods—but you found that this had no effect on the number of happy or sad childhood events they recalled. This could be because being in a happy or sad mood has no effect on memories for childhood events. But it could also be that the music was ineffective at putting participants in happy or sad moods. A manipulation check, in this case, a measure of participants’ moods, would help resolve this uncertainty. If it showed that you had successfully manipulated participants’ moods, then it would appear that there is indeed no effect of mood on memory for childhood events. But if it showed that you did not successfully manipulate participants’ moods, then it would appear that you need a more effective manipulation to answer your research question.

5.1.4. Measures of the Same Construct ¶

Another common approach to including multiple dependent variables is to operationalize and measure the same construct, or closely related ones, in different ways. Imagine, for example, that a researcher conducts an experiment on the effect of daily exercise on stress. The dependent variable, stress, is a construct that can be operationalized in different ways. For this reason, the researcher might have participants complete the paper-and-pencil Perceived Stress Scale and also measure their levels of the stress hormone cortisol. This is an example of the use of converging operations. If the researcher finds that the different measures are affected by exercise in the same way, then he or she can be confident in the conclusion that exercise affects the more general construct of stress.

When multiple dependent variables are different measures of the same construct - especially if they are measured on the same scale - researchers have the option of combining them into a single measure of that construct. Recall that Schnall and her colleagues were interested in the harshness of people’s moral judgments. To measure this construct, they presented their participants with seven different scenarios describing morally questionable behaviors and asked them to rate the moral acceptability of each one. Although the researchers could have treated each of the seven ratings as a separate dependent variable, these researchers combined them into a single dependent variable by computing their mean.

When researchers combine dependent variables in this way, they are treating them collectively as a multiple-response measure of a single construct. The advantage of this is that multiple-response measures are generally more reliable than single-response measures. However, it is important to make sure the individual dependent variables are correlated with each other by computing an internal consistency measure such as Cronbach’s \(\alpha\) . If they are not correlated with each other, then it does not make sense to combine them into a measure of a single construct. If they have poor internal consistency, then they should be treated as separate dependent variables.

5.1.5. Key Takeaways ¶

Researchers in psychology often include multiple dependent variables in their studies. The primary reason is that this easily allows them to answer more research questions with minimal additional effort.

When an independent variable is a construct that is manipulated indirectly, it is a good idea to include a manipulation check. This is a measure of the independent variable typically given at the end of the procedure to confirm that it was successfully manipulated.

Multiple measures of the same construct can be analyzed separately or combined to produce a single multiple-item measure of that construct. The latter approach requires that the measures taken together have good internal consistency.

5.1.6. Exercises ¶

Practice: List three independent variables for which it would be good to include a manipulation check. List three others for which a manipulation check would be unnecessary. Hint: Consider whether there is any ambiguity concerning whether the manipulation will have its intended effect.

Practice: Imagine a study in which the independent variable is whether the room where participants are tested is warm (30°) or cool (12°). List three dependent variables that you might treat as measures of separate variables. List three more that you might combine and treat as measures of the same underlying construct.

5.2. Multiple Independent Variables ¶

5.2.1. learning objectives ¶.

Explain why researchers often include multiple independent variables in their studies.

Define factorial design, and use a factorial design table to represent and interpret simple factorial designs.

Distinguish between main effects and interactions, and recognize and give examples of each.

Sketch and interpret bar graphs and line graphs showing the results of studies with simple factorial designs.

Just as it is common for studies in psychology to include multiple dependent variables, it is also common for them to include multiple independent variables. Schnall and her colleagues studied the effect of both disgust and private body consciousness in the same study. The tendency to include multiple independent variables in one experiment is further illustrated by the following titles of actual research articles published in professional journals:

The Effects of Temporal Delay and Orientation on Haptic Object Recognition

Opening Closed Minds: The Combined Effects of Intergroup Contact and Need for Closure on Prejudice

Effects of Expectancies and Coping on Pain-Induced Intentions to Smoke

The Effect of Age and Divided Attention on Spontaneous Recognition

The Effects of Reduced Food Size and Package Size on the Consumption Behavior of Restrained and Unrestrained Eaters

Just as including multiple dependent variables in the same experiment allows one to answer more research questions, so too does including multiple independent variables in the same experiment. For example, instead of conducting one study on the effect of disgust on moral judgment and another on the effect of private body consciousness on moral judgment, Schnall and colleagues were able to conduct one study that addressed both variables. But including multiple independent variables also allows the researcher to answer questions about whether the effect of one independent variable depends on the level of another. This is referred to as an interaction between the independent variables. Schnall and her colleagues, for example, observed an interaction between disgust and private body consciousness because the effect of disgust depended on whether participants were high or low in private body consciousness. As we will see, interactions are often among the most interesting results in psychological research.

5.2.2. Factorial Designs ¶

By far the most common approach to including multiple independent variables in an experiment is the factorial design. In a factorial design, each level of one independent variable (which can also be called a factor) is combined with each level of the others to produce all possible combinations. Each combination, then, becomes a condition in the experiment. Imagine, for example, an experiment on the effect of cell phone use (yes vs. no) and time of day (day vs. night) on driving ability. This is shown in the factorial design table in Figure 5.1 . The columns of the table represent cell phone use, and the rows represent time of day. The four cells of the table represent the four possible combinations or conditions: using a cell phone during the day, not using a cell phone during the day, using a cell phone at night, and not using a cell phone at night. This particular design is referred to as a 2 x 2 (read “two-by- two”) factorial design because it combines two variables, each of which has two levels. If one of the independent variables had a third level (e.g., using a hand-held cell phone, using a hands-free cell phone, and not using a cell phone), then it would be a 3 x 2 factorial design, and there would be six distinct conditions. Notice that the number of possible conditions is the product of the numbers of levels. A 2 x 2 factorial design has four conditions, a 3 x 2 factorial design has six conditions, a 4 x 5 factorial design would have 20 conditions, and so on.

Fig. 5.1 Factorial Design Table Representing a 2 x 2 Factorial Design ¶

In principle, factorial designs can include any number of independent variables with any number of levels. For example, an experiment could include the type of psychotherapy (cognitive vs. behavioral), the length of the psychotherapy (2 weeks vs. 2 months), and the sex of the psychotherapist (female vs. male). This would be a 2 x 2 x 2 factorial design and would have eight conditions. Figure 5.2 shows one way to represent this design. In practice, it is unusual for there to be more than three independent variables with more than two or three levels each.

This is for at least two reasons: For one, the number of conditions can quickly become unmanageable. For example, adding a fourth independent variable with three levels (e.g., therapist experience: low vs. medium vs. high) to the current example would make it a 2 x 2 x 2 x 3 factorial design with 24 distinct conditions. Second, the number of participants required to populate all of these conditions (while maintaining a reasonable ability to detect a real underlying effect) can render the design unfeasible (for more information, see the discussion about the importance of adequate statistical power in Chapter 13 ). As a result, in the remainder of this section we will focus on designs with two independent variables. The general principles discussed here extend in a straightforward way to more complex factorial designs.

Fig. 5.2 Factorial Design Table Representing a 2 x 2 x 2 Factorial Design ¶

5.2.3. Assigning Participants to Conditions ¶

Recall that in a simple between-subjects design, each participant is tested in only one condition. In a simple within-subjects design, each participant is tested in all conditions. In a factorial experiment, the decision to take the between-subjects or within-subjects approach must be made separately for each independent variable. In a between-subjects factorial design, all of the independent variables are manipulated between subjects. For example, all participants could be tested either while using a cell phone or while not using a cell phone and either during the day or during the night. This would mean that each participant was tested in one and only one condition. In a within-subjects factorial design, all of the independent variables are manipulated within subjects. All participants could be tested both while using a cell phone and while not using a cell phone and both during the day and during the night. This would mean that each participant was tested in all conditions. The advantages and disadvantages of these two approaches are the same as those discussed in Chapter 4 ). The between-subjects design is conceptually simpler, avoids carryover effects, and minimizes the time and effort of each participant. The within-subjects design is more efficient for the researcher and help to control extraneous variables.

It is also possible to manipulate one independent variable between subjects and another within subjects. This is called a mixed factorial design. For example, a researcher might choose to treat cell phone use as a within-subjects factor by testing the same participants both while using a cell phone and while not using a cell phone (while counterbalancing the order of these two conditions). But he or she might choose to treat time of day as a between-subjects factor by testing each participant either during the day or during the night (perhaps because this only requires them to come in for testing once). Thus each participant in this mixed design would be tested in two of the four conditions.

Regardless of whether the design is between subjects, within subjects, or mixed, the actual assignment of participants to conditions or orders of conditions is typically done randomly.

5.2.4. Non-manipulated Independent Variables ¶

In many factorial designs, one of the independent variables is a non-manipulated independent variable. The researcher measures it but does not manipulate it. The study by Schnall and colleagues is a good example. One independent variable was disgust, which the researchers manipulated by testing participants in a clean room or a messy room. The other was private body consciousness, a variable which the researchers simply measured. Another example is a study by Halle Brown and colleagues in which participants were exposed to several words that they were later asked to recall [BKD+99] . The manipulated independent variable was the type of word. Some were negative, health-related words (e.g., tumor, coronary), and others were not health related (e.g., election, geometry). The non-manipulated independent variable was whether participants were high or low in hypochondriasis (excessive concern with ordinary bodily symptoms). Results from this study suggested that participants high in hypochondriasis were better than those low in hypochondriasis at recalling the health-related words, but that they were no better at recalling the non-health-related words.

Such studies are extremely common, and there are several points worth making about them. First, non-manipulated independent variables are usually participant characteristics (private body consciousness, hypochondriasis, self-esteem, and so on), and as such they are, by definition, between-subject factors. For example, people are either low in hypochondriasis or high in hypochondriasis; they cannot be in both of these conditions. Second, such studies are generally considered to be experiments as long as at least one independent variable is manipulated, regardless of how many non-manipulated independent variables are included. Third, it is important to remember that causal conclusions can only be drawn about the manipulated independent variable. For example, Schnall and her colleagues were justified in concluding that disgust affected the harshness of their participants’ moral judgments because they manipulated that variable and randomly assigned participants to the clean or messy room. But they would not have been justified in concluding that participants’ private body consciousness affected the harshness of their participants’ moral judgments because they did not manipulate that variable. It could be, for example, that having a strict moral code and a heightened awareness of one’s body are both caused by some third variable (e.g., neuroticism). Thus it is important to be aware of which variables in a study are manipulated and which are not.

5.2.5. Graphing the Results of Factorial Experiments ¶

The results of factorial experiments with two independent variables can be graphed by representing one independent variable on the x-axis and representing the other by using different kinds of bars or lines. (The y-axis is always reserved for the dependent variable.)

Fig. 5.3 Two ways to plot the results of a factorial experiment with two independent variables ¶

Figure 5.3 shows results for two hypothetical factorial experiments. The top panel shows the results of a 2 x 2 design. Time of day (day vs. night) is represented by different locations on the x-axis, and cell phone use (no vs. yes) is represented by different-colored bars. It would also be possible to represent cell phone use on the x-axis and time of day as different-colored bars. The choice comes down to which way seems to communicate the results most clearly. The bottom panel of Figure 5.3 shows the results of a 4 x 2 design in which one of the variables is quantitative. This variable, psychotherapy length, is represented along the x-axis, and the other variable (psychotherapy type) is represented by differently formatted lines. This is a line graph rather than a bar graph because the variable on the x-axis is quantitative with a small number of distinct levels. Line graphs are also appropriate when representing measurements made over a time interval (also referred to as time series information) on the x-axis.

5.2.6. Main Effects and Interactions ¶

In factorial designs, there are two kinds of results that are of interest: main effects and interactions. A main effect is the statistical relationship between one independent variable and a dependent variable-averaging across the levels of the other independent variable(s). Thus there is one main effect to consider for each independent variable in the study. The top panel of Figure 5.4 shows a main effect of cell phone use because driving performance was better, on average, when participants were not using cell phones than when they were. The blue bars are, on average, higher than the red bars. It also shows a main effect of time of day because driving performance was better during the day than during the night-both when participants were using cell phones and when they were not. Main effects are independent of each other in the sense that whether or not there is a main effect of one independent variable says nothing about whether or not there is a main effect of the other. The bottom panel of Figure 5.4 , for example, shows a clear main effect of psychotherapy length. The longer the psychotherapy, the better it worked.

Fig. 5.4 Bar graphs showing three types of interactions. In the top panel, one independent variable has an effect at one level of the second independent variable but not at the other. In the middle panel, one independent variable has a stronger effect at one level of the second independent variable than at the other. In the bottom panel, one independent variable has the opposite effect at one level of the second independent variable than at the other. ¶

There is an interaction effect (or just “interaction”) when the effect of one independent variable depends on the level of another. Although this might seem complicated, you already have an intuitive understanding of interactions. It probably would not surprise you, for example, to hear that the effect of receiving psychotherapy is stronger among people who are highly motivated to change than among people who are not motivated to change. This is an interaction because the effect of one independent variable (whether or not one receives psychotherapy) depends on the level of another (motivation to change). Schnall and her colleagues also demonstrated an interaction because the effect of whether the room was clean or messy on participants’ moral judgments depended on whether the participants were low or high in private body consciousness. If they were high in private body consciousness, then those in the messy room made harsher judgments. If they were low in private body consciousness, then whether the room was clean or messy did not matter.

The effect of one independent variable can depend on the level of the other in several different ways. This is shown in Figure 5.5 .

Fig. 5.5 Line Graphs Showing Three Types of Interactions. In the top panel, one independent variable has an effect at one level of the second independent variable but not at the other. In the middle panel, one independent variable has a stronger effect at one level of the second independent variable than at the other. In the bottom panel, one independent variable has the opposite effect at one level of the second independent variable than at the other. ¶

In the top panel, independent variable “B” has an effect at level 1 of independent variable “A” but no effect at level 2 of independent variable “A” (much like the study of Schnall in which there was an effect of disgust for those high in private body consciousness but not for those low in private body consciousness). In the middle panel, independent variable “B” has a stronger effect at level 1 of independent variable “A” than at level 2. This is like the hypothetical driving example where there was a stronger effect of using a cell phone at night than during the day. In the bottom panel, independent variable “B” again has an effect at both levels of independent variable “A”, but the effects are in opposite directions. This is what is called called a crossover interaction. One example of a crossover interaction comes from a study by Kathy Gilliland on the effect of caffeine on the verbal test scores of introverts and extraverts [Gil80] . Introverts perform better than extraverts when they have not ingested any caffeine. But extraverts perform better than introverts when they have ingested 4 mg of caffeine per kilogram of body weight.

In many studies, the primary research question is about an interaction. The study by Brown and her colleagues was inspired by the idea that people with hypochondriasis are especially attentive to any negative health-related information. This led to the hypothesis that people high in hypochondriasis would recall negative health-related words more accurately than people low in hypochondriasis but recall non-health-related words about the same as people low in hypochondriasis. And this is exactly what happened in this study.

5.2.7. Key Takeaways ¶

Researchers often include multiple independent variables in their experiments. The most common approach is the factorial design, in which each level of one independent variable is combined with each level of the others to create all possible conditions.

In a factorial design, the main effect of an independent variable is its overall effect averaged across all other independent variables. There is one main effect for each independent variable.

There is an interaction between two independent variables when the effect of one depends on the level of the other. Some of the most interesting research questions and results in psychology are specifically about interactions.

5.2.8. Exercises ¶

Practice: Return to the five article titles presented at the beginning of this section. For each one, identify the independent variables and the dependent variable.

Practice: Create a factorial design table for an experiment on the effects of room temperature and noise level on performance on the MCAT. Be sure to indicate whether each independent variable will be manipulated between-subjects or within-subjects and explain why.

Practice: Sketch 8 different bar graphs to depict each of the following possible results in a 2 x 2 factorial experiment:

No main effect of A; no main effect of B; no interaction

Main effect of A; no main effect of B; no interaction

No main effect of A; main effect of B; no interaction

Main effect of A; main effect of B; no interaction

Main effect of A; main effect of B; interaction

Main effect of A; no main effect of B; interaction

No main effect of A; main effect of B; interaction

No main effect of A; no main effect of B; interaction

5.3. Factorial designs: Round 2 ¶

Factorial designs require the experimenter to manipulate at least two independent variables. Consider the light-switch example from earlier. Imagine you are trying to figure out which of two light switches turns on a light. The dependent variable is the light (we measure whether it is on or off). The first independent variable is light switch #1, and it has two levels, up or down. The second independent variable is light switch #2, and it also has two levels, up or down. When there are two independent variables, each with two levels, there are four total conditions that can be tested. We can describe these four conditions in a 2x2 table.

This kind of design has a special property that makes it a factorial design. That is, the levels of each independent variable are each manipulated across the levels of the other indpendent variable. In other words, we manipulate whether switch #1 is up or down when switch #2 is up, and when switch numebr #2 is down. Another term for this property of factorial designs is “fully-crossed”.

It is possible to conduct experiments with more than independent variable that are not fully-crossed, or factorial designs. This would mean that each of the levels of one independent variable are not necessarilly manipulated for each of the levels of the other independent variables. These kinds of designs are sometimes called unbalanced designs, and they are not as common as fully-factorial designs. An example, of an unbalanced design would be the following design with only 3 conditions:

Factorial designs are often described using notation such as AXB, where A indicates the number of levels for the first independent variable, and B indicates the number of levels for the second independent variable. The fully-crossed version of the 2-light switch experiment would be called a 2x2 factorial design. This notation is convenient because by multiplying the numbers in the equation we can find the number of conditions in the design. For example 2x2 = 4 conditions.

More complicated factorial designs have more indepdent variables and more levels. We use the same notation describe these designs. Each number represents the number of levels for one of the independent variables, and the number of numbers represents the number of variables. So, a 2x2x2 design has three independent variables, and each one has 2 levels, for a total of 2x2x2=6 conditions. A 3x3 design has two independent variables, each with three levels, for a total of 9 conditions. Designs can get very complicated, such as a 5x3x6x2x7 experiment, with five independent variables, each with differing numbers of levels, for a total of 1260 conditions. If you are considering a complicated design like that one, you might want to consider how to simplify it.

5.3.1. 2x2 Factorial designs ¶

For simplicity, we will focus mainly on 2x2 factorial designs. As with simple designs with only one independent variable, factorial designs have the same basic empirical question. Did manipulation of the independent variables cause changes in the dependent variables? However, 2x2 designs have more than one manipulation, so there is more than one way that the dependent variable can change. So, we end up asking the basic empirical question more than once.

More specifically, the analysis of factorial designs is split into two parts: main effects and interactions. Main effects occur when the manipulation of one independent variable cause a change in the dependent variable. In a 2x2 design, there are two independent variables, so there are two possible main effects: the main effect of independent variable 1, and the main effect of independent variable 2. An interaction occurs when the effect of one independent variable depends on the levels of the other independent variable. My experience in teaching the concept of main effects and interactions is that they are confusing. So, I expect that these definitions will not be very helpful, and although they are clear and precise, they only become helpful as definitions after you understand the concepts…so they are not useful for explaining the concepts. To explain the concepts we will go through several different kinds of examples.

To briefly add to the confusion, or perhaps to illustrate why these two concepts can be confusing, we will look at the eight possible outcomes that could occur in a 2x2 factorial experiment.

In the table, a yes means that there was statistically significant difference for one of the main effects or interaction, and a no means that there was not a statisically significant difference. As you can see, just by adding one more independent variable, the number of possible outcomes quickly become more complicated. When you conduct a 2x2 design, the task for analysis is to determine which of the 8 possibilites occured, and then explain the patterns for each of the effects that occurred. That’s a lot of explaining to do.

5.3.2. Main effects ¶

Main effects occur when the levels of an independent variable cause change in the measurement or dependent variable. There is one possible main effect for each independent variable in the design. When we find that independent variable did influence the dependent variable, then we say there was a main effect. When we find that the independent variable did not influence the dependent variable, then we say there was no main effect.

The simplest way to understand a main effect is to pretend that the other independent variables do not exist. If you do this, then you simply have a single-factor design, and you are asking whether that single factor caused change in the measurement. For a 2x2 experiment, you do this twice, once for each independent variable.

Let’s consider a silly example to illustrate an important property of main effects. In this experiment the dependent variable will be height in inches. The independent variables will be shoes and hats. The shoes independent variable will have two levels: wearing shoes vs. no shoes. The hats independent variable will have two levels: wearing a hat vs. not wearing a hat. The experimenter will provide the shoes and hats. The shoes add 1 inch to a person’s height, and the hats add 6 inches to a person’s height. Further imagine that we conduct a within-subjects design, so we measure each person’s height in each of the fours conditions. Before we look at some example data, the findings from this experiment should be pretty obvious. People will be 1 inch taller when they wear shoes, and 6 inches taller when they where a hat. We see this in the example data from 10 subjects presented below:

The mean heights in each condition are:

To find the main effect of the shoes manipulation we want to find the mean height in the no shoes condition, and compare it to the mean height of the shoes condition. To do this, we collapse , or average over the observations in the hat conditions. For example, looking only at the no shoes vs. shoes conditions we see the following averages for each subject.

The group means are:

As expected, we see that the average height is 1 inch taller when subjects wear shoes vs. do not wear shoes. So, the main effect of wearing shoes is to add 1 inch to a person’s height.

We can do the very same thing to find the main effect of hats. Except in this case, we find the average heights in the no hat vs. hat conditions by averaging over the shoe variable.

As expected, we the average height is 6 inches taller when the subjects wear a hat vs. do not wear a hat. So, the main effect of wearing hats is to add 1 inch to a person’s height.

Instead of using tables to show the data, let’s use some bar graphs. First, we will plot the average heights in all four conditions.

Fig. 5.6 Means from our experiment involving hats and shoes. ¶

Some questions to ask yourself are 1) can you identify the main effect of wearing shoes in the figure, and 2) can you identify the main effet of wearing hats in the figure. Both of these main effects can be seen in the figure, but they aren’t fully clear. You have to do some visual averaging.

Perhaps the most clear is the main effect of wearing a hat. The red bars show the conditions where people wear hats, and the green bars show the conditions where people do not wear hats. For both levels of the wearing shoes variable, the red bars are higher than the green bars. That is easy enough to see. More specifically, in both cases, wearing a hat adds exactly 6 inches to the height, no more no less.

Less clear is the main effect of wearing shoes. This is less clear because the effect is smaller so it is harder to see. How to find it? You can look at the red bars first and see that the red bar for no-shoes is slightly smaller than the red bar for shoes. The same is true for the green bars. The green bar for no-shoes is slightly smaller than the green bar for shoes.

Fig. 5.7 Means of our Hat and No-Hat conditions (averaging over the shoe condition). ¶

Fig. 5.8 Means of our Shoe and No-Shoe conditions (averaging over the hat condition). ¶

Data from 2x2 designs is often present in graphs like the one above. An advantage of these graphs is that they display means in all four conditions of the design. However, they do not clearly show the two main effects. Someone looking at this graph alone would have to guesstimate the main effects. Or, in addition to the main effects, a researcher could present two more graphs, one for each main effect (however, in practice this is not commonly done because it takes up space in a journal article, and with practice it becomes second nature to “see” the presence or absence of main effects in graphs showing all of the conditions). If we made a separate graph for the main effect of shoes we should see a difference of 1 inch between conditions. Similarly, if we made a separate graph for the main effect of hats then we should see a difference of 6 between conditions. Examples of both of those graphs appear in the margin.

Why have we been talking about shoes and hats? These independent variables are good examples of variables that are truly independent from one another. Neither one influences the other. For example, shoes with a 1 inch sole will always add 1 inch to a person’s height. This will be true no matter whether they wear a hat or not, and no matter how tall the hat is. In other words, the effect of wearing a shoe does not depend on wearing a hat. More formally, this means that the shoe and hat independent variables do not interact. It would be very strange if they did interact. It would mean that the effect of wearing a shoe on height would depend on wearing a hat. This does not happen in our universe. But in some other imaginary universe, it could mean, for example, that wearing a shoe adds 1 to your height when you do not wear a hat, but adds more than 1 inch (or less than 1 inch) when you do wear a hat. This thought experiment will be our entry point into discussing interactions. A take-home message before we begin is that some independent variables (like shoes and hats) do not interact; however, there are many other independent variables that do.

5.3.3. Interactions ¶

Interactions occur when the effect of an independent variable depends on the levels of the other independent variable. As we discussed above, some independent variables are independent from one another and will not produce interactions. However, other combinations of independent variables are not independent from one another and they produce interactions. Remember, independent variables are always manipulated independently from the measured variable (see margin note), but they are not necessarilly independent from each other.

Independence

These ideas can be confusing if you think that the word “independent” refers to the relationship between independent variables. However, the term “independent variable” refers to the relationship between the manipulated variable and the measured variable. Remember, “independent variables” are manipulated independently from the measured variable. Specifically, the levels of any independent variable do not change because we take measurements. Instead, the experimenter changes the levels of the independent variable and then observes possible changes in the measures.

There are many simple examples of two independent variables being dependent on one another to produce an outcome. Consider driving a car. The dependent variable (outcome that is measured) could be how far the car can drive in 1 minute. Independent variable 1 could be gas (has gas vs. no gas). Independent variable 2 could be keys (has keys vs. no keys). This is a 2x2 design, with four conditions.

Importantly, the effect of the gas variable on driving depends on the levels of having a key. Or, to state it in reverse, the effect of the key variable on driving depends on the levesl of the gas variable. Finally, in plain english. You need the keys and gas to drive. Otherwise, there is no driving.

5.3.4. What makes a people hangry? ¶

To continue with more examples, let’s consider an imaginary experiment examining what makes people hangry. You may have been hangry before. It’s when you become highly irritated and angry because you are very hungry…hangry. I will propose an experiment to measure conditions that are required to produce hangriness. The pretend experiment will measure hangriness (we ask people how hangry they are on a scale from 1-10, with 10 being most hangry, and 0 being not hangry at all). The first independent variable will be time since last meal (1 hour vs. 5 hours), and the second independent variable will be how tired someone is (not tired vs very tired). I imagine the data could look something the following bar graph.

Fig. 5.9 Means from our study of hangriness. ¶

The graph shows clear evidence of two main effects, and an interaction . There is a main effect of time since last meal. Both the bars in the 1 hour conditions have smaller hanger ratings than both of the bars in the 5 hour conditions. There is a main effect of being tired. Both of the bars in the “not tired” conditions are smaller than than both of the bars in the “tired” conditions. What about the interaction?

Remember, an interaction occurs when the effect of one independent variable depends on the level of the other independent variable. We can look at this two ways, and either way shows the presence of the very same interaction. First, does the effect of being tired depend on the levels of the time since last meal? Yes. Look first at the effect of being tired only for the “1 hour condition”. We see the red bar (tired) is 1 unit lower than the green bar (not tired). So, there is an effect of 1 unit of being tired in the 1 hour condition. Next, look at the effect of being tired only for the “5 hour” condition. We see the red bar (tired) is 3 units lower than the green bar (not tired). So, there is an effect of 3 units for being tired in the 5 hour condition. Clearly, the size of the effect for being tired depends on the levels of the time since last meal variable. We call this an interaction.

The second way of looking at the interaction is to start by looking at the other variable. For example, does the effect of time since last meal depend on the levels of the tired variable? The answer again is yes. Look first at the effect of time since last meal only for the red bars in the “not tired” condition. The red bar in the 1 hour condition is 1 unit smaller than the red bar in the 5 hour condition. Next, look at the effect of time since last meal only for the green bars in the “tired” condition. The green bar in the 1 hour condition is 3 units smaller than the green bar in the 5 hour condition. Again, the size of the effect of time since last meal depends on the levels of the tired variable.No matter which way you look at the interaction, we get the same numbers for the size of the interaction effect, which is 2 units (i.e., the difference between 3 and 1). The interaction suggests that something special happens when people are tired and haven’t eaten in 5 hours. In this condition, they can become very hangry. Whereas, in the other conditions, there are only small increases in being hangry.

5.3.5. Identifying main effects and interactions ¶

Research findings are often presented to readers using graphs or tables. For example, the very same pattern of data can be displayed in a bar graph, line graph, or table of means. These different formats can make the data look different, even though the pattern in the data is the same. An important skill to develop is the ability to identify the patterns in the data, regardless of the format they are presented in. Some examples of bar and line graphs are presented in the margin, and two example tables are presented below. Each format displays the same pattern of data.

Fig. 5.10 Data from a 2x2 factorial design summarized in a bar plot. ¶

Fig. 5.11 The same data from above, but instead summarized in a line plot. ¶

After you become comfortable with interpreting data in these different formats, you should be able to quickly identify the pattern of main effects and interactions. For example, you would be able to notice that all of these graphs and tables show evidence for two main effects and one interaction.

As an exercise toward this goal, we will first take a closer look at extracting main effects and interactions from tables. This exercise will how the condition means are used to calculate the main effects and interactions. Consider the table of condition means below.

5.3.6. Main effects ¶

Main effects are the differences between the means of single independent variable. Notice, this table only shows the condition means for each level of all independent variables. So, the means for each IV must be calculated. The main effect for IV1 is the comparison between level A and level B, which involves calculating the two column means. The mean for IV1 Level A is (4+3)/2 = 3.5. The mean for IV1 Level B is (5+8)/2 = 6.5. So the main effect is 3 (6.5 - 3.5). The main effect for IV2 is the comparison between level 1 and level 2, which involves calculating the two row means. The mean for IV2 Level 1 is (4+5)/2 = 4.5. The mean for IV2 Level 2 is (3+8)/2 = 5.5. So the main effect is 1 (5.5 - 4.5). The process of computing the average for each level of a single independent variable, always involves collapsing, or averaging over, all of the other conditions from other variables that also occured in that condition

5.3.7. Interactions ¶

Interactions ask whether the effect of one independent variable depends on the levels of the other independent variables. This question is answered by computing difference scores between the condition means. For example, we look the effect of IV1 (A vs. B) for both levels of of IV2. Focus first on the condition means in the first row for IV2 level 1. We see that A=4 and B=5, so the effect IV1 here was 5-4 = 1. Next, look at the condition in the second row for IV2 level 2. We see that A=3 and B=8, so the effect of IV1 here was 8-3 = 5. We have just calculated two differences (5-4=1, and 8-3=5). These difference scores show that the size of the IV1 effect was different across the levels of IV2. To calculate the interaction effect we simply find the difference between the difference scores, 5-1=4. In general, if the difference between the difference scores is different, then there is an interaction effect.

5.3.8. Example bar graphs ¶

Fig. 5.12 Four patterns that could be observed in a 2x2 factorial design. ¶

The IV1 shows a main effect only for IV1 (both red and green bars are lower for level 1 than level 2). The IV1&IV2 graphs shows main effects for both variables. The two bars on the left are both lower than the two on the right, and the red bars are both lower than the green bars. The IV1xIV2 graph shows an example of a classic cross-over interaction. Here, there are no main effects, just an interaction. There is a difference of 2 between the green and red bar for Level 1 of IV1, and a difference of -2 for Level 2 of IV1. That makes the differences between the differences = 4. Why are their no main effects? Well the average of the red bars would equal the average of the green bars, so there is no main effect for IV2. And, the average of the red and green bars for level 1 of IV1 would equal the average of the red and green bars for level 2 of IV1, so there is no main effect. The bar graph for IV2 shows only a main effect for IV2, as the red bars are both lower than the green bars.

5.3.9. Example line graphs ¶

You may find that the patterns of main effects and interaction looks different depending on the visual format of the graph. The exact same patterns of data plotted up in bar graph format, are plotted as line graphs for your viewing pleasure. Note that for the IV1 graph, the red line does not appear because it is hidden behind the green line (the points for both numbers are identical).

Fig. 5.13 Four patterns that could be observed in a 2x2 factorial design, now depicted using line plots. ¶

5.3.10. Interpreting main effects and interactions ¶

The presence of an interaction, particularly a strong interaction, can sometimes make it challenging to interpet main effects. For example, take a look at Figure 5.14 , which indicates a very strong interaction.

Fig. 5.14 A clear interaction effect. But what about the main effects? ¶

In Figure 5.14 , IV2 has no effect under level 1 of IV1 (e.g., the red and green bars are the same). IV2 has a large effect under level 2 of IV2 (the red bar is 2 and the green bar is 9). So, the interaction effect is a total of 7. Are there any main effects? Yes there are. Consider the main effect for IV1. The mean for level 1 is (2+2)/2 = 2, and the mean for level 2 is (2+9)/2 = 5.5. There is a difference between the means of 3.5, which is consistent with a main effect. Consider, the main effect for IV2. The mean for level 1 is again (2+2)/2 = 2, and the mean for level 2 is again (2+9)/2 = 5.5. Again, there is a difference between the means of 3.5, which is consistent with a main effect. However, it may seem somewhat misleading to say that our manipulation of IV1 influenced the DV. Why? Well, it only seemed to have have this influence half the time. The same is true for our manipulation of IV2. For this reason, we often say that the presence of interactions qualifies our main effects. In other words, there are two main effects here, but they must be interpreting knowing that we also have an interaction.

The example in Figure 5.15 shows a case in which it is probably a bit more straightforward to interpret both the main effects and the interaction.

Fig. 5.15 Perhaps the main effects are more straightforward to interpret in this example. ¶

Can you spot the interaction right away? The difference between red and green bars is small for level 1 of IV1, but large for level 2. The differences between the differences are different, so there is an interaction. But, we also see clear evidence of two main effects. For example, both the red and green bars for IV1 level 1 are higher than IV1 Level 2. And, both of the red bars (IV2 level 1) are higher than the green bars (IV2 level 2).

5.4. Complex Correlational Designs ¶

5.5. learning objectives ¶.

Explain why researchers use complex correlational designs.

Create and interpret a correlation matrix.

Describe how researchers can use correlational research to explore causal relationships among variables—including the limits of this approach.

As we have already seen, researchers conduct correlational studies rather than experiments when they are interested in noncausal relationships or when they are interested variables that cannot be manipulated for practical or ethical reasons. In this section, we look at some approaches to complex correlational research that involve measuring several variables and assessing the relationships among them.

5.5.1. Correlational Studies With Factorial Designs ¶

We have already seen that factorial experiments can include manipulated independent variables or a combination of manipulated and non-manipulated independent variables. But factorial designs can also consist exclusively of non-manipulated independent variables, in which case they are no longer experiments but correlational studies. Consider a hypothetical study in which a researcher measures two variables. First, the researcher measures participants’ mood and self-esteem. The research then also measure participants’ willingness to have unprotected sexual intercourse. This study can be conceptualized as a 2 x 2 factorial design with mood (positive vs. negative) and self-esteem (high vs. low) as between-subjects factors. Willingness to have unprotected sex is the dependent variable. This design can be represented in a factorial design table and the results in a bar graph of the sort we have already seen. The researcher would consider the main effect of sex, the main effect of self-esteem, and the interaction between these two independent variables.

Again, because neither independent variable in this example was manipulated, it is a correlational study rather than an experiment (the study by MacDonald and Martineau [MM02] was similar, but was an experiment because they manipulated their participants’ moods). This is important because, as always, one must be cautious about inferring causality from correlational studies because of the directionality and third-variable problems. For example, a main effect of participants’ moods on their willingness to have unprotected sex might be caused by any other variable that happens to be correlated with their moods.

5.5.2. Assessing Relationships Among Multiple Variables ¶

Most complex correlational research, however, does not fit neatly into a factorial design. Instead, it involves measuring several variables, often both categorical and quantitative, and then assessing the statistical relationships among them. For example, researchers Nathan Radcliffe and William Klein studied a sample of middle-aged adults to see how their level of optimism (measured by using a short questionnaire called the Life Orientation Test) was related to several other heart-health-related variables [RK02] . These included health, knowledge of heart attack risk factors, and beliefs about their own risk of having a heart attack. They found that more optimistic participants were healthier (e.g., they exercised more and had lower blood pressure), knew about heart attack risk factors, and correctly believed their own risk to be lower than that of their peers.

This approach is often used to assess the validity of new psychological measures. For example, when John Cacioppo and Richard Petty created their Need for Cognition Scale, a measure of the extent to which people like to think and value thinking, they used it to measure the need for cognition for a large sample of college students along with three other variables: intelligence, socially desirable responding (the tendency to give what one thinks is the “appropriate” response), and dogmatism [CP82] . The results of this study are summarized in Figure 5.16 , which is a correlation matrix showing the correlation (Pearson’s \(r\) ) between every possible pair of variables in the study.

Fig. 5.16 Correlation matrix showing correlations among need for cognition and three other variables based on research by Cacioppo and Petty (1982). Only half the matrix is filled in because the other half would contain exactly the same information. Also, because the correlation between a variable and itself is always \(r=1.0\) , these values are replaced with dashes throughout the matrix. ¶

For example, the correlation between the need for cognition and intelligence was \(r=.39\) , the correlation between intelligence and socially desirable responding was \(r=.02\) , and so on. In this case, the overall pattern of correlations was consistent with the researchers’ ideas about how scores on the need for cognition should be related to these other constructs.

When researchers study relationships among a large number of conceptually similar variables, they often use a complex statistical technique called factor analysis. In essence, factor analysis organizes the variables into a smaller number of clusters, such that they are strongly correlated within each cluster but weakly correlated between clusters. Each cluster is then interpreted as multiple measures of the same underlying construct. These underlying constructs are also called “factors.” For example, when people perform a wide variety of mental tasks, factor analysis typically organizes them into two main factors—one that researchers interpret as mathematical intelligence (arithmetic, quantitative estimation, spatial reasoning, and so on) and another that they interpret as verbal intelligence (grammar, reading comprehension, vocabulary, and so on). The Big Five personality factors have been identified through factor analyses of people’s scores on a large number of more specific traits. For example, measures of warmth, gregariousness, activity level, and positive emotions tend to be highly correlated with each other and are interpreted as representing the construct of extraversion. As a final example, researchers Peter Rentfrow and Samuel Gosling asked more than 1,700 university students to rate how much they liked 14 different popular genres of music [RG03] . They then submitted these 14 variables to a factor analysis, which identified four distinct factors. The researchers called them Reflective and Complex (blues, jazz, classical, and folk), Intense and Rebellious (rock, alternative, and heavy metal), Upbeat and Conventional (country, soundtrack, religious, pop), and Energetic and Rhythmic (rap/hip-hop, soul/funk, and electronica).

Two additional points about factor analysis are worth making here. One is that factors are not categories. Factor analysis does not tell us that people are either extraverted or conscientious or that they like either “reflective and complex” music or “intense and rebellious” music. Instead, factors are constructs that operate independently of each other. So people who are high in extraversion might be high or low in conscientiousness, and people who like reflective and complex music might or might not also like intense and rebellious music. The second point is that factor analysis reveals only the underlying structure of the variables. It is up to researchers to interpret and label the factors and to explain the origin of that particular factor structure. For example, one reason that extraversion and the other Big Five operate as separate factors is that they appear to be controlled by different genes [PDMM08] .

5.5.3. Exploring Causal Relationships ¶

NO NO NO NO NO NO NO NO NO

IGNORE, SECTION UNDER CONSTRUCTION (or destruction)

Another important use of complex correlational research is to explore possible causal relationships among variables. This might seem surprising given that “correlation does not imply causation”. It is true that correlational research cannot unambiguously establish that one variable causes another. Complex correlational research, however, can often be used to rule out other plausible interpretations.

The primary way of doing this is through the statistical control of potential third variables. Instead of controlling these variables by random assignment or by holding them constant as in an experiment, the researcher measures them and includes them in the statistical analysis. Consider some research by Paul Piff and his colleagues, who hypothesized that being lower in socioeconomic status (SES) causes people to be more generous [PKCote+10] . They measured their participants’ SES and had them play the “dictator game.” They told participants that each would be paired with another participant in a different room. (In reality, there was no other participant.) Then they gave each participant 10 points (which could later be converted to money) to split with the “partner” in whatever way he or she decided. Because the participants were the “dictators,” they could even keep all 10 points for themselves if they wanted to.

As these researchers expected, participants who were lower in SES tended to give away more of their points than participants who were higher in SES. This is consistent with the idea that being lower in SES causes people to be more generous. But there are also plausible third variables that could explain this relationship. It could be, for example, that people who are lower in SES tend to be more religious and that it is their greater religiosity that causes them to be more generous. Or it could be that people who are lower in SES tend to come from certain ethnic groups that emphasize generosity more than other ethnic groups. The researchers dealt with these potential third variables, however, by measuring them and including them in their statistical analyses. They found that neither religiosity nor ethnicity was correlated with generosity and were therefore able to rule them out as third variables. This does not prove that SES causes greater generosity because there could still be other third variables that the researchers did not measure. But by ruling out some of the most plausible third variables, the researchers made a stronger case for SES as the cause of the greater generosity.

Many studies of this type use a statistical technique called multiple regression. This involves measuring several independent variables (X1, X2, X3,…Xi), all of which are possible causes of a single dependent variable (Y). The result of a multiple regression analysis is an equation that expresses the dependent variable as an additive combination of the independent variables. This regression equation has the following general form:

\(b1X1+ b2X2+ b3X3+ ... + biXi = Y\)

The quantities b1, b2, and so on are regression weights that indicate how large a contribution an independent variable makes, on average, to the dependent variable. Specifically, they indicate how much the dependent variable changes for each one-unit change in the independent variable.

The advantage of multiple regression is that it can show whether an independent variable makes a contribution to a dependent variable over and above the contributions made by other independent variables. As a hypothetical example, imagine that a researcher wants to know how the independent variables of income and health relate to the dependent variable of happiness. This is tricky because income and health are themselves related to each other. Thus if people with greater incomes tend to be happier, then perhaps this is only because they tend to be healthier. Likewise, if people who are healthier tend to be happier, perhaps this is only because they tend to make more money. But a multiple regression analysis including both income and happiness as independent variables would show whether each one makes a contribution to happiness when the other is taken into account. Research like this, by the way, has shown both income and health make extremely small contributions to happiness except in the case of severe poverty or illness [Die00] .

The examples discussed in this section only scratch the surface of how researchers use complex correlational research to explore possible causal relationships among variables. It is important to keep in mind, however, that purely correlational approaches cannot unambiguously establish that one variable causes another. The best they can do is show patterns of relationships that are consistent with some causal interpretations and inconsistent with others.

5.5.4. Key Takeaways ¶

Researchers often use complex correlational research to explore relationships among several variables in the same study.

Complex correlational research can be used to explore possible causal relationships among variables using techniques such as multiple regression. Such designs can show patterns of relationships that are consistent with some causal interpretations and inconsistent with others, but they cannot unambiguously establish that one variable causes another.

5.5.5. Exercises ¶

Practice: Construct a correlation matrix for a hypothetical study including the variables of depression, anxiety, self-esteem, and happiness. Include the Pearson’s r values that you would expect.

Discussion: Imagine a correlational study that looks at intelligence, the need for cognition, and high school students’ performance in a critical-thinking course. A multiple regression analysis shows that intelligence is not related to performance in the class but that the need for cognition is. Explain what this study has shown in terms of what causes good performance in the critical- thinking course.

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Chapter 3. Psychological Science

3.2 Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behaviour

Learning objectives.

• Differentiate the goals of descriptive, correlational, and experimental research designs and explain the advantages and disadvantages of each.
• Explain the goals of descriptive research and the statistical techniques used to interpret it.
• Summarize the uses of correlational research and describe why correlational research cannot be used to infer causality.
• Review the procedures of experimental research and explain how it can be used to draw causal inferences.

Psychologists agree that if their ideas and theories about human behaviour are to be taken seriously, they must be backed up by data. However, the research of different psychologists is designed with different goals in mind, and the different goals require different approaches. These varying approaches, summarized in Table 3.2, are known as research designs . A research design  is the specific method a researcher uses to collect, analyze, and interpret data . Psychologists use three major types of research designs in their research, and each provides an essential avenue for scientific investigation. Descriptive research  is research designed to provide a snapshot of the current state of affairs . Correlational research  is research designed to discover relationships among variables and to allow the prediction of future events from present knowledge . Experimental research  is research in which initial equivalence among research participants in more than one group is created, followed by a manipulation of a given experience for these groups and a measurement of the influence of the manipulation . Each of the three research designs varies according to its strengths and limitations, and it is important to understand how each differs.

Descriptive Research: Assessing the Current State of Affairs

Descriptive research is designed to create a snapshot of the current thoughts, feelings, or behaviour of individuals. This section reviews three types of descriptive research : case studies , surveys , and naturalistic observation (Figure 3.4).

Sometimes the data in a descriptive research project are based on only a small set of individuals, often only one person or a single small group. These research designs are known as case studies — descriptive records of one or more individual’s experiences and behaviour . Sometimes case studies involve ordinary individuals, as when developmental psychologist Jean Piaget used his observation of his own children to develop his stage theory of cognitive development. More frequently, case studies are conducted on individuals who have unusual or abnormal experiences or characteristics or who find themselves in particularly difficult or stressful situations. The assumption is that by carefully studying individuals who are socially marginal, who are experiencing unusual situations, or who are going through a difficult phase in their lives, we can learn something about human nature.

Sigmund Freud was a master of using the psychological difficulties of individuals to draw conclusions about basic psychological processes. Freud wrote case studies of some of his most interesting patients and used these careful examinations to develop his important theories of personality. One classic example is Freud’s description of “Little Hans,” a child whose fear of horses the psychoanalyst interpreted in terms of repressed sexual impulses and the Oedipus complex (Freud, 1909/1964).

Another well-known case study is Phineas Gage, a man whose thoughts and emotions were extensively studied by cognitive psychologists after a railroad spike was blasted through his skull in an accident. Although there are questions about the interpretation of this case study (Kotowicz, 2007), it did provide early evidence that the brain’s frontal lobe is involved in emotion and morality (Damasio et al., 2005). An interesting example of a case study in clinical psychology is described by Rokeach (1964), who investigated in detail the beliefs of and interactions among three patients with schizophrenia, all of whom were convinced they were Jesus Christ.

In other cases the data from descriptive research projects come in the form of a survey — a measure administered through either an interview or a written questionnaire to get a picture of the beliefs or behaviours of a sample of people of interest . The people chosen to participate in the research (known as the sample) are selected to be representative of all the people that the researcher wishes to know about (the population). In election polls, for instance, a sample is taken from the population of all “likely voters” in the upcoming elections.

The results of surveys may sometimes be rather mundane, such as “Nine out of 10 doctors prefer Tymenocin” or “The median income in the city of Hamilton is $46,712.” Yet other times (particularly in discussions of social behaviour), the results can be shocking: “More than 40,000 people are killed by gunfire in the United States every year” or “More than 60% of women between the ages of 50 and 60 suffer from depression.” Descriptive research is frequently used by psychologists to get an estimate of the prevalence (or incidence ) of psychological disorders.

A final type of descriptive research — known as naturalistic observation — is research based on the observation of everyday events . For instance, a developmental psychologist who watches children on a playground and describes what they say to each other while they play is conducting descriptive research, as is a biopsychologist who observes animals in their natural habitats. One example of observational research involves a systematic procedure known as the strange situation , used to get a picture of how adults and young children interact. The data that are collected in the strange situation are systematically coded in a coding sheet such as that shown in Table 3.3.

The results of descriptive research projects are analyzed using descriptive statistics — numbers that summarize the distribution of scores on a measured variable . Most variables have distributions similar to that shown in Figure 3.5 where most of the scores are located near the centre of the distribution, and the distribution is symmetrical and bell-shaped. A data distribution that is shaped like a bell is known as a normal distribution .

A distribution can be described in terms of its central tendency — that is, the point in the distribution around which the data are centred — and its dispersion, or spread . The arithmetic average, or arithmetic mean , symbolized by the letter M , is the most commonly used measure of central tendency . It is computed by calculating the sum of all the scores of the variable and dividing this sum by the number of participants in the distribution (denoted by the letter N ). In the data presented in Figure 3.5 the mean height of the students is 67.12 inches (170.5 cm). The sample mean is usually indicated by the letter M .

In some cases, however, the data distribution is not symmetrical. This occurs when there are one or more extreme scores (known as outliers ) at one end of the distribution. Consider, for instance, the variable of family income (see Figure 3.6), which includes an outlier (a value of $3,800,000). In this case the mean is not a good measure of central tendency. Although it appears from Figure 3.6 that the central tendency of the family income variable should be around $70,000, the mean family income is actually $223,960. The single very extreme income has a disproportionate impact on the mean, resulting in a value that does not well represent the central tendency.

The median is used as an alternative measure of central tendency when distributions are not symmetrical. The median  is the score in the center of the distribution, meaning that 50% of the scores are greater than the median and 50% of the scores are less than the median . In our case, the median household income ($73,000) is a much better indication of central tendency than is the mean household income ($223,960).

A final measure of central tendency, known as the mode , represents the value that occurs most frequently in the distribution . You can see from Figure 3.6 that the mode for the family income variable is $93,000 (it occurs four times).

In addition to summarizing the central tendency of a distribution, descriptive statistics convey information about how the scores of the variable are spread around the central tendency. Dispersion refers to the extent to which the scores are all tightly clustered around the central tendency , as seen in Figure 3.7.

Or they may be more spread out away from it, as seen in Figure 3.8.

One simple measure of dispersion is to find the largest (the maximum ) and the smallest (the minimum ) observed values of the variable and to compute the range of the variable as the maximum observed score minus the minimum observed score. You can check that the range of the height variable in Figure 3.5 is 72 – 62 = 10. The standard deviation , symbolized as s , is the most commonly used measure of dispersion . Distributions with a larger standard deviation have more spread. The standard deviation of the height variable is s = 2.74, and the standard deviation of the family income variable is s = $745,337.

An advantage of descriptive research is that it attempts to capture the complexity of everyday behaviour. Case studies provide detailed information about a single person or a small group of people, surveys capture the thoughts or reported behaviours of a large population of people, and naturalistic observation objectively records the behaviour of people or animals as it occurs naturally. Thus descriptive research is used to provide a relatively complete understanding of what is currently happening.

Despite these advantages, descriptive research has a distinct disadvantage in that, although it allows us to get an idea of what is currently happening, it is usually limited to static pictures. Although descriptions of particular experiences may be interesting, they are not always transferable to other individuals in other situations, nor do they tell us exactly why specific behaviours or events occurred. For instance, descriptions of individuals who have suffered a stressful event, such as a war or an earthquake, can be used to understand the individuals’ reactions to the event but cannot tell us anything about the long-term effects of the stress. And because there is no comparison group that did not experience the stressful situation, we cannot know what these individuals would be like if they hadn’t had the stressful experience.

Correlational Research: Seeking Relationships among Variables

In contrast to descriptive research, which is designed primarily to provide static pictures, correlational research involves the measurement of two or more relevant variables and an assessment of the relationship between or among those variables. For instance, the variables of height and weight are systematically related (correlated) because taller people generally weigh more than shorter people. In the same way, study time and memory errors are also related, because the more time a person is given to study a list of words, the fewer errors he or she will make. When there are two variables in the research design, one of them is called the predictor variable and the other the outcome variable . The research design can be visualized as shown in Figure 3.9, where the curved arrow represents the expected correlation between these two variables.

One way of organizing the data from a correlational study with two variables is to graph the values of each of the measured variables using a scatter plot . As you can see in Figure 3.10 a scatter plot  is a visual image of the relationship between two variables . A point is plotted for each individual at the intersection of his or her scores for the two variables. When the association between the variables on the scatter plot can be easily approximated with a straight line , as in parts (a) and (b) of Figure 3.10 the variables are said to have a linear relationship .

When the straight line indicates that individuals who have above-average values for one variable also tend to have above-average values for the other variable , as in part (a), the relationship is said to be positive linear . Examples of positive linear relationships include those between height and weight, between education and income, and between age and mathematical abilities in children. In each case, people who score higher on one of the variables also tend to score higher on the other variable. Negative linear relationships , in contrast, as shown in part (b), occur when above-average values for one variable tend to be associated with below-average values for the other variable. Examples of negative linear relationships include those between the age of a child and the number of diapers the child uses, and between practice on and errors made on a learning task. In these cases, people who score higher on one of the variables tend to score lower on the other variable.

Relationships between variables that cannot be described with a straight line are known as nonlinear relationships . Part (c) of Figure 3.10 shows a common pattern in which the distribution of the points is essentially random. In this case there is no relationship at all between the two variables, and they are said to be independent . Parts (d) and (e) of Figure 3.10 show patterns of association in which, although there is an association, the points are not well described by a single straight line. For instance, part (d) shows the type of relationship that frequently occurs between anxiety and performance. Increases in anxiety from low to moderate levels are associated with performance increases, whereas increases in anxiety from moderate to high levels are associated with decreases in performance. Relationships that change in direction and thus are not described by a single straight line are called curvilinear relationships .

The most common statistical measure of the strength of linear relationships among variables is the Pearson correlation coefficient , which is symbolized by the letter r . The value of the correlation coefficient ranges from r = –1.00 to r = +1.00. The direction of the linear relationship is indicated by the sign of the correlation coefficient. Positive values of r (such as r = .54 or r = .67) indicate that the relationship is positive linear (i.e., the pattern of the dots on the scatter plot runs from the lower left to the upper right), whereas negative values of r (such as r = –.30 or r = –.72) indicate negative linear relationships (i.e., the dots run from the upper left to the lower right). The strength of the linear relationship is indexed by the distance of the correlation coefficient from zero (its absolute value). For instance, r = –.54 is a stronger relationship than r = .30, and r = .72 is a stronger relationship than r = –.57. Because the Pearson correlation coefficient only measures linear relationships, variables that have curvilinear relationships are not well described by r , and the observed correlation will be close to zero.

It is also possible to study relationships among more than two measures at the same time. A research design in which more than one predictor variable is used to predict a single outcome variable is analyzed through multiple regression (Aiken & West, 1991).  Multiple regression  is a statistical technique, based on correlation coefficients among variables, that allows predicting a single outcome variable from more than one predictor variable . For instance, Figure 3.11 shows a multiple regression analysis in which three predictor variables (Salary, job satisfaction, and years employed) are used to predict a single outcome (job performance). The use of multiple regression analysis shows an important advantage of correlational research designs — they can be used to make predictions about a person’s likely score on an outcome variable (e.g., job performance) based on knowledge of other variables.

An important limitation of correlational research designs is that they cannot be used to draw conclusions about the causal relationships among the measured variables. Consider, for instance, a researcher who has hypothesized that viewing violent behaviour will cause increased aggressive play in children. He has collected, from a sample of Grade 4 children, a measure of how many violent television shows each child views during the week, as well as a measure of how aggressively each child plays on the school playground. From his collected data, the researcher discovers a positive correlation between the two measured variables.

Although this positive correlation appears to support the researcher’s hypothesis, it cannot be taken to indicate that viewing violent television causes aggressive behaviour. Although the researcher is tempted to assume that viewing violent television causes aggressive play, there are other possibilities. One alternative possibility is that the causal direction is exactly opposite from what has been hypothesized. Perhaps children who have behaved aggressively at school develop residual excitement that leads them to want to watch violent television shows at home (Figure 3.13):

Although this possibility may seem less likely, there is no way to rule out the possibility of such reverse causation on the basis of this observed correlation. It is also possible that both causal directions are operating and that the two variables cause each other (Figure 3.14).

Still another possible explanation for the observed correlation is that it has been produced by the presence of a common-causal variable (also known as a third variable ). A common-causal variable  is a variable that is not part of the research hypothesis but that causes both the predictor and the outcome variable and thus produces the observed correlation between them . In our example, a potential common-causal variable is the discipline style of the children’s parents. Parents who use a harsh and punitive discipline style may produce children who like to watch violent television and who also behave aggressively in comparison to children whose parents use less harsh discipline (Figure 3.15)

In this case, television viewing and aggressive play would be positively correlated (as indicated by the curved arrow between them), even though neither one caused the other but they were both caused by the discipline style of the parents (the straight arrows). When the predictor and outcome variables are both caused by a common-causal variable, the observed relationship between them is said to be spurious . A spurious relationship  is a relationship between two variables in which a common-causal variable produces and “explains away” the relationship . If effects of the common-causal variable were taken away, or controlled for, the relationship between the predictor and outcome variables would disappear. In the example, the relationship between aggression and television viewing might be spurious because by controlling for the effect of the parents’ disciplining style, the relationship between television viewing and aggressive behaviour might go away.

Common-causal variables in correlational research designs can be thought of as mystery variables because, as they have not been measured, their presence and identity are usually unknown to the researcher. Since it is not possible to measure every variable that could cause both the predictor and outcome variables, the existence of an unknown common-causal variable is always a possibility. For this reason, we are left with the basic limitation of correlational research: correlation does not demonstrate causation. It is important that when you read about correlational research projects, you keep in mind the possibility of spurious relationships, and be sure to interpret the findings appropriately. Although correlational research is sometimes reported as demonstrating causality without any mention being made of the possibility of reverse causation or common-causal variables, informed consumers of research, like you, are aware of these interpretational problems.

In sum, correlational research designs have both strengths and limitations. One strength is that they can be used when experimental research is not possible because the predictor variables cannot be manipulated. Correlational designs also have the advantage of allowing the researcher to study behaviour as it occurs in everyday life. And we can also use correlational designs to make predictions — for instance, to predict from the scores on their battery of tests the success of job trainees during a training session. But we cannot use such correlational information to determine whether the training caused better job performance. For that, researchers rely on experiments.

Experimental Research: Understanding the Causes of Behaviour

The goal of experimental research design is to provide more definitive conclusions about the causal relationships among the variables in the research hypothesis than is available from correlational designs. In an experimental research design, the variables of interest are called the independent variable (or variables ) and the dependent variable . The independent variable  in an experiment is the causing variable that is created (manipulated) by the experimenter . The dependent variable  in an experiment is a measured variable that is expected to be influenced by the experimental manipulation . The research hypothesis suggests that the manipulated independent variable or variables will cause changes in the measured dependent variables. We can diagram the research hypothesis by using an arrow that points in one direction. This demonstrates the expected direction of causality (Figure 3.16):

Research Focus: Video Games and Aggression

Consider an experiment conducted by Anderson and Dill (2000). The study was designed to test the hypothesis that viewing violent video games would increase aggressive behaviour. In this research, male and female undergraduates from Iowa State University were given a chance to play with either a violent video game (Wolfenstein 3D) or a nonviolent video game (Myst). During the experimental session, the participants played their assigned video games for 15 minutes. Then, after the play, each participant played a competitive game with an opponent in which the participant could deliver blasts of white noise through the earphones of the opponent. The operational definition of the dependent variable (aggressive behaviour) was the level and duration of noise delivered to the opponent. The design of the experiment is shown in Figure 3.17

Two advantages of the experimental research design are (a) the assurance that the independent variable (also known as the experimental manipulation ) occurs prior to the measured dependent variable, and (b) the creation of initial equivalence between the conditions of the experiment (in this case by using random assignment to conditions).

Experimental designs have two very nice features. For one, they guarantee that the independent variable occurs prior to the measurement of the dependent variable. This eliminates the possibility of reverse causation. Second, the influence of common-causal variables is controlled, and thus eliminated, by creating initial equivalence among the participants in each of the experimental conditions before the manipulation occurs.

The most common method of creating equivalence among the experimental conditions is through random assignment to conditions, a procedure in which the condition that each participant is assigned to is determined through a random process, such as drawing numbers out of an envelope or using a random number table . Anderson and Dill first randomly assigned about 100 participants to each of their two groups (Group A and Group B). Because they used random assignment to conditions, they could be confident that, before the experimental manipulation occurred, the students in Group A were, on average, equivalent to the students in Group B on every possible variable, including variables that are likely to be related to aggression, such as parental discipline style, peer relationships, hormone levels, diet — and in fact everything else.

Then, after they had created initial equivalence, Anderson and Dill created the experimental manipulation — they had the participants in Group A play the violent game and the participants in Group B play the nonviolent game. Then they compared the dependent variable (the white noise blasts) between the two groups, finding that the students who had viewed the violent video game gave significantly longer noise blasts than did the students who had played the nonviolent game.

Anderson and Dill had from the outset created initial equivalence between the groups. This initial equivalence allowed them to observe differences in the white noise levels between the two groups after the experimental manipulation, leading to the conclusion that it was the independent variable (and not some other variable) that caused these differences. The idea is that the only thing that was different between the students in the two groups was the video game they had played.

Despite the advantage of determining causation, experiments do have limitations. One is that they are often conducted in laboratory situations rather than in the everyday lives of people. Therefore, we do not know whether results that we find in a laboratory setting will necessarily hold up in everyday life. Second, and more important, is that some of the most interesting and key social variables cannot be experimentally manipulated. If we want to study the influence of the size of a mob on the destructiveness of its behaviour, or to compare the personality characteristics of people who join suicide cults with those of people who do not join such cults, these relationships must be assessed using correlational designs, because it is simply not possible to experimentally manipulate these variables.

Key Takeaways

• Descriptive, correlational, and experimental research designs are used to collect and analyze data.
• Descriptive designs include case studies, surveys, and naturalistic observation. The goal of these designs is to get a picture of the current thoughts, feelings, or behaviours in a given group of people. Descriptive research is summarized using descriptive statistics.
• Correlational research designs measure two or more relevant variables and assess a relationship between or among them. The variables may be presented on a scatter plot to visually show the relationships. The Pearson Correlation Coefficient ( r ) is a measure of the strength of linear relationship between two variables.
• Common-causal variables may cause both the predictor and outcome variable in a correlational design, producing a spurious relationship. The possibility of common-causal variables makes it impossible to draw causal conclusions from correlational research designs.
• Experimental research involves the manipulation of an independent variable and the measurement of a dependent variable. Random assignment to conditions is normally used to create initial equivalence between the groups, allowing researchers to draw causal conclusions.

Exercises and Critical Thinking

• There is a negative correlation between the row that a student sits in in a large class (when the rows are numbered from front to back) and his or her final grade in the class. Do you think this represents a causal relationship or a spurious relationship, and why?
• Think of two variables (other than those mentioned in this book) that are likely to be correlated, but in which the correlation is probably spurious. What is the likely common-causal variable that is producing the relationship?
• Imagine a researcher wants to test the hypothesis that participating in psychotherapy will cause a decrease in reported anxiety. Describe the type of research design the investigator might use to draw this conclusion. What would be the independent and dependent variables in the research?

Image Attributions

Figure 3.4: “ Reading newspaper ” by Alaskan Dude (http://commons.wikimedia.org/wiki/File:Reading_newspaper.jpg) is licensed under CC BY 2.0

Aiken, L., & West, S. (1991).  Multiple regression: Testing and interpreting interactions . Newbury Park, CA: Sage.

Ainsworth, M. S., Blehar, M. C., Waters, E., & Wall, S. (1978).  Patterns of attachment: A psychological study of the strange situation . Hillsdale, NJ: Lawrence Erlbaum Associates.

Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life.  Journal of Personality and Social Psychology, 78 (4), 772–790.

Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., Damasio, A. R., Cacioppo, J. T., & Berntson, G. G. (2005). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. In  Social neuroscience: Key readings.  (pp. 21–28). New York, NY: Psychology Press.

Freud, S. (1909/1964). Analysis of phobia in a five-year-old boy. In E. A. Southwell & M. Merbaum (Eds.),  Personality: Readings in theory and research  (pp. 3–32). Belmont, CA: Wadsworth. (Original work published 1909).

Kotowicz, Z. (2007). The strange case of Phineas Gage.  History of the Human Sciences, 20 (1), 115–131.

Rokeach, M. (1964).  The three Christs of Ypsilanti: A psychological study . New York, NY: Knopf.

Stangor, C. (2011). Research methods for the behavioural sciences (4th ed.). Mountain View, CA: Cengage.

Long Descriptions

Figure 3.6 long description: There are 25 families. 24 families have an income between $44,000 and $111,000 and one family has an income of $3,800,000. The mean income is $223,960 while the median income is $73,000. [Return to Figure 3.6]

Figure 3.10 long description: Types of scatter plots.

• Positive linear, r=positive .82. The plots on the graph form a rough line that runs from lower left to upper right.
• Negative linear, r=negative .70. The plots on the graph form a rough line that runs from upper left to lower right.
• Independent, r=0.00. The plots on the graph are spread out around the centre.
• Curvilinear, r=0.00. The plots of the graph form a rough line that goes up and then down like a hill.
• Curvilinear, r=0.00. The plots on the graph for a rough line that goes down and then up like a ditch.

[Return to Figure 3.10]

Introduction to Psychology - 1st Canadian Edition Copyright © 2014 by Jennifer Walinga and Charles Stangor is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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9 Chapter 9: Simple Experiments

Simple experiments.

What Is an Experiment?

As we saw earlier, an experiment is a type of study designed specifically to answer the question of whether there is a causal relationship between two variables. Do changes in an independent variable cause changes in a dependent variable? Experiments have two fundamental features. The first is that the researchers manipulate, or systematically vary, the level of the independent variable. The different levels of the independent variable are called conditions. For example, in Darley and Latané’s experiment, the independent variable was the number of witnesses that participants believed to be present. The researchers manipulated this independent variable by telling participants that there were either one, two, or five other students involved in the discussion, thereby creating three conditions. The second fundamental feature of an experiment is that the researcher controls, or minimizes the variability in, variables other than the independent and dependent variable. These other variables are called extraneous variables. Darley and Latané tested all their participants in the same room, exposed them to the same emergency situation, and so on. They also randomly assigned their participants to conditions so that the three groups would be similar to each other to begin with. Notice that although the words manipulation and control have similar meanings in everyday language, researchers make a clear distinction between them. They manipulate the independent variable by systematically changing its levels and control other variables by holding them constant.

9.1  Experiment Basics

Internal Validity

Recall that the fact that two variables are statistically related does not necessarily mean that one causes the other. “Correlation does not imply causation.” For example, if it were the case that people who exercise regularly are happier than people who do not exercise regularly, this would not necessarily mean that exercising increases people’s happiness. It could mean instead that greater happiness causes people to exercise (the directionality problem) or that something like better physical health causes people to exercise and be happier (the third-variable problem).

The purpose of an experiment, however, is to show that two variables are statistically related and to do so in a way that supports the conclusion that the independent variable caused any observed differences in the dependent variable. The basic logic is this: If the researcher creates two or more highly similar conditions and then manipulates the independent variable to produce just one difference between them, then any later difference between the conditions must have been caused by the independent variable. For example, because the only difference between Darley and Latané’s conditions was the number of students that participants believed to be involved in the discussion, this must have been responsible for differences in helping between the conditions.

An empirical study is said to be high in internal validity if the way it was conducted supports the conclusion that the independent variable caused any observed differences in the dependent variable. Thus experiments are high in internal validity because the way they are conducted—with the manipulation of the independent variable and the control of extraneous variables—provides strong support for causal conclusions.

External Validity

At the same time, the way that experiments are conducted sometimes leads to a different kind of criticism. Specifically, the need to manipulate the independent variable and control extraneous variables means that experiments are often conducted under conditions that seem artificial or unlike “real life” (Stanovich, 2010). In many psychology experiments, the participants are all college undergraduates and come to a classroom or laboratory to fill out a series of paper-and-pencil questionnaires or to perform a carefully designed computerized task. Consider, for example, an experiment in which researcher Barbara Fredrickson and her colleagues had college students come to a laboratory on campus and complete a math test while wearing a swimsuit (Fredrickson, Roberts, Noll, Quinn, & Twenge, 1998). At first, this might seem silly. When will college students ever have to complete math tests in their swimsuits outside of this experiment?

The issue we are confronting is that of external validity. An empirical study is high in external validity if the way it was conducted supports generalizing the results to people and situations beyond those actually studied. As a general rule, studies are higher in external validity when the participants and the situation studied are similar to those that the researchers want to generalize to. Imagine, for example, that a group of researchers is interested in how shoppers in large grocery stores are affected by whether breakfast cereal is packaged in yellow or purple boxes. Their study would be high in external validity if they studied the decisions of ordinary people doing their weekly shopping in a real grocery store. If the shoppers bought much more cereal in purple boxes, the researchers would be fairly confident that this would be true for other shoppers in other stores. Their study would be relatively low in external validity, however, if they studied a sample of college students in a laboratory at a selective college who merely judged the appeal of various colors presented on a computer screen. If the students judged purple to be more appealing than yellow, the researchers would not be very confident that this is relevant to grocery shoppers’ cereal-buying decisions.

We should be careful, however, not to draw the blanket conclusion that experiments are low in external validity. One reason is that experiments need not seem artificial. Consider that Darley and Latané’s experiment provided a reasonably good simulation of a real emergency situation. Or consider field experiments that are conducted entirely outside the laboratory. In one such experiment, Robert Cialdini and his colleagues studied whether hotel guests choose to reuse their towels for a second day as opposed to having them washed as a way of conserving water and energy (Cialdini, 2005). These researchers manipulated the message on a card left in a large sample of hotel rooms. One version of the message emphasized showing respect for the environment, another emphasized that the hotel would donate a portion of their savings to an environmental cause, and a third emphasized that most hotel guests choose to reuse their towels. The result was that guests who received the message that most hotel guests choose to reuse their towels reused their own towels substantially more often than guests receiving either of the other two messages. Given the way they conducted their study, it seems very likely that their result would hold true for other guests in other hotels.

A second reason not to draw the blanket conclusion that experiments are low in external validity is that they are often conducted to learn about psychological processes that are likely to operate in a variety of people and situations. Let us return to the experiment by Fredrickson and colleagues. They found that the women in their study, but not the men, performed worse on the math test when they were wearing swimsuits. They argued that this was due to women’s greater tendency to objectify themselves—to think about themselves from the perspective of an outside observer—which diverts their attention away from other tasks. They argued, furthermore, that this process of self-objectification and its effect on attention is likely to operate in a variety of women and situations—even if none of them ever finds herself taking a math test in her swimsuit.

Manipulation of the Independent Variable

Again, to manipulate an independent variable means to change its level systematically so that different groups of participants are exposed to different levels of that variable, or the same group of participants is exposed to different levels at different times. For example, to see whether expressive writing affects people’s health, a researcher might instruct some participants to write about traumatic experiences and others to write about neutral experiences. The different levels of the independent variable are referred to as conditions, and researchers often give the conditions short descriptive names to make it easy to talk and write about them. In this case, the conditions might be called the “traumatic condition” and the “neutral condition.”

Notice that the manipulation of an independent variable must involve the active intervention of the researcher. Comparing groups of people who differ on the independent variable before the study begins is not the same as manipulating that variable. For example, a researcher who compares the health of people who already keep a journal with the health of people who do not keep a journal has not manipulated this variable and therefore not conducted an experiment. This is important because groups that already differ in one way at the beginning of a study are likely to differ in other ways too. For example, people who choose to keep journals might also be more conscientious, more introverted, or less stressed than people who do not. Therefore, any observed difference between the two groups in terms of their health might have been caused by whether or not they keep a journal, or it might have been caused by any of the other differences between people who do and do not keep journals. Thus the active manipulation of the independent variable is crucial for eliminating the third-variable problem.

Of course, there are many situations in which the independent variable cannot be manipulated for practical or ethical reasons and therefore an experiment is not possible. For example, whether or not people have a significant early illness experience cannot be manipulated, making it impossible to do an experiment on the effect of early illness experiences on the development of hypochondriasis. This does not mean it is impossible to study the relationship between early illness experiences and hypochondriasis—only that it must be done using non-experimental approaches. We will discuss this in detail later in the book.

In many experiments, the independent variable is a construct that can only be manipulated indirectly. For example, a researcher might try to manipulate participants’ stress levels indirectly by telling some of them that they have five minutes to prepare a short speech that they will then have to give to an audience of other participants. In such situations, researchers often include a manipulation check in their procedure. A manipulation check is a separate measure of the construct the researcher is trying to manipulate. For example, researchers trying to manipulate participants’ stress levels might give them a paper-and-pencil stress questionnaire or take their blood pressure—perhaps right after the manipulation or at the end of the procedure—to verify that they successfully manipulated this variable.

Control of Extraneous Variables

An extraneous variable is anything that varies in the context of a study other than the independent and dependent variables. In an experiment on the effect of expressive writing on health, for example, extraneous variables would include participant variables (individual differences) such as their writing ability, their diet, and their shoe size. They would also include situation or task variables such as the time of day when participants write, whether they write by hand or on a computer, and the weather. Extraneous variables pose a problem because many of them are likely to have some effect on the dependent variable. For example, participants’ health will be affected by many things other than whether or not they engage in expressive writing. This can make it difficult to separate the effect of the independent variable from the effects of the extraneous variables, which is why it is important to control extraneous variables by holding them constant.

One way to control extraneous variables is to hold them constant. This can mean holding situation or task variables constant by testing all participants in the same location, giving them identical instructions, treating them in the same way, and so on. It can also mean holding participant variables constant. For example, many studies of language limit participants to right-handed people, who generally have their language areas isolated in their left cerebral hemispheres. Left-handed people are more likely to have their language areas isolated in their right cerebral hemispheres or distributed across both hemispheres, which can change the way they process language and thereby add noise to the data.

In principle, researchers can control extraneous variables by limiting participants to one very specific category of person, such as 20-year-old, straight, female, right-handed, sophomore psychology majors. The obvious downside to this approach is that it would lower the external validity of the study—in particular, the extent to which the results can be generalized beyond the people actually studied. For example, it might be unclear whether results obtained with a sample of younger straight women would apply to older gay men. In many situations, the advantages of a diverse sample outweigh the reduction in noise achieved by a homogeneous one.

Extraneous Variables as Confounding Variables

The second way that extraneous variables can make it difficult to detect the effect of the independent variable is by becoming confounding variables. A confounding variable is an extraneous variable that differs on average across levels of the independent variable. For example, in almost all experiments, participants’ intelligence quotients (IQs) will be an extraneous variable. But as long as there are participants with lower and higher IQs at each level of the independent variable so that the average IQ is roughly equal, then this variation is probably acceptable (and may even be desirable). What would be bad, however, would be for participants at one level of the independent variable to have substantially lower IQs on average and participants at another level to have substantially higher IQs on average. In this case, IQ would be a confounding variable.

To confound means to confuse, and this is exactly what confounding variables do. Because they differ across conditions—just like the independent variable—they provide an alternative explanation for any observed difference in the dependent variable. Consider the results of a hypothetical study in which participants in a positive mood condition scored higher on a memory task than participants in a negative mood condition. If IQ is a confounding variable—with participants in the positive mood condition having higher IQs on average than participants in the negative mood condition—then it is unclear whether it was the positive moods or the higher IQs that caused participants in the first condition to score higher. One way to avoid confounding variables is by holding extraneous variables constant. For example, one could prevent IQ from becoming a confounding variable by limiting participants only to those with IQs of exactly 100. But this approach is not always desirable for reasons we have already discussed. A second and much more general approach—random assignment to conditions—will be discussed in detail shortly.

Key Takeaways

·         An experiment is a type of empirical study that features the manipulation of an independent variable, the measurement of a dependent variable, and control of extraneous variables.

·         Studies are high in internal validity to the extent that the way they are conducted supports the conclusion that the independent variable caused any observed differences in the dependent variable. Experiments are generally high in internal validity because of the manipulation of the independent variable and control of extraneous variables.

·         Studies are high in external validity to the extent that the result can be generalized to people and situations beyond those actually studied. Although experiments can seem “artificial”—and low in external validity—it is important to consider whether the psychological processes under study are likely to operate in other people and situations.

9.2  Experimental Design

In this section, we look at some different ways to design an experiment. The primary distinction we will make is between approaches in which each participant experiences one level of the independent variable and approaches in which each participant experiences all levels of the independent variable. The former are called between-subjects experiments and the latter are called within-subjects experiments.

Between-Subjects Experiments

In a between-subjects experiment, each participant is tested in only one condition. For example, a researcher with a sample of 100 college students might assign half of them to write about a traumatic event and the other half write about a neutral event. Or a researcher with a sample of 60 people with severe agoraphobia (fear of open spaces) might assign 20 of them to receive each of three different treatments for that disorder. It is essential in a between-subjects experiment that the researcher assign participants to conditions so that the different groups are, on average, highly similar to each other. Those in a trauma condition and a neutral condition, for example, should include a similar proportion of men and women, and they should have similar average intelligence quotients (IQs), similar average levels of motivation, similar average numbers of health problems, and so on. This is a matter of controlling these extraneous participant variables across conditions so that they do not become confounding variables.

Random Assignment

The primary way that researchers accomplish this kind of control of extraneous variables across conditions is called random assignment, which means using a random process to decide which participants are tested in which conditions. Do not confuse random assignment with random sampling. Random sampling is a method for selecting a sample from a population, and it is rarely used in psychological research. Random assignment is a method for assigning participants in a sample to the different conditions, and it is an important element of all experimental research in psychology and other fields too.

In its strictest sense, random assignment should meet two criteria. One is that each participant has an equal chance of being assigned to each condition (e.g., a 50% chance of being assigned to each of two conditions). The second is that each participant is assigned to a condition independently of other participants. Thus one way to assign participants to two conditions would be to flip a coin for each one. If the coin lands heads, the participant is assigned to Condition A, and if it lands tails, the participant is assigned to Condition B. For three conditions, one could use a computer to generate a random integer from 1 to 3 for each participant. If the integer is 1, the participant is assigned to Condition A; if it is 2, the participant is assigned to Condition B; and if it is 3, the participant is assigned to Condition C. In practice, a full sequence of conditions—one for each participant expected to be in the experiment—is usually created ahead of time, and each new participant is assigned to the next condition in the sequence as he or she is tested. When the procedure is computerized, the computer program often handles the random assignment.

One problem with coin flipping and other strict procedures for random assignment is that they are likely to result in unequal sample sizes in the different conditions. Unequal sample sizes are generally not a serious problem, and you should never throw away data you have already collected to achieve equal sample sizes. However, for a fixed number of participants, it is statistically most efficient to divide them into equal-sized groups. It is standard practice, therefore, to use a kind of modified random assignment that keeps the number of participants in each group as similar as possible. One approach is block randomization. In block randomization, all the conditions occur once in the sequence before any of them is repeated. Then they all occur again before any of them is repeated again. Within each of these “blocks,” the conditions occur in a random order. Again, the sequence of conditions is usually generated before any participants are tested, and each new participant is assigned to the next condition in the sequence. Random assignment is not guaranteed to control all extraneous variables across conditions. It is always possible that just by chance, the participants in one condition might turn out to be substantially older, less tired, more motivated, or less depressed on average than the participants in another condition. However, there are some reasons that this is not a major concern. One is that random assignment works better than one might expect, especially for large samples. Another is that the inferential statistics that researchers use to decide whether a difference between groups reflects a difference in the population takes the “fallibility” of random assignment into account. Yet another reason is that even if random assignment does result in a confounding variable and therefore produces misleading results, this is likely to be detected when the experiment is replicated. The upshot is that random assignment to conditions—although not infallible in terms of controlling extraneous variables—is always considered a strength of a research design.

Treatment and Control Conditions

Between-subjects experiments are often used to determine whether a treatment works. In psychological research, a treatment is any intervention meant to change people’s behavior for the better. This includes psychotherapies and medical treatments for psychological disorders but also interventions designed to improve learning, promote conservation, reduce prejudice, and so on. To determine whether a treatment works, participants are randomly assigned to either a treatment condition, in which they receive the treatment, or a control condition, in which they do not receive the treatment. If participants in the treatment condition end up better off than participants in the control condition—for example, they are less depressed, learn faster, conserve more, express less prejudice—then the researcher can conclude that the treatment works. In research on the effectiveness of psychotherapies and medical treatments, this type of experiment is often called a randomized clinical trial.

There are different types of control conditions. In a no-treatment control condition, participants receive no treatment whatsoever. One problem with this approach, however, is the existence of placebo effects. A placebo is a simulated treatment that lacks any active ingredient or element that should make it effective, and a placebo effect is a positive effect of such a treatment. Many folk remedies that seem to work—such as eating chicken soup for a cold or placing soap under the bedsheets to stop nighttime leg cramps—are probably nothing more than placebos. Although placebo effects are not well understood, they are probably driven primarily by people’s expectations that they will improve. Having the expectation to improve can result in reduced stress, anxiety, and depression, which can alter perceptions and even improve immune system functioning (Price, Finniss, & Benedetti, 2008).

Placebo effects are interesting in their own right, but they also pose a serious problem for researchers who want to determine whether a treatment works. Fortunately, there are several solutions to this problem. One is to include a placebo control condition, in which participants receive a placebo that looks much like the treatment but lacks the active ingredient or element thought to be responsible for the treatment’s effectiveness. When participants in a treatment condition take a pill, for example, then those in a placebo control condition would take an identical-looking pill that lacks the active ingredient in the treatment (a “sugar pill”). In research on psychotherapy effectiveness, the placebo might involve going to a psychotherapist and talking in an unstructured way about one’s problems. The idea is that if participants in both the treatment and the placebo control groups expect to improve, then any improvement in the treatment group over and above that in the placebo control group must have been caused by the treatment and not by participants’ expectations.

Of course, the principle of informed consent requires that participants be told that they will be assigned to either a treatment or a placebo control condition—even though they cannot be told which until the experiment ends. In many cases the participants who had been in the control condition are then offered an opportunity to have the real treatment. An alternative approach is to use a waitlist control condition, in which participants are told that they will receive the treatment but must wait until the participants in the treatment condition have already received it. This allows researchers to compare participants who have received the treatment with participants who are not currently receiving it but who still expect to improve (eventually). A final solution to the problem of placebo effects is to leave out the control condition completely and compare any new treatment with the best available alternative treatment. For example, a new treatment for simple phobia could be compared with standard exposure therapy. Because participants in both conditions receive a treatment, their expectations about improvement should be similar. This approach also makes sense because once there is an effective treatment, the interesting question about a new treatment is not simply “Does it work?” but “Does it work better than what is already available?”

Within-Subjects Experiments

In a within-subjects experiment, each participant is tested under all conditions. Consider an experiment on the effect of a defendant’s physical attractiveness on judgments of his guilt. Again, in a between-subjects experiment, one group of participants would be shown an attractive defendant and asked to judge his guilt, and another group of participants would be shown an unattractive defendant and asked to judge his guilt. In a within-subjects experiment, however, the same group of participants would judge the guilt of both an attractive and an unattractive defendant.

The primary advantage of this approach is that it provides maximum control of extraneous participant variables. Participants in all conditions have the same mean IQ, same socioeconomic status, same number of siblings, and so on—because they are the very same people. Within-subjects experiments also make it possible to use statistical procedures that remove the effect of these extraneous participant variables on the dependent variable and therefore make the data less “noisy” and the effect of the independent variable easier to detect. We will look more closely at this idea later in the book.

Carryover Effects and Counterbalancing

The primary disadvantage of within-subjects designs is that they can result in carryover effects. A carryover effect is an effect of being tested in one condition on participants’ behavior in later conditions. One type of carryover effect is a practice effect, where participants perform a task better in later conditions because they have had a chance to practice it. Another type is a fatigue effect, where participants perform a task worse in later conditions because they become tired or bored. Being tested in one condition can also change how participants perceive stimuli or interpret their task in later conditions. This is called a context effect. For example, an average-looking defendant might be judged more harshly when participants have just judged an attractive defendant than when they have just judged an unattractive defendant. Within-subjects experiments also make it easier for participants to guess the hypothesis. For example, a participant who is asked to judge the guilt of an attractive defendant and then is asked to judge the guilt of an unattractive defendant is likely to guess that the hypothesis is that defendant attractiveness affects judgments of guilt. This could lead the participant to judge the unattractive defendant more harshly because he thinks this is what he is expected to do. Or it could make participants judge the two defendants similarly in an effort to be “fair.”

Carryover effects can be interesting in their own right. (Does the attractiveness of one person depend on the attractiveness of other people that we have seen recently?) But when they are not the focus of the research, carryover effects can be problematic. Imagine, for example, that participants judge the guilt of an attractive defendant and then judge the guilt of an unattractive defendant. If they judge the unattractive defendant more harshly, this might be because of his unattractiveness. But it could be instead that they judge him more harshly because they are becoming bored or tired. In other words, the order of the conditions is a confounding variable. The attractive condition is always the first condition and the unattractive condition the second. Thus any difference between the conditions in terms of the dependent variable could be caused by the order of the conditions and not the independent variable itself.

There is a solution to the problem of order effects, however, that can be used in many situations. It is counterbalancing, which means testing different participants in different orders. For example, some participants would be tested in the attractive defendant condition followed by the unattractive defendant condition, and others would be tested in the unattractive condition followed by the attractive condition. With three conditions, there would be six different orders (ABC, ACB, BAC, BCA, CAB, and CBA), so some participants would be tested in each of the six orders. With counterbalancing, participants are assigned to orders randomly, using the techniques we have already discussed. Thus random assignment plays an important role in within-subjects designs just as in between-subjects designs. Here, instead of randomly assigning to conditions, they are randomly assigned to different orders of conditions. In fact, it can safely be said that if a study does not involve random assignment in one form or another, it is not an experiment.

There are two ways to think about what counterbalancing accomplishes. One is that it controls the order of conditions so that it is no longer a confounding variable. Instead of the attractive condition always being first and the unattractive condition always being second, the attractive condition comes first for some participants and second for others. Likewise, the unattractive condition comes first for some participants and second for others. Thus any overall difference in the dependent variable between the two conditions cannot have been caused by the order of conditions. A second way to think about what counterbalancing accomplishes is that if there are carryover effects, it makes it possible to detect them. One can analyze the data separately for each order to see whether it had an effect.

Simultaneous Within-Subjects Designs

So far, we have discussed an approach to within-subjects designs in which participants are tested in one condition at a time. There is another approach, however, that is often used when participants make multiple responses in each condition. Imagine, for example, that participants judge the guilt of 10 attractive defendants and 10 unattractive defendants. Instead of having people make judgments about all 10 defendants of one type followed by all 10 defendants of the other type, the researcher could present all 20 defendants in a sequence that mixed the two types. The researcher could then compute each participant’s mean rating for each type of defendant. Or imagine an experiment designed to see whether people with social anxiety disorder remember negative adjectives (e.g., “stupid,” “incompetent”) better than positive ones (e.g., “happy,” “productive”). The researcher could have participants study a single list that includes both kinds of words and then have them try to recall as many words as possible. The researcher could then count the number of each type of word that was recalled. There are many ways to determine the order in which the stimuli are presented, but one common way is to generate a different random order for each participant.

Between-Subjects or Within-Subjects?

Almost every experiment can be conducted using either a between-subjects design or a within-subjects design. This means that researchers must choose between the two approaches based on their relative merits for the particular situation.

Between-subjects experiments have the advantage of being conceptually simpler and requiring less testing time per participant. They also avoid carryover effects without the need for counterbalancing. Within-subjects experiments have the advantage of controlling extraneous participant variables, which generally reduces noise in the data and makes it easier to detect a relationship between the independent and dependent variables.

A good rule of thumb, then, is that if it is possible to conduct a within-subjects experiment (with proper counterbalancing) in the time that is available per participant—and you have no serious concerns about carryover effects—this is probably the best option. If a within-subjects design would be difficult or impossible to carry out, then you should consider a between-subjects design instead. For example, if you were testing participants in a doctor’s waiting room or shoppers in line at a grocery store, you might not have enough time to test each participant in all conditions and therefore would opt for a between-subjects design. Or imagine you were trying to reduce people’s level of prejudice by having them interact with someone of another race. A within-subjects design with counterbalancing would require testing some participants in the treatment condition first and then in a control condition. But if the treatment works and reduces people’s level of prejudice, then they would no longer be suitable for testing in the control condition. This is true for many designs that involve a treatment meant to produce long-term change in participants’ behavior (e.g., studies testing the effectiveness of psychotherapy). Clearly, a between-subjects design would be necessary here.

Remember also that using one type of design does not preclude using the other type in a different study. There is no reason that a researcher could not use both a between-subjects design and a within-subjects design to answer the same research question. In fact, professional researchers often do exactly this.

·         Experiments can be conducted using either between-subjects or within-subjects designs. Deciding which to use in a particular situation requires careful consideration of the pros and cons of each approach.

·         Random assignment to conditions in between-subjects experiments or to orders of conditions in within-subjects experiments is a fundamental element of experimental research. Its purpose is to control extraneous variables so that they do not become confounding variables.

·         Experimental research on the effectiveness of a treatment requires both a treatment condition and a control condition, which can be a no-treatment control condition, a placebo control condition, or a waitlist control condition. Experimental treatments can also be compared with the best available alternative.

9.3  Conducting Experiments

The information presented so far in this chapter is enough to design a basic experiment. When it comes time to conduct that experiment, however, several additional practical issues arise. In this section, we consider some of these issues and how to deal with them. Much of this information applies to non-experimental studies as well as experimental ones.

Recruiting Participants

Of course, you should be thinking about how you will obtain your participants from the beginning of any research project. Unless you have access to people with schizophrenia or incarcerated juvenile offenders, for example, then there is no point designing a study that focuses on these populations. But even if you plan to use a convenience sample, you will have to recruit participants for your study.

There are several approaches to recruiting participants. One is to use participants from a formal subject pool—an established group of people who have agreed to be contacted about participating in research studies. For example, at many colleges and universities, there is a subject pool consisting of students enrolled in introductory psychology courses who must participate in a certain number of studies to meet a course requirement. Researchers post descriptions of their studies and students sign up to participate, usually via an online system. Participants who are not in subject pools can also be recruited by posting or publishing advertisements or making personal appeals to groups that represent the population of interest. For example, a researcher interested in studying older adults could arrange to speak at a meeting of the residents at a retirement community to explain the study and ask for volunteers.

The Volunteer Subject

Even if the participants in a study receive compensation in the form of course credit, a small amount of money, or a chance at being treated for a psychological problem, they are still essentially volunteers. This is worth considering because people who volunteer to participate in psychological research have been shown to differ in predictable ways from those who do not volunteer. Specifically, there is good evidence that on average, volunteers have the following characteristics compared with non-volunteers (Rosenthal Rosnow, 1976):

·         They are more interested in the topic of the research.

·         They are more educated.

·         They have a greater need for approval.

·         They have higher intelligence quotients (IQs).

·         They are more sociable.

·         They are higher in social class.

This can be an issue of external validity if there is reason to believe that participants with these characteristics are likely to behave differently than the general population. For example, in testing different methods of persuading people, a rational argument might work better on volunteers than it does on the general population because of their generally higher educational level and IQ.

In many field experiments, the task is not recruiting participants but selecting them. For example, researchers Nicolas Guéguen and Marie-Agnès de Gail conducted a field experiment on the effect of being smiled at on helping, in which the participants were shoppers at a supermarket. A confederate walking down a stairway gazed directly at a shopper walking up the stairway and either smiled or did not smile. Shortly afterward, the shopper encountered another confederate, who dropped some computer diskettes on the ground. The dependent variable was whether or not the shopper stopped to help pick up the diskettes (Guéguen & de Gail, 2003). Notice that these participants were not “recruited,” but the researchers still had to select them from among all the shoppers taking the stairs that day. It is extremely important that this kind of selection be done according to a well-defined set of rules that is established before the data collection begins and can be explained clearly afterward. In this case, with each trip down the stairs, the confederate was instructed to gaze at the first person he encountered who appeared to be between the ages of 20 and 50. Only if the person gazed back did he or she become a participant in the study. The point of having a well-defined selection rule is to avoid bias in the selection of participants. For example, if the confederate was free to choose which shoppers he would gaze at, he might choose friendly-looking shoppers when he was set to smile and unfriendly-looking ones when he was not set to smile. As we will see shortly, such biases can be entirely unintentional.

Standardizing the Procedure

It is surprisingly easy to introduce extraneous variables during the procedure. For example, the same experimenter might give clear instructions to one participant but vague instructions to another. Or one experimenter might greet participants warmly while another barely makes eye contact with them. To the extent that such variables affect participants’ behaviour, they add noise to the data and make the effect of the independent variable more difficult to detect. If they vary across conditions, they become confounding variables and provide alternative explanations for the results. For example, if participants in a treatment group are tested by a warm and friendly experimenter and participants in a control group are tested by a cold and unfriendly one, then what appears to be an effect of the treatment might actually be an effect of experimenter demeanour.

Experimenter Expectancy Effects

It is well known that whether research participants are male or female can affect the results of a study. But what about whether the experimenter is male or female? There is plenty of evidence that this matters too. Male and female experimenters have slightly different ways of interacting with their participants, and of course participants also respond differently to male and female experimenters (Rosenthal, 1976). For example, in a recent study on pain perception, participants immersed their hands in icy water for as long as they could (Ibolya, Brake, & Voss, 2004). Male participants tolerated the pain longer when the experimenter was a woman, and female participants tolerated it longer when the experimenter was a man.

Researcher Robert Rosenthal has spent much of his career showing that this kind of unintended variation in the procedure does, in fact, affect participants’ behaviour. Furthermore, one important source of such variation is the experimenter’s expectations about how participants “should” behave in the experiment. This is referred to as an experimenter expectancy effect (Rosenthal, 1976). For example, if an experimenter expects participants in a treatment group to perform better on a task than participants in a control group, then he or she might unintentionally give the treatment group participants clearer instructions or more encouragement or allow them more time to complete the task. In a striking example, Rosenthal and Kermit Fode had several students in a laboratory course in psychology train rats to run through a maze. Although the rats were genetically similar, some of the students were told that they were working with “maze-bright” rats that had been bred to be good learners, and other students were told that they were working with “maze-dull” rats that had been bred to be poor learners. Sure enough, over five days of training, the “maze-bright” rats made more correct responses, made the correct response more quickly, and improved more steadily than the “maze-dull” rats (Rosenthal & Fode, 1963). Clearly it had to have been the students’ expectations about how the rats would perform that made the difference. But how? Some clues come from data gathered at the end of the study, which showed that students who expected their rats to learn quickly felt more positively about their animals and reported behaving toward them in a more friendly manner (e.g., handling them more).

The way to minimize unintended variation in the procedure is to standardize it as much as possible so that it is carried out in the same way for all participants regardless of the condition they are in. Here are several ways to do this:

·         Create a written protocol that specifies everything that the experimenters are to do and say from the time they greet participants to the time they dismiss them.

·         Create standard instructions that participants read themselves or that are read to them word for word by the experimenter.

·         Automate the rest of the procedure as much as possible by using software packages for this purpose or even simple computer slide shows.

·         Anticipate participants’ questions and either raise and answer them in the instructions or develop standard answers for them.

·         Train multiple experimenters on the protocol together and have them practice on each other.

·         Be sure that each experimenter tests participants in all conditions.

Another good practice is to arrange for the experimenters to be “blind” to the research question or to the condition that each participant is tested in. The idea is to minimize experimenter expectancy effects by minimizing the experimenters’ expectations. For example, in a drug study in which each participant receives the drug or a placebo, it is often the case that neither the participants nor the experimenter who interacts with the participants know which condition he or she has been assigned to. Because both the participants and the experimenters are blind to the condition, this is referred to as a double-blind study. (A single-blind study is one in which the participant, but not the experimenter, is blind to the condition.) Of course, there are many times this is not possible. For example, if you are both the investigator and the only experimenter, it is not possible for you to remain blind to the research question. Also, in many studies the experimenter must know the condition because he or she must carry out the procedure in a different way in the different conditions.

Record Keeping

It is essential to keep good records when you conduct an experiment. As discussed earlier, it is typical for experimenters to generate a written sequence of conditions before the study begins and then to test each new participant in the next condition in the sequence. As you test them, it is a good idea to add to this list basic demographic information; the date, time, and place of testing; and the name of the experimenter who did the testing. It is also a good idea to have a place for the experimenter to write down comments about unusual occurrences (e.g., a confused or uncooperative participant) or questions that come up. This kind of information can be useful later if you decide to analyze sex differences or effects of different experimenters, or if a question arises about a particular participant or testing session.

It can also be useful to assign an identification number to each participant as you test them. Simply numbering them consecutively beginning with 1 is usually sufficient. This number can then also be written on any response sheets or questionnaires that participants generate, making it easier to keep them together.

Pilot Testing

It is always a good idea to conduct a pilot test of your experiment. A pilot test is a small-scale study conducted to make sure that a new procedure works as planned. In a pilot test, you can recruit participants formally (e.g., from an established participant pool) or you can recruit them informally from among family, friends, classmates, and so on. The number of participants can be small, but it should be enough to give you confidence that your procedure works as planned. There are several important questions that you can answer by conducting a pilot test:

·         Do participants understand the instructions?

·         What kind of misunderstandings do participants have, what kind of mistakes do they make, and what kind of questions do they ask?

·         Do participants become bored or frustrated?

·         Is an indirect manipulation effective? (You will need to include a manipulation check.)

·         Can participants guess the research question or hypothesis?

·         How long does the procedure take?

·         Are computer programs or other automated procedures working properly?

·         Are data being recorded correctly?

Of course, to answer some of these questions you will need to observe participants carefully during the procedure and talk with them about it afterward. Participants are often hesitant to criticize a study in front of the researcher, so be sure they understand that this is a pilot test and you are genuinely interested in feedback that will help you improve the procedure. If the procedure works as planned, then you can proceed with the actual study. If there are problems to be solved, you can solve them, pilot test the new procedure, and continue with this process until you are ready to proceed.

·         There are several effective methods you can use to recruit research participants for your experiment, including through formal subject pools, advertisements, and personal appeals. Field experiments require well-defined participant selection procedures.

·         It is important to standardize experimental procedures to minimize extraneous variables, including experimenter expectancy effects.

·         It is important to conduct one or more small-scale pilot tests of an experiment to be sure that the procedure works as planned.

References from Chapter 9

Birnbaum, M. H. (1999). How to show that 9 221: Collect judgments in a between-subjects design. Psychological Methods, 4, 243–249.

Cialdini, R. (2005, April). Don’t throw in the towel: Use social influence research. APS Observer. Retrieved from  http://www.psychologicalscience.org/observer/getArticle.cfm?id=1762 .

Fredrickson, B. L., Roberts, T.-A., Noll, S. M., Quinn, D. M., & Twenge, J. M. (1998). The swimsuit becomes you: Sex differences in self-objectification, restrained eating, and math performance. Journal of Personality and Social Psychology, 75, 269–284.

Guéguen, N., & de Gail, Marie-Agnès. (2003). The effect of smiling on helping behavior: Smiling and good Samaritan behavior. Communication Reports, 16, 133–140.

Ibolya, K., Brake, A., & Voss, U. (2004). The effect of experimenter characteristics on pain reports in women and men. Pain, 112, 142–147.

Moseley, J. B., O’Malley, K., Petersen, N. J., Menke, T. J., Brody, B. A., Kuykendall, D. H., … & Wray, N. P. (2002). A controlled trial of arthroscopic surgery for osteoarthritis of the knee. The New England Journal of Medicine, 347, 81–88.

Price, D. D., Finniss, D. G., & Benedetti, F. (2008). A comprehensive review of the placebo effect: Recent advances and current thought. Annual Review of Psychology, 59, 565–590.

Rosenthal, R. (1976). Experimenter effects in behavioral research (enlarged ed.). New York, NY: Wiley.

Rosenthal, R., & Fode, K. (1963). The effect of experimenter bias on performance of the albino rat. Behavioral Science, 8, 183-189.

Rosenthal, R., & Rosnow, R. L. (1976). The volunteer subject. New York, NY: Wiley.

Shapiro, A. K., & Shapiro, E. (1999). The powerful placebo: From ancient priest to modern physician. Baltimore, MD: Johns Hopkins University Press.

Stanovich, K. E. (2010). How to think straight about psychology (9th ed.). Boston, MA: Allyn Bacon.

Research Methods in Psychology & Neuroscience Copyright © by Dalhousie University Introduction to Psychology and Neuroscience Team. All Rights Reserved.

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Experimental Research

24 Experimental Design

Learning objectives.

• Explain the difference between between-subjects and within-subjects experiments, list some of the pros and cons of each approach, and decide which approach to use to answer a particular research question.
• Define random assignment, distinguish it from random sampling, explain its purpose in experimental research, and use some simple strategies to implement it
• Define several types of carryover effect, give examples of each, and explain how counterbalancing helps to deal with them.

In this section, we look at some different ways to design an experiment. The primary distinction we will make is between approaches in which each participant experiences one level of the independent variable and approaches in which each participant experiences all levels of the independent variable. The former are called between-subjects experiments and the latter are called within-subjects experiments.

Between-Subjects Experiments

In a  between-subjects experiment , each participant is tested in only one condition. For example, a researcher with a sample of 100 university students might assign half of them to write about a traumatic event and the other half write about a neutral event. Or a researcher with a sample of 60 people with severe agoraphobia (fear of open spaces) might assign 20 of them to receive each of three different treatments for that disorder. It is essential in a between-subjects experiment that the researcher assigns participants to conditions so that the different groups are, on average, highly similar to each other. Those in a trauma condition and a neutral condition, for example, should include a similar proportion of men and women, and they should have similar average IQs, similar average levels of motivation, similar average numbers of health problems, and so on. This matching is a matter of controlling these extraneous participant variables across conditions so that they do not become confounding variables.

Random Assignment

The primary way that researchers accomplish this kind of control of extraneous variables across conditions is called  random assignment , which means using a random process to decide which participants are tested in which conditions. Do not confuse random assignment with random sampling. Random sampling is a method for selecting a sample from a population, and it is rarely used in psychological research. Random assignment is a method for assigning participants in a sample to the different conditions, and it is an important element of all experimental research in psychology and other fields too.

In its strictest sense, random assignment should meet two criteria. One is that each participant has an equal chance of being assigned to each condition (e.g., a 50% chance of being assigned to each of two conditions). The second is that each participant is assigned to a condition independently of other participants. Thus one way to assign participants to two conditions would be to flip a coin for each one. If the coin lands heads, the participant is assigned to Condition A, and if it lands tails, the participant is assigned to Condition B. For three conditions, one could use a computer to generate a random integer from 1 to 3 for each participant. If the integer is 1, the participant is assigned to Condition A; if it is 2, the participant is assigned to Condition B; and if it is 3, the participant is assigned to Condition C. In practice, a full sequence of conditions—one for each participant expected to be in the experiment—is usually created ahead of time, and each new participant is assigned to the next condition in the sequence as they are tested. When the procedure is computerized, the computer program often handles the random assignment.

One problem with coin flipping and other strict procedures for random assignment is that they are likely to result in unequal sample sizes in the different conditions. Unequal sample sizes are generally not a serious problem, and you should never throw away data you have already collected to achieve equal sample sizes. However, for a fixed number of participants, it is statistically most efficient to divide them into equal-sized groups. It is standard practice, therefore, to use a kind of modified random assignment that keeps the number of participants in each group as similar as possible. One approach is block randomization . In block randomization, all the conditions occur once in the sequence before any of them is repeated. Then they all occur again before any of them is repeated again. Within each of these “blocks,” the conditions occur in a random order. Again, the sequence of conditions is usually generated before any participants are tested, and each new participant is assigned to the next condition in the sequence.  Table 5.2  shows such a sequence for assigning nine participants to three conditions. The Research Randomizer website ( http://www.randomizer.org ) will generate block randomization sequences for any number of participants and conditions. Again, when the procedure is computerized, the computer program often handles the block randomization.

Random assignment is not guaranteed to control all extraneous variables across conditions. The process is random, so it is always possible that just by chance, the participants in one condition might turn out to be substantially older, less tired, more motivated, or less depressed on average than the participants in another condition. However, there are some reasons that this possibility is not a major concern. One is that random assignment works better than one might expect, especially for large samples. Another is that the inferential statistics that researchers use to decide whether a difference between groups reflects a difference in the population takes the “fallibility” of random assignment into account. Yet another reason is that even if random assignment does result in a confounding variable and therefore produces misleading results, this confound is likely to be detected when the experiment is replicated. The upshot is that random assignment to conditions—although not infallible in terms of controlling extraneous variables—is always considered a strength of a research design.

Matched Groups

An alternative to simple random assignment of participants to conditions is the use of a matched-groups design . Using this design, participants in the various conditions are matched on the dependent variable or on some extraneous variable(s) prior the manipulation of the independent variable. This guarantees that these variables will not be confounded across the experimental conditions. For instance, if we want to determine whether expressive writing affects people’s health then we could start by measuring various health-related variables in our prospective research participants. We could then use that information to rank-order participants according to how healthy or unhealthy they are. Next, the two healthiest participants would be randomly assigned to complete different conditions (one would be randomly assigned to the traumatic experiences writing condition and the other to the neutral writing condition). The next two healthiest participants would then be randomly assigned to complete different conditions, and so on until the two least healthy participants. This method would ensure that participants in the traumatic experiences writing condition are matched to participants in the neutral writing condition with respect to health at the beginning of the study. If at the end of the experiment, a difference in health was detected across the two conditions, then we would know that it is due to the writing manipulation and not to pre-existing differences in health.

Within-Subjects Experiments

In a  within-subjects experiment , each participant is tested under all conditions. Consider an experiment on the effect of a defendant’s physical attractiveness on judgments of his guilt. Again, in a between-subjects experiment, one group of participants would be shown an attractive defendant and asked to judge his guilt, and another group of participants would be shown an unattractive defendant and asked to judge his guilt. In a within-subjects experiment, however, the same group of participants would judge the guilt of both an attractive  and  an unattractive defendant.

The primary advantage of this approach is that it provides maximum control of extraneous participant variables. Participants in all conditions have the same mean IQ, same socioeconomic status, same number of siblings, and so on—because they are the very same people. Within-subjects experiments also make it possible to use statistical procedures that remove the effect of these extraneous participant variables on the dependent variable and therefore make the data less “noisy” and the effect of the independent variable easier to detect. We will look more closely at this idea later in the book .  However, not all experiments can use a within-subjects design nor would it be desirable to do so.

Carryover Effects and Counterbalancing

The primary disadvantage of within-subjects designs is that they can result in order effects. An order effect   occurs when participants’ responses in the various conditions are affected by the order of conditions to which they were exposed. One type of order effect is a carryover effect. A  carryover effect  is an effect of being tested in one condition on participants’ behavior in later conditions. One type of carryover effect is a  practice effect , where participants perform a task better in later conditions because they have had a chance to practice it. Another type is a fatigue effect , where participants perform a task worse in later conditions because they become tired or bored. Being tested in one condition can also change how participants perceive stimuli or interpret their task in later conditions. This  type of effect is called a  context effect (or contrast effect) . For example, an average-looking defendant might be judged more harshly when participants have just judged an attractive defendant than when they have just judged an unattractive defendant. Within-subjects experiments also make it easier for participants to guess the hypothesis. For example, a participant who is asked to judge the guilt of an attractive defendant and then is asked to judge the guilt of an unattractive defendant is likely to guess that the hypothesis is that defendant attractiveness affects judgments of guilt. This knowledge could lead the participant to judge the unattractive defendant more harshly because he thinks this is what he is expected to do. Or it could make participants judge the two defendants similarly in an effort to be “fair.”

Carryover effects can be interesting in their own right. (Does the attractiveness of one person depend on the attractiveness of other people that we have seen recently?) But when they are not the focus of the research, carryover effects can be problematic. Imagine, for example, that participants judge the guilt of an attractive defendant and then judge the guilt of an unattractive defendant. If they judge the unattractive defendant more harshly, this might be because of his unattractiveness. But it could be instead that they judge him more harshly because they are becoming bored or tired. In other words, the order of the conditions is a confounding variable. The attractive condition is always the first condition and the unattractive condition the second. Thus any difference between the conditions in terms of the dependent variable could be caused by the order of the conditions and not the independent variable itself.

There is a solution to the problem of order effects, however, that can be used in many situations. It is  counterbalancing , which means testing different participants in different orders. The best method of counterbalancing is complete counterbalancing   in which an equal number of participants complete each possible order of conditions. For example, half of the participants would be tested in the attractive defendant condition followed by the unattractive defendant condition, and others half would be tested in the unattractive condition followed by the attractive condition. With three conditions, there would be six different orders (ABC, ACB, BAC, BCA, CAB, and CBA), so some participants would be tested in each of the six orders. With four conditions, there would be 24 different orders; with five conditions there would be 120 possible orders. With counterbalancing, participants are assigned to orders randomly, using the techniques we have already discussed. Thus, random assignment plays an important role in within-subjects designs just as in between-subjects designs. Here, instead of randomly assigning to conditions, they are randomly assigned to different orders of conditions. In fact, it can safely be said that if a study does not involve random assignment in one form or another, it is not an experiment.

A more efficient way of counterbalancing is through a Latin square design which randomizes through having equal rows and columns. For example, if you have four treatments, you must have four versions. Like a Sudoku puzzle, no treatment can repeat in a row or column. For four versions of four treatments, the Latin square design would look like:

You can see in the diagram above that the square has been constructed to ensure that each condition appears at each ordinal position (A appears first once, second once, third once, and fourth once) and each condition precedes and follows each other condition one time. A Latin square for an experiment with 6 conditions would by 6 x 6 in dimension, one for an experiment with 8 conditions would be 8 x 8 in dimension, and so on. So while complete counterbalancing of 6 conditions would require 720 orders, a Latin square would only require 6 orders.

Finally, when the number of conditions is large experiments can use  random counterbalancing  in which the order of the conditions is randomly determined for each participant. Using this technique every possible order of conditions is determined and then one of these orders is randomly selected for each participant. This is not as powerful a technique as complete counterbalancing or partial counterbalancing using a Latin squares design. Use of random counterbalancing will result in more random error, but if order effects are likely to be small and the number of conditions is large, this is an option available to researchers.

There are two ways to think about what counterbalancing accomplishes. One is that it controls the order of conditions so that it is no longer a confounding variable. Instead of the attractive condition always being first and the unattractive condition always being second, the attractive condition comes first for some participants and second for others. Likewise, the unattractive condition comes first for some participants and second for others. Thus any overall difference in the dependent variable between the two conditions cannot have been caused by the order of conditions. A second way to think about what counterbalancing accomplishes is that if there are carryover effects, it makes it possible to detect them. One can analyze the data separately for each order to see whether it had an effect.

When 9 Is “Larger” Than 221

Researcher Michael Birnbaum has argued that the  lack  of context provided by between-subjects designs is often a bigger problem than the context effects created by within-subjects designs. To demonstrate this problem, he asked participants to rate two numbers on how large they were on a scale of 1-to-10 where 1 was “very very small” and 10 was “very very large”.  One group of participants were asked to rate the number 9 and another group was asked to rate the number 221 (Birnbaum, 1999) [1] . Participants in this between-subjects design gave the number 9 a mean rating of 5.13 and the number 221 a mean rating of 3.10. In other words, they rated 9 as larger than 221! According to Birnbaum, this  difference  is because participants spontaneously compared 9 with other one-digit numbers (in which case it is  relatively large) and compared 221 with other three-digit numbers (in which case it is relatively  small).

Simultaneous Within-Subjects Designs

So far, we have discussed an approach to within-subjects designs in which participants are tested in one condition at a time. There is another approach, however, that is often used when participants make multiple responses in each condition. Imagine, for example, that participants judge the guilt of 10 attractive defendants and 10 unattractive defendants. Instead of having people make judgments about all 10 defendants of one type followed by all 10 defendants of the other type, the researcher could present all 20 defendants in a sequence that mixed the two types. The researcher could then compute each participant’s mean rating for each type of defendant. Or imagine an experiment designed to see whether people with social anxiety disorder remember negative adjectives (e.g., “stupid,” “incompetent”) better than positive ones (e.g., “happy,” “productive”). The researcher could have participants study a single list that includes both kinds of words and then have them try to recall as many words as possible. The researcher could then count the number of each type of word that was recalled. 

Between-Subjects or Within-Subjects?

Almost every experiment can be conducted using either a between-subjects design or a within-subjects design. This possibility means that researchers must choose between the two approaches based on their relative merits for the particular situation.

Between-subjects experiments have the advantage of being conceptually simpler and requiring less testing time per participant. They also avoid carryover effects without the need for counterbalancing. Within-subjects experiments have the advantage of controlling extraneous participant variables, which generally reduces noise in the data and makes it easier to detect any effect of the independent variable upon the dependent variable. Within-subjects experiments also require fewer participants than between-subjects experiments to detect an effect of the same size.

A good rule of thumb, then, is that if it is possible to conduct a within-subjects experiment (with proper counterbalancing) in the time that is available per participant—and you have no serious concerns about carryover effects—this design is probably the best option. If a within-subjects design would be difficult or impossible to carry out, then you should consider a between-subjects design instead. For example, if you were testing participants in a doctor’s waiting room or shoppers in line at a grocery store, you might not have enough time to test each participant in all conditions and therefore would opt for a between-subjects design. Or imagine you were trying to reduce people’s level of prejudice by having them interact with someone of another race. A within-subjects design with counterbalancing would require testing some participants in the treatment condition first and then in a control condition. But if the treatment works and reduces people’s level of prejudice, then they would no longer be suitable for testing in the control condition. This difficulty is true for many designs that involve a treatment meant to produce long-term change in participants’ behavior (e.g., studies testing the effectiveness of psychotherapy). Clearly, a between-subjects design would be necessary here.

Remember also that using one type of design does not preclude using the other type in a different study. There is no reason that a researcher could not use both a between-subjects design and a within-subjects design to answer the same research question. In fact, professional researchers often take exactly this type of mixed methods approach.

• Birnbaum, M.H. (1999). How to show that 9>221: Collect judgments in a between-subjects design. Psychological Methods, 4 (3), 243-249. ↵

An experiment in which each participant is tested in only one condition.

Means using a random process to decide which participants are tested in which conditions.

All the conditions occur once in the sequence before any of them is repeated.

An experiment design in which the participants in the various conditions are matched on the dependent variable or on some extraneous variable(s) prior the manipulation of the independent variable.

An experiment in which each participant is tested under all conditions.

An effect that occurs when participants' responses in the various conditions are affected by the order of conditions to which they were exposed.

An effect of being tested in one condition on participants’ behavior in later conditions.

An effect where participants perform a task better in later conditions because they have had a chance to practice it.

An effect where participants perform a task worse in later conditions because they become tired or bored.

Unintended influences on respondents’ answers because they are not related to the content of the item but to the context in which the item appears.

Varying the order of the conditions in which participants are tested, to help solve the problem of order effects in within-subjects experiments.

A method in which an equal number of participants complete each possible order of conditions. 

A method in which the order of the conditions is randomly determined for each participant.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Home » Experimental Design – Types, Methods, Guide

Experimental Design – Types, Methods, Guide

Table of Contents

Experimental Design

Experimental design is a process of planning and conducting scientific experiments to investigate a hypothesis or research question. It involves carefully designing an experiment that can test the hypothesis, and controlling for other variables that may influence the results.

Experimental design typically includes identifying the variables that will be manipulated or measured, defining the sample or population to be studied, selecting an appropriate method of sampling, choosing a method for data collection and analysis, and determining the appropriate statistical tests to use.

Types of Experimental Design

Here are the different types of experimental design:

Completely Randomized Design

In this design, participants are randomly assigned to one of two or more groups, and each group is exposed to a different treatment or condition.

Randomized Block Design

This design involves dividing participants into blocks based on a specific characteristic, such as age or gender, and then randomly assigning participants within each block to one of two or more treatment groups.

Factorial Design

In a factorial design, participants are randomly assigned to one of several groups, each of which receives a different combination of two or more independent variables.

Repeated Measures Design

In this design, each participant is exposed to all of the different treatments or conditions, either in a random order or in a predetermined order.

Crossover Design

This design involves randomly assigning participants to one of two or more treatment groups, with each group receiving one treatment during the first phase of the study and then switching to a different treatment during the second phase.

Split-plot Design

In this design, the researcher manipulates one or more variables at different levels and uses a randomized block design to control for other variables.

Nested Design

This design involves grouping participants within larger units, such as schools or households, and then randomly assigning these units to different treatment groups.

Laboratory Experiment

Laboratory experiments are conducted under controlled conditions, which allows for greater precision and accuracy. However, because laboratory conditions are not always representative of real-world conditions, the results of these experiments may not be generalizable to the population at large.

Field Experiment

Field experiments are conducted in naturalistic settings and allow for more realistic observations. However, because field experiments are not as controlled as laboratory experiments, they may be subject to more sources of error.

Experimental Design Methods

Experimental design methods refer to the techniques and procedures used to design and conduct experiments in scientific research. Here are some common experimental design methods:

Randomization

This involves randomly assigning participants to different groups or treatments to ensure that any observed differences between groups are due to the treatment and not to other factors.

Control Group

The use of a control group is an important experimental design method that involves having a group of participants that do not receive the treatment or intervention being studied. The control group is used as a baseline to compare the effects of the treatment group.

Blinding involves keeping participants, researchers, or both unaware of which treatment group participants are in, in order to reduce the risk of bias in the results.

Counterbalancing

This involves systematically varying the order in which participants receive treatments or interventions in order to control for order effects.

Replication

Replication involves conducting the same experiment with different samples or under different conditions to increase the reliability and validity of the results.

This experimental design method involves manipulating multiple independent variables simultaneously to investigate their combined effects on the dependent variable.

This involves dividing participants into subgroups or blocks based on specific characteristics, such as age or gender, in order to reduce the risk of confounding variables.

Data Collection Method

Experimental design data collection methods are techniques and procedures used to collect data in experimental research. Here are some common experimental design data collection methods:

Direct Observation

This method involves observing and recording the behavior or phenomenon of interest in real time. It may involve the use of structured or unstructured observation, and may be conducted in a laboratory or naturalistic setting.

Self-report Measures

Self-report measures involve asking participants to report their thoughts, feelings, or behaviors using questionnaires, surveys, or interviews. These measures may be administered in person or online.

Behavioral Measures

Behavioral measures involve measuring participants’ behavior directly, such as through reaction time tasks or performance tests. These measures may be administered using specialized equipment or software.

Physiological Measures

Physiological measures involve measuring participants’ physiological responses, such as heart rate, blood pressure, or brain activity, using specialized equipment. These measures may be invasive or non-invasive, and may be administered in a laboratory or clinical setting.

Archival Data

Archival data involves using existing records or data, such as medical records, administrative records, or historical documents, as a source of information. These data may be collected from public or private sources.

Computerized Measures

Computerized measures involve using software or computer programs to collect data on participants’ behavior or responses. These measures may include reaction time tasks, cognitive tests, or other types of computer-based assessments.

Video Recording

Video recording involves recording participants’ behavior or interactions using cameras or other recording equipment. This method can be used to capture detailed information about participants’ behavior or to analyze social interactions.

Data Analysis Method

Experimental design data analysis methods refer to the statistical techniques and procedures used to analyze data collected in experimental research. Here are some common experimental design data analysis methods:

Descriptive Statistics

Descriptive statistics are used to summarize and describe the data collected in the study. This includes measures such as mean, median, mode, range, and standard deviation.

Inferential Statistics

Inferential statistics are used to make inferences or generalizations about a larger population based on the data collected in the study. This includes hypothesis testing and estimation.

Analysis of Variance (ANOVA)

ANOVA is a statistical technique used to compare means across two or more groups in order to determine whether there are significant differences between the groups. There are several types of ANOVA, including one-way ANOVA, two-way ANOVA, and repeated measures ANOVA.

Regression Analysis

Regression analysis is used to model the relationship between two or more variables in order to determine the strength and direction of the relationship. There are several types of regression analysis, including linear regression, logistic regression, and multiple regression.

Factor Analysis

Factor analysis is used to identify underlying factors or dimensions in a set of variables. This can be used to reduce the complexity of the data and identify patterns in the data.

Structural Equation Modeling (SEM)

SEM is a statistical technique used to model complex relationships between variables. It can be used to test complex theories and models of causality.

Cluster Analysis

Cluster analysis is used to group similar cases or observations together based on similarities or differences in their characteristics.

Time Series Analysis

Time series analysis is used to analyze data collected over time in order to identify trends, patterns, or changes in the data.

Multilevel Modeling

Multilevel modeling is used to analyze data that is nested within multiple levels, such as students nested within schools or employees nested within companies.

Applications of Experimental Design 

Experimental design is a versatile research methodology that can be applied in many fields. Here are some applications of experimental design:

• Medical Research: Experimental design is commonly used to test new treatments or medications for various medical conditions. This includes clinical trials to evaluate the safety and effectiveness of new drugs or medical devices.
• Agriculture : Experimental design is used to test new crop varieties, fertilizers, and other agricultural practices. This includes randomized field trials to evaluate the effects of different treatments on crop yield, quality, and pest resistance.
• Environmental science: Experimental design is used to study the effects of environmental factors, such as pollution or climate change, on ecosystems and wildlife. This includes controlled experiments to study the effects of pollutants on plant growth or animal behavior.
• Psychology : Experimental design is used to study human behavior and cognitive processes. This includes experiments to test the effects of different interventions, such as therapy or medication, on mental health outcomes.
• Engineering : Experimental design is used to test new materials, designs, and manufacturing processes in engineering applications. This includes laboratory experiments to test the strength and durability of new materials, or field experiments to test the performance of new technologies.
• Education : Experimental design is used to evaluate the effectiveness of teaching methods, educational interventions, and programs. This includes randomized controlled trials to compare different teaching methods or evaluate the impact of educational programs on student outcomes.
• Marketing : Experimental design is used to test the effectiveness of marketing campaigns, pricing strategies, and product designs. This includes experiments to test the impact of different marketing messages or pricing schemes on consumer behavior.

Examples of Experimental Design 

Here are some examples of experimental design in different fields:

• Example in Medical research : A study that investigates the effectiveness of a new drug treatment for a particular condition. Patients are randomly assigned to either a treatment group or a control group, with the treatment group receiving the new drug and the control group receiving a placebo. The outcomes, such as improvement in symptoms or side effects, are measured and compared between the two groups.
• Example in Education research: A study that examines the impact of a new teaching method on student learning outcomes. Students are randomly assigned to either a group that receives the new teaching method or a group that receives the traditional teaching method. Student achievement is measured before and after the intervention, and the results are compared between the two groups.
• Example in Environmental science: A study that tests the effectiveness of a new method for reducing pollution in a river. Two sections of the river are selected, with one section treated with the new method and the other section left untreated. The water quality is measured before and after the intervention, and the results are compared between the two sections.
• Example in Marketing research: A study that investigates the impact of a new advertising campaign on consumer behavior. Participants are randomly assigned to either a group that is exposed to the new campaign or a group that is not. Their behavior, such as purchasing or product awareness, is measured and compared between the two groups.
• Example in Social psychology: A study that examines the effect of a new social intervention on reducing prejudice towards a marginalized group. Participants are randomly assigned to either a group that receives the intervention or a control group that does not. Their attitudes and behavior towards the marginalized group are measured before and after the intervention, and the results are compared between the two groups.

When to use Experimental Research Design 

Experimental research design should be used when a researcher wants to establish a cause-and-effect relationship between variables. It is particularly useful when studying the impact of an intervention or treatment on a particular outcome.

Here are some situations where experimental research design may be appropriate:

• When studying the effects of a new drug or medical treatment: Experimental research design is commonly used in medical research to test the effectiveness and safety of new drugs or medical treatments. By randomly assigning patients to treatment and control groups, researchers can determine whether the treatment is effective in improving health outcomes.
• When evaluating the effectiveness of an educational intervention: An experimental research design can be used to evaluate the impact of a new teaching method or educational program on student learning outcomes. By randomly assigning students to treatment and control groups, researchers can determine whether the intervention is effective in improving academic performance.
• When testing the effectiveness of a marketing campaign: An experimental research design can be used to test the effectiveness of different marketing messages or strategies. By randomly assigning participants to treatment and control groups, researchers can determine whether the marketing campaign is effective in changing consumer behavior.
• When studying the effects of an environmental intervention: Experimental research design can be used to study the impact of environmental interventions, such as pollution reduction programs or conservation efforts. By randomly assigning locations or areas to treatment and control groups, researchers can determine whether the intervention is effective in improving environmental outcomes.
• When testing the effects of a new technology: An experimental research design can be used to test the effectiveness and safety of new technologies or engineering designs. By randomly assigning participants or locations to treatment and control groups, researchers can determine whether the new technology is effective in achieving its intended purpose.

How to Conduct Experimental Research

Here are the steps to conduct Experimental Research:

• Identify a Research Question : Start by identifying a research question that you want to answer through the experiment. The question should be clear, specific, and testable.
• Develop a Hypothesis: Based on your research question, develop a hypothesis that predicts the relationship between the independent and dependent variables. The hypothesis should be clear and testable.
• Design the Experiment : Determine the type of experimental design you will use, such as a between-subjects design or a within-subjects design. Also, decide on the experimental conditions, such as the number of independent variables, the levels of the independent variable, and the dependent variable to be measured.
• Select Participants: Select the participants who will take part in the experiment. They should be representative of the population you are interested in studying.
• Randomly Assign Participants to Groups: If you are using a between-subjects design, randomly assign participants to groups to control for individual differences.
• Conduct the Experiment : Conduct the experiment by manipulating the independent variable(s) and measuring the dependent variable(s) across the different conditions.
• Analyze the Data: Analyze the data using appropriate statistical methods to determine if there is a significant effect of the independent variable(s) on the dependent variable(s).
• Draw Conclusions: Based on the data analysis, draw conclusions about the relationship between the independent and dependent variables. If the results support the hypothesis, then it is accepted. If the results do not support the hypothesis, then it is rejected.
• Communicate the Results: Finally, communicate the results of the experiment through a research report or presentation. Include the purpose of the study, the methods used, the results obtained, and the conclusions drawn.

Purpose of Experimental Design 

The purpose of experimental design is to control and manipulate one or more independent variables to determine their effect on a dependent variable. Experimental design allows researchers to systematically investigate causal relationships between variables, and to establish cause-and-effect relationships between the independent and dependent variables. Through experimental design, researchers can test hypotheses and make inferences about the population from which the sample was drawn.

Experimental design provides a structured approach to designing and conducting experiments, ensuring that the results are reliable and valid. By carefully controlling for extraneous variables that may affect the outcome of the study, experimental design allows researchers to isolate the effect of the independent variable(s) on the dependent variable(s), and to minimize the influence of other factors that may confound the results.

Experimental design also allows researchers to generalize their findings to the larger population from which the sample was drawn. By randomly selecting participants and using statistical techniques to analyze the data, researchers can make inferences about the larger population with a high degree of confidence.

Overall, the purpose of experimental design is to provide a rigorous, systematic, and scientific method for testing hypotheses and establishing cause-and-effect relationships between variables. Experimental design is a powerful tool for advancing scientific knowledge and informing evidence-based practice in various fields, including psychology, biology, medicine, engineering, and social sciences.

Advantages of Experimental Design 

Experimental design offers several advantages in research. Here are some of the main advantages:

• Control over extraneous variables: Experimental design allows researchers to control for extraneous variables that may affect the outcome of the study. By manipulating the independent variable and holding all other variables constant, researchers can isolate the effect of the independent variable on the dependent variable.
• Establishing causality: Experimental design allows researchers to establish causality by manipulating the independent variable and observing its effect on the dependent variable. This allows researchers to determine whether changes in the independent variable cause changes in the dependent variable.
• Replication : Experimental design allows researchers to replicate their experiments to ensure that the findings are consistent and reliable. Replication is important for establishing the validity and generalizability of the findings.
• Random assignment: Experimental design often involves randomly assigning participants to conditions. This helps to ensure that individual differences between participants are evenly distributed across conditions, which increases the internal validity of the study.
• Precision : Experimental design allows researchers to measure variables with precision, which can increase the accuracy and reliability of the data.
• Generalizability : If the study is well-designed, experimental design can increase the generalizability of the findings. By controlling for extraneous variables and using random assignment, researchers can increase the likelihood that the findings will apply to other populations and contexts.

Limitations of Experimental Design

Experimental design has some limitations that researchers should be aware of. Here are some of the main limitations:

• Artificiality : Experimental design often involves creating artificial situations that may not reflect real-world situations. This can limit the external validity of the findings, or the extent to which the findings can be generalized to real-world settings.
• Ethical concerns: Some experimental designs may raise ethical concerns, particularly if they involve manipulating variables that could cause harm to participants or if they involve deception.
• Participant bias : Participants in experimental studies may modify their behavior in response to the experiment, which can lead to participant bias.
• Limited generalizability: The conditions of the experiment may not reflect the complexities of real-world situations. As a result, the findings may not be applicable to all populations and contexts.
• Cost and time : Experimental design can be expensive and time-consuming, particularly if the experiment requires specialized equipment or if the sample size is large.
• Researcher bias : Researchers may unintentionally bias the results of the experiment if they have expectations or preferences for certain outcomes.
• Lack of feasibility : Experimental design may not be feasible in some cases, particularly if the research question involves variables that cannot be manipulated or controlled.

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11+ Psychology Experiment Ideas (Goals + Methods)

Have you ever wondered why some days you remember things easily, while on others you keep forgetting? Or why certain songs make you super happy and others just…meh?

Our minds are like big, mysterious puzzles, and every day we're finding new pieces to fit. One of the coolest ways to explore our brains and the way they work is through psychology experiments.

A psychology experiment is a special kind of test or activity researchers use to learn more about how our minds work and why we behave the way we do.

It's like a detective game where scientists ask questions and try out different clues to find answers about our feelings, thoughts, and actions. These experiments aren't just for scientists in white coats but can be fun activities we all try to discover more about ourselves and others.

Some of these experiments have become so famous, they’re like the celebrities of the science world! Like the Marshmallow Test, where kids had to wait to eat a yummy marshmallow, or Pavlov's Dogs, where dogs learned to drool just hearing a bell.

Let's look at a few examples of psychology experiments you can do at home.

What Are Some Classic Experiments?

Imagine a time when the mysteries of the mind were being uncovered in groundbreaking ways. During these moments, a few experiments became legendary, capturing the world's attention with their intriguing results.

The Marshmallow Test

One of the most talked-about experiments of the 20th century was the Marshmallow Test , conducted by Walter Mischel in the late 1960s at Stanford University.

The goal was simple but profound: to understand a child's ability to delay gratification and exercise self-control.

Children were placed in a room with a marshmallow and given a choice: eat the marshmallow now or wait 15 minutes and receive two as a reward. Many kids struggled with the wait, some devouring the treat immediately, while others demonstrated remarkable patience.

But the experiment didn’t end there. Years later, Mischel discovered something astonishing. The children who had waited for the second marshmallow were generally more successful in several areas of life, from school achievements to job satisfaction!

While this experiment highlighted the importance of teaching patience and self-control from a young age, it wasn't without its criticisms. Some argued that a child's background, upbringing, or immediate surroundings might play a significant role in their choices.

Moreover, there were concerns about the ethics of judging a child's potential success based on a brief interaction with a marshmallow.

Pavlov's Dogs

Traveling further back in time and over to Russia, another classic experiment took the world by storm. Ivan Pavlov , in the early 1900s, wasn't initially studying learning or behavior. He was exploring the digestive systems of dogs.

But during his research, Pavlov stumbled upon a fascinating discovery. He noticed that by ringing a bell every time he fed his dogs, they eventually began to associate the bell's sound with mealtime. So much so, that merely ringing the bell, even without presenting food, made the dogs drool in anticipation!

This reaction demonstrated the concept of "conditioning" - where behaviors can be learned by linking two unrelated stimuli. Pavlov's work revolutionized the world's understanding of learning and had ripple effects in various areas like animal training and therapy techniques.

Pavlov came up with the term classical conditioning , which is still used today. Other psychologists have developed more nuanced types of conditioning that help us understand how people learn to perform different behaviours.

Classical conditioning is the process by which a neutral stimulus becomes associated with a meaningful stimulus , leading to the same response. In Pavlov's case, the neutral stimulus (bell) became associated with the meaningful stimulus (food), leading the dogs to salivate just by hearing the bell.

Modern thinkers often critique Pavlov's methods from an ethical standpoint. The dogs, crucial to his discovery, may not have been treated with today's standards of care and respect in research.

Both these experiments, while enlightening, also underline the importance of conducting research with empathy and consideration, especially when it involves living beings.

What is Ethical Experimentation?

The tales of Pavlov's bells and Mischel's marshmallows offer us not just insights into the human mind and behavior but also raise a significant question: At what cost do these discoveries come?

Ethical experimentation isn't just a fancy term; it's the backbone of good science. When we talk about ethics, we're referring to the moral principles that guide a researcher's decisions and actions. But why does it matter so much in the realm of psychological experimentation?

An example of an experiment that had major ethical issues is an experiment called the Monster Study . This study was conducted in 1936 and was interested in why children develop a stutter.

The major issue with it is that the psychologists treated some of the children poorly over a period of five months, telling them things like “You must try to stop yourself immediately. Don’t ever speak unless you can do it right.”

You can imagine how that made the children feel!

This study helped create guidelines for ethical treatment in experiments. The guidelines include:

Respect for Individuals: Whether it's a dog in Pavlov's lab or a child in Mischel's study room, every participant—human or animal—deserves respect. They should never be subjected to harm or undue stress. For humans, informed consent (knowing what they're signing up for) is a must. This means that if a child is participating, they, along with their guardians, should understand what the experiment entails and agree to it without being pressured.

Honesty is the Best Policy: Researchers have a responsibility to be truthful. This means not only being honest with participants about the study but also reporting findings truthfully, even if the results aren't what they hoped for. There can be exceptions if an experiment will only succeed if the participants aren't fully aware, but it has to be approved by an ethics committee .

Safety First: No discovery, no matter how groundbreaking, is worth harming a participant. The well-being and mental, emotional, and physical safety of participants is paramount. Experiments should be designed to minimize risks and discomfort.

Considering the Long-Term: Some experiments might have effects that aren't immediately obvious. For example, while a child might seem fine after participating in an experiment, they could feel stressed or anxious later on. Ethical researchers consider and plan for these possibilities, offering support and follow-up if needed.

The Rights of Animals: Just because animals can't voice their rights doesn't mean they don't have any. They should be treated with care, dignity, and respect. This means providing them with appropriate living conditions, not subjecting them to undue harm, and considering alternatives to animal testing when possible.

While the world of psychological experiments offers fascinating insights into behavior and the mind, it's essential to tread with care and compassion. The golden rule? Treat every participant, human or animal, as you'd wish to be treated. After all, the true mark of a groundbreaking experiment isn't just its findings but the ethical integrity with which it's conducted.

So, even if you're experimenting at home, please keep in mind the impact your experiments could have on the people and beings around you!

Let's get into some ideas for experiments.

1) Testing Conformity

Our primary aim with this experiment is to explore the intriguing world of social influences, specifically focusing on how much sway a group has over an individual's decisions. This social influence is called groupthink .

Humans, as social creatures, often find solace in numbers, seeking the approval and acceptance of those around them. But how deep does this need run? Does the desire to "fit in" overpower our trust in our own judgments?

This experiment not only provides insights into these questions but also touches upon the broader themes of peer pressure, societal norms, and individuality. Understanding this could shed light on various real-world situations, from why fashion trends catch on to more critical scenarios like how misinformation can spread.

Method: This idea is inspired by the classic Asch Conformity Experiments . Here's a simple way to try it:

• Assemble a group of people (about 7-8). Only one person will be the real participant; the others will be in on the experiment.
• Show the group a picture of three lines of different lengths and another line labeled "Test Line."
• Ask each person to say out loud which of the three lines matches the length of the "Test Line."
• Unknown to the real participant, the other members will intentionally choose the wrong line. This is to see if the participant goes along with the group's incorrect choice, even if they can see it's wrong.

Real-World Impacts of Groupthink

Groupthink is more than just a science term; we see it in our daily lives:

Decisions at Work or School: Imagine being in a group where everyone wants to do one thing, even if it's not the best idea. People might not speak up because they're worried about standing out or being the only one with a different opinion.

Wrong Information: Ever heard a rumor that turned out to be untrue? Sometimes, if many people believe and share something, others might believe it too, even if it's not correct. This happens a lot on the internet.

Peer Pressure: Sometimes, friends might all want to do something that's not safe or right. People might join in just because they don't want to feel left out.

Missing Out on New Ideas: When everyone thinks the same way and agrees all the time, cool new ideas might never get heard. It's like always coloring with the same crayon and missing out on all the other bright colors!

2) Testing Color and Mood

We all have favorite colors, right? But did you ever wonder if colors can make you feel a certain way? Color psychology is the study of how colors can influence our feelings and actions.

For instance, does blue always calm us down? Does red make us feel excited or even a bit angry? By exploring this, we can learn how colors play a role in our daily lives, from the clothes we wear to the color of our bedroom walls.

• Find a quiet room and set up different colored lights or large sheets of colored paper: blue, red, yellow, and green.
• Invite some friends over and let each person spend a few minutes under each colored light or in front of each colored paper.
• After each color, ask your friends to write down or talk about how they feel. Are they relaxed? Energized? Happy? Sad?

Researchers have always been curious about this. Some studies have shown that colors like blue and green can make people feel calm, while colors like red might make them feel more alert or even hungry!

Real-World Impacts of Color Psychology

Ever noticed how different places use colors?

Hospitals and doctors' clinics often use soft blues and greens. This might be to help patients feel more relaxed and calm.

Many fast food restaurants use bright reds and yellows. These colors might make us feel hungry or want to eat quickly and leave.

Classrooms might use a mix of colors to help students feel both calm and energized.

3) Testing Music and Brainpower

Think about your favorite song. Do you feel smarter or more focused when you listen to it? This experiment seeks to understand the relationship between music and our brain's ability to remember things. Some people believe that certain types of music, like classical tunes, can help us study or work better. Let's find out if it's true!

• Prepare a list of 10-15 things to remember, like a grocery list or names of places.
• Invite some friends over. First, let them try to memorize the list in a quiet room.
• After a short break, play some music (try different types like pop, classical, or even nature sounds) and ask them to memorize the list again.
• Compare the results. Was there a difference in how much they remembered with and without music?

The " Mozart Effect " is a popular idea. Some studies in the past suggested that listening to Mozart's music might make people smarter, at least for a little while. But other researchers think the effect might not be specific to Mozart; it could be that any music we enjoy boosts our mood and helps our brain work better.

Real-World Impacts of Music and Memory

Think about how we use music:

• Study Sessions: Many students listen to music while studying, believing it helps them concentrate better.
• Workout Playlists: Gyms play energetic music to keep people motivated and help them push through tough workouts.
• Meditation and Relaxation: Calm, soothing sounds are often used to help people relax or meditate.

4) Testing Dreams and Food

Ever had a really wild dream and wondered where it came from? Some say that eating certain foods before bedtime can make our dreams more vivid or even a bit strange.

This experiment is all about diving into the dreamy world of sleep to see if what we eat can really change our nighttime adventures. Can a piece of chocolate or a slice of cheese transport us to a land of wacky dreams? Let's find out!

• Ask a group of friends to keep a "dream diary" for a week. Every morning, they should write down what they remember about their dreams.
• For the next week, ask them to eat a small snack before bed, like cheese, chocolate, or even spicy foods.
• They should continue writing in their "dream diary" every morning.
• At the end of the two weeks, compare the dream notes. Do the dreams seem different during the snack week?

The link between food and dreams isn't super clear, but some people have shared personal stories. For example, some say that spicy food can lead to bizarre dreams. Scientists aren't completely sure why, but it could be related to how food affects our body temperature or brain activity during sleep.

A cool idea related to this experiment is that of vivid dreams , which are very clear, detailed, and easy to remember dreams. Some people are even able to control their vivid dreams, or say that they feel as real as daily, waking life !

Real-World Impacts of Food and Dreams

Our discoveries might shed light on:

• Bedtime Routines: Knowing which foods might affect our dreams can help us choose better snacks before bedtime, especially if we want calmer sleep.
• Understanding Our Brain: Dreams can be mysterious, but studying them can give us clues about how our brains work at night.
• Cultural Beliefs: Many cultures have myths or stories about foods and dreams. Our findings might add a fun twist to these age-old tales!

5) Testing Mirrors and Self-image

Stand in front of a mirror. How do you feel? Proud? Shy? Curious? Mirrors reflect more than just our appearance; they might influence how we think about ourselves.

This experiment delves into the mystery of self-perception. Do we feel more confident when we see our reflection? Or do we become more self-conscious? Let's take a closer look.

• Set up two rooms: one with mirrors on all walls and another with no mirrors at all.
• Invite friends over and ask them to spend some time in each room doing normal activities, like reading or talking.
• After their time in both rooms, ask them questions like: "Did you think about how you looked more in one room? Did you feel more confident or shy?"
• Compare the responses to see if the presence of mirrors changes how they feel about themselves.

Studies have shown that when people are in rooms with mirrors, they can become more aware of themselves. Some might stand straighter, fix their hair, or even change how they behave. The mirror acts like an audience, making us more conscious of our actions.

Real-World Impacts of Mirrors and Self-perception

Mirrors aren't just for checking our hair. Ever wonder why clothing stores have so many mirrors? They might help shoppers visualize themselves in new outfits, encouraging them to buy.

Mirrors in gyms can motivate people to work out with correct form and posture. They also help us see progress in real-time!

And sometimes, looking in a mirror can be a reminder to take care of ourselves, both inside and out.

But remember, what we look like isn't as important as how we act in the world or how healthy we are. Some people claim that having too many mirrors around can actually make us more self conscious and distract us from the good parts of ourselves.

Some studies are showing that mirrors can actually increase self-compassion , amongst other things. As any tool, it seems like mirrors can be both good and bad, depending on how we use them!

6) Testing Plants and Talking

Have you ever seen someone talking to their plants? It might sound silly, but some people believe that plants can "feel" our vibes and that talking to them might even help them grow better.

In this experiment, we'll explore whether plants can indeed react to our voices and if they might grow taller, faster, or healthier when we chat with them.

• Get three similar plants, placing each one in a separate room.
• Talk to the first plant, saying positive things like "You're doing great!" or singing to it.
• Say negative things to the second plant, like "You're not growing fast enough!"
• Don't talk to the third plant at all; let it be your "silent" control group .
• Water all plants equally and make sure they all get the same amount of light.
• At the end of the month, measure the growth of each plant and note any differences in their health or size.

The idea isn't brand new. Some experiments from the past suggest plants might respond to sounds or vibrations. Some growers play music for their crops, thinking it helps them flourish.

Even if talking to our plants doesn't have an impact on their growth, it can make us feel better! Sometimes, if we are lonely, talking to our plants can help us feel less alone. Remember, they are living too!

Real-World Impacts of Talking to Plants

If plants do react to our voices, gardeners and farmers might adopt new techniques, like playing music in greenhouses or regularly talking to plants.

Taking care of plants and talking to them could become a recommended activity for reducing stress and boosting mood.

And if plants react to sound, it gives us a whole new perspective on how connected all living things might be .

7) Testing Virtual Reality and Senses

Virtual reality (VR) seems like magic, doesn't it? You put on a headset and suddenly, you're in a different world! But how does this "new world" affect our senses? This experiment wants to find out how our brains react to VR compared to the real world. Do we feel, see, or hear things differently? Let's get to the bottom of this digital mystery!

• You'll need a VR headset and a game or experience that can be replicated in real life (like walking through a forest). If you don't have a headset yourself, there are virtual reality arcades now!
• Invite friends to first experience the scenario in VR.
• Afterwards, replicate the experience in the real world, like taking a walk in an actual forest.
• Ask them questions about both experiences: Did one seem more real than the other? Which sounds were more clear? Which colors were brighter? Did they feel different emotions?

As VR becomes more popular, scientists have been curious about its effects. Some studies show that our brains can sometimes struggle to tell the difference between VR and reality. That's why some people might feel like they're really "falling" in a VR game even though they're standing still.

Real-World Impacts of VR on Our Senses

Schools might use VR to teach lessons, like taking students on a virtual trip to ancient Egypt. Understanding how our senses react in VR can also help game designers create even more exciting and realistic games.

Doctors could use VR to help patients overcome fears or to provide relaxation exercises. This is actually already a method therapists can use for helping patients who have serious phobias. This is called exposure therapy , which basically means slowly exposing someone (or yourself) to the thing you fear, starting from very far away to becoming closer.

For instance, if someone is afraid of snakes. You might show them images of snakes first. Once they are comfortable with the picture, they can know there is one in the next room. Once they are okay with that, they might use a VR headset to see the snake in the same room with them, though of course there is not an actual snake there.

8) Testing Sleep and Learning

We all know that feeling of trying to study or work when we're super tired. Our brains feel foggy, and it's hard to remember stuff. But how exactly does sleep (or lack of it) influence our ability to learn and remember things?

With this experiment, we'll uncover the mysteries of sleep and see how it can be our secret weapon for better learning.

• Split participants into two groups.
• Ask both groups to study the same material in the evening.
• One group goes to bed early, while the other stays up late.
• The next morning, give both groups a quiz on what they studied.
• Compare the results to see which group remembered more.

Sleep and its relation to learning have been explored a lot. Scientists believe that during sleep, especially deep sleep, our brains sort and store new information. This is why sometimes, after a good night's rest, we might understand something better or remember more.

Real-World Impacts of Sleep and Learning

Understanding the power of sleep can help:

• Students: If they know the importance of sleep, students might plan better, mixing study sessions with rest, especially before big exams.
• Workplaces: Employers might consider more flexible hours, understanding that well-rested employees learn faster and make fewer mistakes.
• Health: Regularly missing out on sleep can have other bad effects on our health. So, promoting good sleep is about more than just better learning.

9) Testing Social Media and Mood

Have you ever felt different after spending time on social media? Maybe happy after seeing a friend's fun photos, or a bit sad after reading someone's tough news.

Social media is a big part of our lives, but how does it really affect our mood? This experiment aims to shine a light on the emotional roller-coaster of likes, shares, and comments.

• Ask participants to note down how they're feeling - are they happy, sad, excited, or bored?
• Have them spend a set amount of time (like 30 minutes) on their favorite social media platforms.
• After the session, ask them again about their mood. Did it change? Why?
• Discuss what they saw or read that made them feel that way.

Previous research has shown mixed results. Some studies suggest that seeing positive posts can make us feel good, while others say that too much time on social media can make us feel lonely or left out.

Real-World Impacts of Social Media on Mood

Understanding the emotional impact of social media can help users understand their feelings and take breaks if needed. Knowing is half the battle! Additionally, teachers and parents can guide young users on healthy social media habits, like limiting time or following positive accounts.

And if it's shown that social media does impact mood, social media companies can design friendlier, less stressful user experiences.

But even if the social media companies don't change things, we can still change our social media habits to make ourselves feel better.

10) Testing Handwriting or Typing

Think about the last time you took notes. Did you grab a pen and paper or did you type them out on a computer or tablet?

Both ways are popular, but there's a big question: which method helps us remember and understand better? In this experiment, we'll find out if the classic art of handwriting has an edge over speedy typing.

• Divide participants into two groups.
• Present a short lesson or story to both groups.
• One group will take notes by hand, while the other will type them out.
• After some time, quiz both groups on the content of the lesson or story.
• Compare the results to see which note-taking method led to better recall and understanding.

Studies have shown some interesting results. While typing can be faster and allows for more notes, handwriting might boost memory and comprehension because it engages the brain differently, making us process the information as we write.

Importantly, each person might find one or the other works better for them. This could be useful in understanding our learning habits and what instructional style would be best for us.

Real-World Impacts of Handwriting vs. Typing

Knowing the pros and cons of each method can:

• Boost Study Habits: Students can pick the method that helps them learn best, especially during important study sessions or lectures.
• Work Efficiency: In jobs where information retention is crucial, understanding the best method can increase efficiency and accuracy.
• Tech Design: If we find out more about how handwriting benefits us, tech companies might design gadgets that mimic the feel of writing while combining the advantages of digital tools.

11) Testing Money and Happiness

We often hear the saying, "Money can't buy happiness," but is that really true? Many dream of winning the lottery or getting a big raise, believing it would solve all problems.

In this experiment, we dig deep to see if there's a real connection between wealth and well-being.

• Survey a range of participants, from those who earn a little to those who earn a lot, about their overall happiness. You can keep it to your friends and family, but that might not be as accurate as surveying a wider group of people.
• Ask them to rank things that bring them joy and note if they believe more money would boost their happiness. You could try different methods, one where you include some things that they have to rank, such as gardening, spending time with friends, reading books, learning, etc. Or you could just leave a blank list that they can fill in with their own ideas.
• Study the data to find patterns or trends about income and happiness.

Some studies have found money can boost happiness, especially when it helps people out of tough financial spots. But after reaching a certain income, extra dollars usually do not add much extra joy.

In fact, psychologists just realized that once people have an income that can comfortably support their needs (and some of their wants), they stop getting happier with more . That number is roughly $75,000, but of course that depends on the cost of living and how many members are in the family.

Real-World Impacts of Money and Happiness

If we can understand the link between money and joy, it might help folks choose jobs they love over jobs that just pay well. And instead of buying things, people might spend on experiences, like trips or classes, that make lasting memories.

Most importantly, we all might spend more time on hobbies, friends, and family, knowing they're big parts of what makes life great.

Some people are hoping that with Artificial Intelligence being able to do a lot of the less well-paying jobs, people might be able to do work they enjoy more, all while making more money and having more time to do the things that make them happy.

12) Testing Temperature and Productivity

Have you ever noticed how a cold classroom or office makes it harder to focus? Or how on hot days, all you want to do is relax? In this experiment, we're going to find out if the temperature around us really does change how well we work.

• Find a group of participants and a room where you can change the temperature.
• Set the room to a chilly temperature and give the participants a set of tasks to do.
• Measure how well and quickly they do these tasks.
• The next day, make the room comfortably warm and have them do similar tasks.
• Compare the results to see if the warmer or cooler temperature made them work better.

Some studies have shown that people can work better when they're in a room that feels just right, not too cold or hot. Being too chilly can make fingers slow, and being too warm can make minds wander.

What temperature is "just right"? It won't be the same for everyone, but most people find it's between 70-73 degrees Fahrenheit (21-23 Celsius).

Real-World Implications of Temperature and Productivity

If we can learn more about how temperature affects our work, teachers might set classroom temperatures to help students focus and learn better, offices might adjust temperatures to get the best work out of their teams, and at home, we might find the best temperature for doing homework or chores quickly and well.

Interestingly, temperature also has an impact on our sleep quality. Most people find slightly cooler rooms to be better for good sleep. While the daytime temperature between 70-73F is good for productivity, a nighttime temperature around 65F (18C) is ideal for most people's sleep.

Psychology is like a treasure hunt, where the prize is understanding ourselves better. With every experiment, we learn a little more about why we think, feel, and act the way we do. Some of these experiments might seem simple, like seeing if colors change our mood or if being warm helps us work better. But even the simple questions can have big answers that help us in everyday life.

Remember, while doing experiments is fun, it's also important to always be kind and think about how others feel. We should never make someone uncomfortable just for a test. Instead, let's use these experiments to learn and grow, helping to make the world a brighter, more understanding place for everyone.

Related posts:

• 150+ Flirty Goodnight Texts For Him (Sweet and Naughty Examples)
• Dream Interpreter & Dictionary (270+ Meanings)
• Sleep Stages (Light, Deep, REM)
• What Part of the Brain Regulates Body Temperature?
• Why Do We Dream? (6 Theories and Psychological Reasons)

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REVIEW article

Eeg-based study of design creativity: a review on research design, experiments, and analysis.

• Concordia Institute for Information Systems Engineering, Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, QC, Canada

Brain dynamics associated with design creativity tasks are largely unexplored. Despite significant strides, there is a limited understanding of the brain-behavior during design creation tasks. The objective of this paper is to review the concepts of creativity and design creativity as well as their differences, and to explore the brain dynamics associated with design creativity tasks using electroencephalography (EEG) as a neuroimaging tool. The paper aims to provide essential insights for future researchers in the field of design creativity neurocognition. It seeks to examine fundamental studies, present key findings, and initiate a discussion on associated brain dynamics. The review employs thematic analysis and a forward and backward snowball search methodology with specific inclusion and exclusion criteria to select relevant studies. This search strategy ensured a comprehensive review focused on EEG-based creativity and design creativity experiments. Different components of those experiments such as participants, psychometrics, experiment design, and creativity tasks, are reviewed and then discussed. The review identifies that while some studies have converged on specific findings regarding EEG alpha band activity in creativity experiments, there remain inconsistencies in the literature. The paper underscores the need for further research to unravel the interplays between these cognitive processes. This comprehensive review serves as a valuable resource for readers seeking an understanding of current literature, principal discoveries, and areas where knowledge remains incomplete. It highlights both positive and foundational aspects, identifies gaps, and poses lingering questions to guide future research endeavors.

1 Introduction

1.1 creativity, design, and design creativity.

Investigating design creativity presents significant challenges due to its multifaceted nature, involving nonlinear cognitive processes and various subtasks such as divergent and convergent thinking, perception, memory retrieval, learning, inferring, understanding, and designing ( Gero, 1994 ; Gero, 2011 ; Nguyen and Zeng, 2012 ; Jung and Vartanian, 2018 ; Xie, 2023 ). Additionally, design creativity tasks are often ambiguous, intricate, and nonlinear, further complicating efforts to understand the underlying mechanisms and the brain dynamics associated with creative design processes.

Creativity, one of the higher-order cognitive processes, is defined as the ability to develop useful, novel, and surprising ideas ( Sternberg and Lubart, 1998 ; Boden, 2004 ; Runco and Jaeger, 2012 ; Simonton, 2012 ). Needless to say, creativity occurs in all parts of social and personal life and all situations and places, including everyday cleverness, the arts, sciences, business, social interaction, and education ( Mokyr, 1990 ; Cropley, 2015b ). However, this study particularly focuses on reviewing EEG-based studies of creativity and design creativity tasks.

Design, as a fundamental and widespread human activity, aiming at changing existing situations into desired ones ( Simon, 1996 ), is nonlinear and complex ( Zeng, 2001 ), and lies at the heart of creativity ( Guilford, 1959 ; Gero, 1996 ; Jung and Vartanian, 2018 ; Xie, 2023 ). According to the recursive logic of design ( Zeng and Cheng, 1991 ), a designer intensively interacts with the design problem, design environment (including stakeholders of design, design context, and design knowledge), and design solutions in the recursive environment-based design evolution process ( Zeng and Gu, 1999 ; Zeng, 2004 , 2015 ; Nagai and Gero, 2012 ). Zeng (2002) conceptualized the design process as an environment-changing process in which the product emerges from the environment, serves the environment, and changes the environment ( Zeng, 2015 ). Convergent and divergent thinking are two primary modes of thinking in the design process, which are involved in analytical, critical, and synthetic processes. Divergent thinking leads to possible solutions, some of which might be creative, to the design problem whereas convergent thinking will evaluate and filter the divergent solutions to choose appropriate and practical ones ( Pahl et al., 1988 ).

Creative design is inherently unpredictable; at times, it may seem implausible – yet it happens. Some argue that a good design process and methodology form the foundation of creative design, while others emphasize the significance of both design methodology and knowledge in fostering creativity. It is noteworthy that different designers may propose varied solutions to the same design problem, and even the same designer might generate diverse design solutions for the same problem over time ( Zeng, 2001 ; Boden, 2004 ). Creativity may spontaneously emerge even if one does not intend to conduct a creative design, whereas creative design just may not come out no matter how hard one tries. A design is considered routine if it operates within a design space of known and ordinary designs, innovative if it navigates within a defined state space of potential designs but yields different outcomes, and creative if it introduces new variables and structures into the space of potential designs ( Gero, 1990 ). Moreover, it is conceivable that a designer may lack creativity while the product itself demonstrates creative attributes, and conversely, a designer may exhibit creativity while the resulting product does not ( Yang et al., 2022 ).

Several models of design creativity have been proposed in the literature. In some earlier studies, design creativity was addressed as engineering creativity or creative problem-solving ( Cropley, 2015b ). Used in recent studies ( Jia et al., 2021 ; Jia and Zeng, 2021 ), the stages of design creativity include problem understanding, idea generation, idea evolution, and idea validation ( Guilford, 1959 ). Problem understanding and idea evaluation are assumed to be convergent cognitive tasks whereas idea generation and idea evolution are considered divergent tasks in design creativity. An earlier model of creative thinking proposed by Wallas (1926) is presented in four phases including preparation, incubation, illumination, and verification ( Cropley, 2015b ). The “Preparation” phase involves understanding a topic and defining the problem. During “Incubation,” one processes the information, usually subconsciously. In the “Illumination” phase, a solution appears, often unexpectedly. Lastly, “Verification” involves evaluating and implementing the derived solution. In addition to this model, a seven-phase model (an extended version of the 4-phase model) was later introduced containing preparation, activation, generation, illumination, verification, communication, and validation ( Cropley, 2015a , b ). It is crucial to emphasize that these phases are not strictly sequential or distinct in that interactions, setbacks, restarts, or premature conclusions might occur ( Haner, 2005 ). In contrast to those emperical models of creativity, the nonlinear recursive logic of design creativity was rigorously formalized in a mathematical design creativity theory ( Zeng, 2001 ; Zeng et al., 2004 ; Zeng and Yao, 2009 ; Nguyen and Zeng, 2012 ). For further details on the theories and models of creativity and design creativity, readers are directed to the referenced literature ( Gero, 1994 , 2011 ; Kaufman and Sternberg, 2010 ; Williams et al., 2011 ; Nagai and Gero, 2012 ; Cropley (2015b) ; Jung and Vartanian, 2018 ; Yang et al., 2022 ; Xie, 2023 ).

1.2 Design creativity neurocognition

First, we would like to provide the definitions of “design” and “creativity” which can be integrated into the definition of “design creativity.” According to the Cambridge Dictionary, the definition of design is: “to make or draw plans for something.” In addition, the definition of creativity is: “the ability to make something new or imaginative.” So, the definition of design creativity is: “the ability to design something new and valuable.” With these definitions, we focus on design creativity neurocognition in this section.

It is of great importance to study design creativity neurocognition as the brain plays a pivotal role in the cognitive processes underlying design creativity tasks. So, to better investigate design creativity we need to concentrate on brain mechanisms associated with the related cognitive processes. However, the complexity of these tasks has led to a significant gap in our understanding; consequently, our knowledge about the neural activities associated with design creativity remains largely limited and unexplored. To address this gap, a burgeoning field known as design creativity neurocognition has emerged. This field focuses on investigating the intricate and unstructured brain dynamics involved in design creativity using various neuroimaging tools such as electroencephalography (EEG).

In a nonlinear evolutionary model of design creativity, it is suggested that the brain handles problems and ideas in a way that leads to unpredictable and potentially creative solutions ( Zeng, 2001 ; Nguyen and Zeng, 2012 ). This involves cognitive processes like thinking of ideas, evolving and evaluating them, along with physical actions like drawing ( Zeng et al., 2004 ; Jia, 2021 ). This indicates that the brain, as a complex and nonlinear system with characteristics like emergence and self-organization, goes through several cognitive processes which enable the generation of creative ideas and solutions. Exploring brain activities during design creativity tasks helps us get a better insight into the design process and improves how designers perform. As a result, design neurocognition combines traditional design study methods with approaches from cognitive neuroscience, neurophysiology, and artificial intelligence, offering unique perspectives on understanding design thinking ( Balters et al., 2023 ). Although several studies have focused on design and creativity, brain dynamics associated with design creativity are largely untouched. It motivated us to conduct this literature review to explore the studies, gather the information and findings, and finally discuss them. Due to the advantages of electroencephalography (EEG) in design creativity experiments which will be explained in the following paragraphs, we decided to focus on EEG-based neurocognition in design creativity.

As mentioned before, design creativity tasks are cognitive activities which are complex, dynamic, nonlinear, self-organized, and emergent. The brain dynamics of design creativity are largely unknown. Brain behavior recognition during design-oriented tasks helps scientists investigate neural mechanisms, vividly understand design tasks, enhance whole design processes, and better help designers ( Nguyen and Zeng, 2014a , b , 2017 ; Liu et al., 2016 ; Nguyen et al., 2018 , 2019 ; Zhao et al., 2018 , 2020 ; Jia, 2021 ; Jia et al., 2021 ; Jia and Zeng, 2021 ). Exploring brain neural circuits in design-related processes has recently gained considerable attention in different fields of science. Several studies have been conducted to decode brain activity in different steps of design creativity ( Petsche et al., 1997 ; Nguyen and Zeng, 2010 , 2014a , b , 2017 ; Liu et al., 2016 ; Nguyen et al., 2018 ; Vieira et al., 2019 ). Such attempts will lead to investigating the mechanism and nature of the design creativity process and consequently enhance designers’ performance ( Balters et al., 2023 ). The main question of the studies performed in design creativity neurocognition is whether and how we can explore brain dynamics and infer designers’ cognitive states using neuro-cognitive and physiological data like EEG signals.

Neuroimaging is a vital tool in understanding the brain’s structure and function, offering insights into various neurological and psychological conditions. It employs a range of techniques to visualize the brain’s activity and structure. Neuroimaging methods mainly include magnetic resonance imaging (MRI), computed tomography (CT), electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), functional MRI (fMRI), and magnetoencephalography (MEG). Neuroimaging techniques have helped researchers explore brain dynamics in complex cognitive tasks, one of which is design creativity ( Nguyen and Zeng, 2014b ; Gao et al., 2017 ; Zhao et al., 2020 ). While several neuroimaging methods exist to study brain activity, electroencephalography (EEG) is one of the best methods which has been widely used in several studies in different applications. EEG, as an inexpensive and simple neuroimaging technique with a high temporal resolution and an acceptable spatial resolution, has been used to infer designers’ cognitive and emotional states. Zangeneh Soroush et al. (2023a , b) have recently introduced two comprehensive datasets encompassing EEG recordings in design and creativity experiments, stemmed from several EEG-based design and design creativity studies ( Nguyen and Zeng, 2014a ; Nguyen et al., 2018 , 2019 ; Jia, 2021 ; Jia et al., 2021 ; Jia and Zeng, 2021 ). In this paper, we review some of the most fundamental studies which have employed electroencephalography (EEG) to explore brain behavior in creativity and design creativity tasks.

1.3 EEG approach to studying creativity neurocognition

EEG stands out as a highly promising method for investigating brain dynamics across various fields, including cognitive, clinical, and computational neuroscience studies. In the context of design creativity, EEG offers a valuable means to explore brain activity, particularly considering the physical movements inherent in the design process. However, EEG analysis poses challenges due to its complexity, nonlinearity, and susceptibility to various artifacts. Therefore, gaining a comprehensive understanding of EEG and mastering its utilization and processing is crucial for conducting effective experiments in design creativity research. This review aims to examine studies that have utilized EEG in investigating design creativity tasks.

EEG is a technique for recording the electrical activity of the brain, primarily generated by neuronal firing within the human brain. This activity is almost always captured non-invasively from the scalp in most cognitive studies, though intracranial EEG (iEEG) is recorded inside the skull, for instance in surgical planning for epilepsy. EEG signals are the result of voltage differences measured across two points on the scalp, reflecting the summed synchronized synaptic activities of large populations of cortical neurons, predominantly from pyramidal cells ( Teplan, 2002 ; Sanei and Chambers, 2013 ).

While the spatial resolution of EEG is relatively poor, EEG offers excellent temporal resolution, capturing neuronal dynamics within milliseconds, a feature not matched by other neuroimaging modalities like functional Near-Infrared Spectroscopy (fNIRS), Positron Emission Tomography (PET), or functional Magnetic Resonance Imaging (fMRI).

In contrast, fMRI provides much higher spatial resolution, offering detailed images of brain activity by measuring blood flow changes associated with neuronal activity. However, fMRI’s temporal resolution is lower than EEG, as hemodynamic responses are slower than electrical activities. PET, like fMRI, offers high spatial resolution and involves tracking a radioactive tracer injected into the bloodstream to image metabolic processes in the brain. It is particularly useful for observing brain metabolism and neurochemical changes but is invasive and has limited temporal resolution. fNIRS, measuring hemodynamic responses in the brain via near-infrared light, stands between EEG and fMRI in terms of spatial resolution. It is non-invasive and offers better temporal resolution than fMRI but is less sensitive to deep brain structures compared to fMRI and PET. Each of these techniques, with their unique strengths and limitations, provides complementary insights into brain function ( Baillet et al., 2001 ; Sanei and Chambers, 2013 ; Choi and Kim, 2018 ; Peng, 2019 ).

This understanding of EEG, from its historical development by Hans Berger in 1924 to modern digital recording and analysis techniques, underscores its significance in studying brain function and diagnosing neurological conditions. Despite advancements in technology, the fundamental methods of EEG recording have remained largely unchanged, emphasizing its enduring relevance in neuroscience ( Teplan, 2002 ; Choi and Kim, 2018 ).

1.4 Objectives and structure of the paper

Balters et al. (2023) conducted a comprehensive systematic review including 82 papers on design neurocognition covering nine topics and a large variety of methodological approaches in design neurocognition. A systematic review ( Pidgeon et al., 2016 ), reported several EEG-based studies on functional neuroimaging of visual creativity. Although such a comprehensive review exists in the field of design neurocognition, just a few early reviews focused on creativity neurocognition ( Fink and Benedek, 2014 , 2021 ; Benedek and Fink, 2019 ).

The present review not only reports the studies but also critically discusses the previous findings and results. The rest of this paper is organized as follows: Section 2 introduces our review methodology; Section 3 presents the results from our review process, and Section 4 discusses the major implications of the existing design creativity neurocognition research in future studies. Section 5 concludes the paper.

2 Methods and materials

Figure 1 shows the main components of EEG-based design creativity studies: (1) experiment design, (2) participants, (3) psychometric tests, (4) experiments (creativity tasks), (5) EEG recording and analysis methods, and (6) final data analysis. The experiment design consists of experiment protocol which includes (design) creativity tasks, the criteria to choose participants, the conditions of the experiment, and recorded physiological responses (which is EEG here). Setting and adjusting these components play a crucial role in successful experiments and reliable results. In this paper, we review studies based on the components in Figure 1 .

Figure 1 . The main components of EEG-based design creativity studies.

The components described in Figure 1 are consistent with the stress-effort model proposed by Nguyan and Zeng ( Nguyen and Zeng, 2012 ; Zhao et al., 2018 ; Yang et al., 2021 ) which characterizes the relationship between mental stress and mental effort by a bell-shaped curve. This model defines mental stress as a ratio of the perceived task workload over the mental capability constituted by affect, skills, and knowledge. Knowledge is shaped by individual experience and understanding related to the given task workload. Skills encompass thinking styles, strategies, and reasoning ability. The degree of affect in response to a task workload can influence the effective utilization of the skills and knowledge. We thus used this model to form our research questions, determine the keywords, and conduct our analysis and discussions.

2.1 Research questions

We focused on the studies assessing brain function in design creativity experiments through EEG analysis. For a comprehensive review, we followed a thorough search strategy, called thematic analysis ( Braun and Clarke, 2012 ), which helped us to code and extract themes from the initial (seed) papers. We began without a fixed topic, immersing ourselves in the existing literature to shape our research questions, keywords, and search queries. Our research questions formed the search keywords and later the search inquiries.

Our main research questions (RQs) were:

RQ1: What are the effective experiment design and protocol to ensure high-quality EEG-based design creativity studies?

RQ2: How can we efficiently record, preprocess, and process EEG reflecting the cognitive workload associated with design creativity tasks?

RQ3: What are the existing methods to analyze the data extracted from EEG signals recorded during design creativity tasks?

RQ4: How can EEG signals provide significant insight into neural circuits and brain dynamics associated with design creativity tasks?

RQ5: What are the significant neuroscientific findings, shortcomings, and inconsistencies in the literature?

With the initial information extracted from the seed papers and the previous studies by the authors in this field ( Nguyen and Zeng, 2012 , 2014a , b ; Jia et al., 2021 ; Jia and Zeng, 2021 ; Yang et al., 2022 ; Zangeneh Soroush et al., 2024 ), we built a conceptual model represented by Figure 1 and then formed these research questions. With this understanding and the RQs, we set our search strategy.

2.2 Search strategy and inclusion-exclusion criteria

Our search started with broad terms like “design,” “creativity,” and “EEG.” These terms encapsulate the overarching cognitive activities and physiological measurement. As we identified relevant papers, we refined our search keywords for a more targeted search. We utilized the Boolean operators such as “OR” and “AND” to finetune our search inquiries. The search inquiries were enhanced by the authors through the knowledge they obtained through selected papers. The first phase started with thematic analysis and continued with choosing papers, obtaining knowledge, discussing the keywords, and updating the search inquiries, recursively until reaching an appropriate search inquiry which resulted in the desired search results. We applied the thematic analysis only in the first iteration to make sure that we had the right and comprehensive understanding of EEG-based design creativity, the appropriate set of keywords, and search inquiries. Finally, we came up with a comprehensive search inquiry as follows:

(“EEG” OR “Electroenceph*” OR “brain” OR “neur*” OR “neural correlates” OR “cognit*”) AND (“design creativity” OR “ideation” OR “creative” OR “divergent thinking” OR “convergent thinking” OR “design neurocognition” OR “creativity” OR “creative design” OR “design thinking” OR “design cognition” OR “creation”)

The search inquiry is a combination of terminologies related to design and creativity, as well as terminologies about EEG, neural activity, and the brain. In a general and quick evaluation, we found out that our proposed search inquiry resulted in relevant studies in the field. This evaluation was a quick way to check how effectively our search keywords work. Then, we went through well-known databases such as PubMed, Scopus, and Web of Science to collect a comprehensive set of original papers, theses, and reviews. These electronic databases were searched to reduce the risk of bias, to get more accurate findings, and to provide coverage of the literature. We continued our search in the aforementioned databases until no more significant papers emerged from those specific databases. It is worth mentioning that we do not consider any specific time interval in our search procedure. We used the fields “title,” “abstract,” and “keywords” in our search process. Then, we selected the papers based on the following inclusion criteria:

1. The paper should answer one or more research questions (RQ1-RQ5).

2. The paper must be a peer-reviewed journal paper authored in English.

3. The paper should focus on EEG analysis related to creativity or design creativity for adult participants.

4. The paper should be related to creativity or design creativity in terms of the concepts, experiments, protocols, and probable models employed in the studies.

5. The paper should use established creativity tasks, including the Alternative Uses Task (AUT), the Torrance Tests of Creative Thinking (TTCT), or a specific design task. (These tasks will be detailed further on.)

6. The paper should include a quantitative analysis of EEG signals in the creativity or design creativity domain.

7. In addition to the above-mentioned criteria, the authors checked the papers to make sure that the included publications have the characteristics of high-quality papers.

These criteria were used to select our initial papers from the large set of papers we collected from Scopus, Web of Science, and PubMed. It should be mentioned that conflicts were resolved through discussion and duplicate papers were removed.

After our initial selection, we used Google Scholar to perform a forward and backward snowball search approach. We chose the snowball search method over the systematic review approach as the forward and backward snowball search methodologies offer efficient alternatives to a systematic review. Unlike systematic reviews, the snowball search method is particularly valuable when dealing with emerging fields or when the scope of inquiry is evolving, allowing researchers to quickly uncover pertinent insights and form connections between seminal and contemporary works. During each iteration of the snowball search, we applied the aforementioned criteria to include or exclude papers accordingly. We continued our snowball search procedure until it converged to the final set of papers. After repeating this over six iterations, we found no new and significant papers, suggesting we had reached a convergent set of papers.

By October 1 st (2023), our search was complete. We then organized and studied the final included publications.

3.1 Search results

Figure 2 illustrates the general flow of our search procedure, adapted from PRISMA guidelines ( Liberati et al., 2009 ). With the search keywords, we identified 1878 studies during the thematic analysis phase. We considered these studies to select the seed papers for the further snowball search process. After performing the snowball search and considering inclusion and exclusion criteria, we finally selected 154 studies including 82 studies related to creativity (comprising 60 original papers, 12 theses, and 10 review papers) and 72 studies related to design creativity (comprising 63 original papers, 5 theses, and 4 review papers). In our search, we also found 6 related textbooks and 157 studies using other modalities (such as fMRI, fNIRS, etc.) which were excluded. We used these textbooks, theses, and their resources to gain more knowledge in the initial steps of our review. Some studies using fMRI and fNIRS were used to evaluate the results in the discussion. In the snowball search process, a large number of studies have consistently appeared across all iterations implying their high relevance and influence in the field. These papers, which have been repeatedly selected throughout the search process, demonstrate their significant contributions to the understanding of design creativity and EEG studies. The snowball process effectively identifies such pivotal studies by highlighting their recurrent presence and citation in the literature, underscoring their importance in shaping the research landscape.

Figure 2 . Search procedure and results (adopted from PRISMA) using the thematic analysis in the first iteration and snowball search in the following iterations.

3.2 Design creativity neurocognition: history and trend

As discussed in Section 1, creativity and design creativity studies are different yet closely related in that design creativity involves a more complex design process. In this subsection, we will look at how the design neurocognition creativity study followed the creativity neurocognition study (though not necessarily in a causal manner).

3.2.1 History of creativity neurocognition

Three early studies in the field of creativity neurocognition are Martindale and Mines (1975) , Martindale and Hasenfus (1978) , and Martindale et al. (1984) . In the first study ( Martindale and Mines, 1975 ), it is stated that creative individuals may exhibit certain traits linked to lower cortical activation. This research has shown distinct neural activities when participants engage in two creativity tasks: the Alternate Uses Tasks (AUT) and the Remote Associate Task (RAT). The AUT, which gauges ideational fluency and involves unfocused attention, is related to higher alpha power in the brain. Conversely, the RAT, which centers on producing specific answers, shows varied alpha levels. Previous psychological research aligns with these findings, emphasizing the different nature of these tasks. Creativity, as determined by both tests, is associated with high alpha percentages during the AUT, hinting at an association between creativity and reduced cortical activation during creative tasks. However, highly creative individuals also show a mild deficit in cortical self-control, evident in their increased alpha levels, even when trying to suppress them. This behavior mirrors findings from earlier and later studies and implies that these individuals might have a predisposition to disinhibition. The varying alpha levels during cognitive tasks likely stem from their reaction to tasks rather than intentional focus shifts ( Martindale and Mines, 1975 ).

In the second study ( Martindale and Hasenfus, 1978 ), the authors explored the relationship between creativity and EEG alpha band presence during different stages of the creative process. There were two experiments in this study. Experiment 1 found that highly creative individuals had lower alpha wave presence during the elaboration stage of the creative process, while Experiment 2 found that effort to be original during the inspiration stage was associated with higher alpha wave presence. Overall, the findings suggest that creativity is associated with changes in EEG alpha wave presence during different stages of the creative process. However, the relationship is complex and may depend on factors such as effort to be original and the specific stage of the creative process.

Finally, a series of three studies indicated a link between creativity and hemispheric asymmetry during creative tasks ( Martindale et al., 1984 ). Creative individuals typically exhibited heightened right-hemisphere activity compared to the left during creative output. However, no distinct correlation was found between creativity and varying levels of hemispheric asymmetry during the inspiration versus elaboration phases. The findings suggest that this relationship is consistent across different stages of creative production. These findings were the foundation of design creativity studies which were more explored later and confirmed by other studies ( Petsche et al., 1997 ). Later studies have used these findings to validate their results. In addition to these early studies, there exist several reviews such as Fink and Benedek (2014) , Pidgeon et al. (2016) , and Rominger et al. (2022a) which provide a comprehensive literature review of previous studies and their main findings including early studies as well as recent creativity research.

3.2.2 EEG-based creativity studies

In the preceding sections, we aimed to lay a foundational understanding of neurocognition in creativity, equipping readers with essential knowledge for the subsequent content. In this subsection, we will briefly introduce the main and most important points regarding creativity experiments. More detailed information can be found in Simonton (2000) , Srinivasan (2007) , Arden et al. (2010) , Fink and Benedek (2014) , Pidgeon et al. (2016) , Lazar (2018) , and Hu and Shepley (2022) .

This section presents key details from the selected studies in a structured format to facilitate easy understanding and comparison for readers. As outlined earlier, crucial elements in creativity research include the participants, psychometric tests used, creativity tasks, EEG recording and analysis techniques, and the methods of final data analysis. We have organized these factors, along with the principal findings of each study, into Table 1 . This approach allows readers to quickly grasp the essential information and compare various aspects of different studies. The table format not only aids in presenting data clearly and concisely but also helps in highlighting similarities and differences across studies, providing a comprehensive overview of the field. Following the table, we have included a discussion section. This discussion synthesizes the information from the table, offering insights and interpretations of the trends, implications, and significance of these studies in the broader context of creativity neurocognition. This structured presentation of studies, followed by a detailed discussion, is designed to enhance the reader’s understanding, and provide a solid foundation for future research in this dynamic and evolving field.

Table 1 . A summary of EEG-based creativity neurocognition studies.

In our research, we initially conducted a thematic analysis and utilized a forward and backward snowball search method to select relevant studies. Out of these, five studies employed machine learning techniques, while the remaining ones concentrated on statistically analyzing EEG features. It is noteworthy that all the chosen studies followed a similar methodology, involving the recruitment of participants, administering probable psychometric tests, conducting creativity tasks, recording EEG data, and concluding with final data analysis.

While most studies follow similar structure for their experiments, some other studies focus on other aspects of creativity such as artistic creativity and poetry, targeting different evaluation methods, and through different approaches. In Shemyakina and Dan’ko (2004) and Danko et al. (2009) , the authors targeted creativity to produce proverbs or definitions of emotions of notions. In other studies ( Leikin, 2013 ; Hetzroni et al., 2019 ), the experiments are focused on creativity and problem-solving in autism and bilingualism. Moreover, some studies such as Volf and Razumnikova (1999) and Razumnikova (2004) focus more on the gender differences in brain organization during creativity tasks. In another study ( Petsche, 1996 ), approaches to verbal, visual, and musical creativity were explored through EEG coherence analysis. Similarly, the study ( Bhattacharya and Petsche, 2005 ) analyzed brain dynamics in mentally composing drawings through differences in cortical integration patterns between artists and non-artists. We summarized the findings of EEG-based creativity studies in Table 1 .

3.2.3 Neurocognitive studies of design and design creativity

Design is closely associated with creativity. On the one hand, by definition, creativity is a measure of the process of creating, for which design, either intentional or unconscious, is an indispensable constituent. On the other hand, it is important to note that not all designs are inherently creative; many designs follow established patterns and resemble existing ones, differing only in their specific context. Early research on design creativity aimed to differentiate between design and design creativity tasks by examining when and how designers exhibited creativity in their work. In recent years, much of the focus in design creativity research has shifted towards cognitive and neurocognitive investigations, as well as the development of computational models to simulate creative processes ( Borgianni and Maccioni, 2020 ; Lloyd-Cox et al., 2022 ). Neurocognitive studies employ neuroimaging methods (such as EEG) while computational models often leverage artificial intelligence or cognitive modeling techniques ( Zeng and Yao, 2009 ; Gero, 2020 ; Gero and Milovanovic, 2020 ). In this section, we review significant EEG-based studies in design creativity to focus more on design creation and highlight the differences. While most studies have processed EEG to provide more detailed insight into brain dynamics, some studies such as Goel (2014) outlined a preliminary framework encompassing cognitive and neuropsychological systems essential for explaining creativity in designing artifacts.

Several studies have recorded and analyzed EEG in design and design creativity tasks. Most neuro-cognitive studies have directly or indirectly employed frequency-based analysis which is based on the analysis of EEG in specific frequency bands including delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), and gamma (>30 Hz). One of the main analyses is called task-related potential (TRP) which has provided a foundation for other analyses. It computes the relative power of the EEG signal associated with a design task in a specific frequency band with respect to the power of EEG in the rest mode. This analysis is simple and effective and reveals the physiological processes underlying EEG dynamics ( Rominger et al., 2018 ; Jia and Zeng, 2021 ; Gubler et al., 2022 ; Rominger et al., 2022b ).

Frequency-based analyses have been widely employed. For example, the study ( Borgianni and Maccioni, 2020 ) applied TRP analysis to compare the neurophysiological activations of mechanical engineers and industrial designers while conducting design tasks including problem-solving, basic design, and open design. These studies have agreed that higher alpha band activity is sensitive to specific task-related requirements, while the lower alpha corresponds to attention processes such as vigilance and alertness ( Klimesch et al., 1998 ; Klimesch, 1999 ; Chrysikou and Gero, 2020 ). Higher alpha activity in the prefrontal region reflects complex cognitive processes, higher internal attention (such as in imagination), and task-irrelevant inhibition ( Fink et al., 2009a , b ; Fink and Benedek, 2014 ). On the other hand, higher alpha activity in the occipital and temporal lobes corresponds to visualization processes ( Vieira et al., 2022a ). In design research, to compare EEG characteristics in design activities (such as idea generation or evaluation) ( Liu et al., 2016 ), frequency-based analysis has been widely employed ( Liu et al., 2018 ). Higher alpha is associated with open-ended tasks, visual association in expert designers, and divergent thinking ( Nguyen and Zeng, 2014b ; Nguyen et al., 2019 ). Higher beta and theta play a pivotal role in convergent thinking, and constraint tasks ( Nguyen and Zeng, 2010 ; Liu et al., 2016 ; Liang and Liu, 2019 ).

The research in design and design creativity is not limited to frequency-based analyses. Nguyen et al. (2019) introduced Microstate analysis to EEG-based design studies. Using the microstate analysis, Jia and Zeng investigated EEG characteristics in design creativity experiment ( Jia and Zeng, 2021 ), where EEG signals were recorded while participants conducted design creativity experiments which were modified TTCT tasks ( Nguyen and Zeng, 2014b ).

Following the same approach, Jia et al. (2021) analyzed EEG microstates to decode brain dynamics in design cognitive states including problem understanding, idea generation, rating idea generation, idea evaluation, and rating idea evaluation, where six design problems including designing a birthday cake, a toothbrush, a recycle bin, a drinking fountain, a workplace, and a wheelchair were used for the EEG based design experimental studies ( Nguyen and Zeng, 2017 ). The data of these two loosely controlled EEG-based design experiments are summarized and available for the research community ( Zangeneh Soroush et al., 2024 ).

We summarized the findings of EEG-based design and design creativity studies in Table 2 .

Table 2 . A summary of EEG-based design creativity neurocognition studies.

3.2.4 Trend analysis

The selected studies span a broad range of years, stretching from 1975 ( Martindale and Mines, 1975 ) to the present day, reflecting advancements in neuro-imaging techniques and machine learning methods that have significantly aided researchers in their investigations. From the earliest studies to more recent ones, the primary focus has centered on EEG sub-bands, brain asymmetry, coherence analysis, and brain topography. Recently, machine learning methods have been employed to classify EEG samples into designers’ cognitive states. These studies can be roughly classified into the following distinct categories based on their proposed experiments and EEG analysis methods ( Pidgeon et al., 2016 ; Jia, 2021 ): (1) visual creativity versus baseline rest/fixation, (2) visual creativity versus non-rest control task(s), (3) individuals of high versus low creativity, (4) generation of original versus standard visual images, (5) creativity in virtual reality vs. real environment, (6) loosely controlled vs. strictly controlled creativity experiments.

The included studies exhibited considerable variation in the tasks utilized and the primary contrasts examined. Some studies employed frequency-based or EEG power analysis to compare brain activity during visual creativity tasks with tasks involving verbal creativity or both verbal and visual tasks. These tasks often entail memory tasks or tasks focused on convergent thinking. Several studies, however, adopted a simpler approach by comparing electrophysiological activity during visual creativity tasks against a baseline fixation or rest condition. Some studies compared neural activities between individuals characterized by high and low levels of creativity, while others compared the generation of original creative images with that of standard creative images. Several studies analyze brain behavior concerning creativity factors such as fluency, originality, and others. These studies typically employ statistical analysis techniques to illustrate and elucidate differences between various creativity factors and their corresponding brain behaviors. This variability underscores the diverse approaches taken by researchers to examine the neural correlates of design creativity ( Pidgeon et al., 2016 ). However, few studies significantly and deeply delved into areas such as gender differences in creativity, creativity among individuals with mental or physical disorders, or creativity in diverse job positions or skill sets. This suggests that there is significant untapped potential within the EEG-based design creativity research area.

In recent years, advancements in fMRI imaging and its applications have led several studies to replace EEG with fMRI to investigate brain behavior. fMRI extracts metabolism, resulting in relatively high spatial resolution compared to EEG. However, it is important to note that fMRI has lower temporal resolution compared to EEG. Despite this difference, the shift towards fMRI highlights the ongoing evolution and exploration of neuroimaging techniques in understanding the neural correlates of design creativity. fMRI studies provide a deep understanding of neural circuits associated with creativity and can be used to evaluate EEG-based studies ( Abraham et al., 2018 ; Japardi et al., 2018 ; Zhuang et al., 2021 ).

The emergence of virtual reality (VR) has had a significant impact on design creativity studies, offering a wide range of experimentation possibilities. VR enables researchers to create diverse scenarios and creativity tasks, providing a dynamic and immersive environment for participants ( Agnoli et al., 2021 ; Chang et al., 2022 ). Through VR technology, various design creativity experiments can be conducted, allowing for novel approaches and innovative methodologies to explore the creative process. This advancement opens up new avenues for researchers to investigate the complexities of design creativity more interactively and engagingly.

Regardless of the significant progress over the past few decades, design and design creativity neurocognitive research is still in its early stages, due to the challenges identified ( Zhao et al., 2020 ; Jia et al., 2021 ), which is summarized below:

1. Design tasks are open-ended, meaning there is no single correct outcome and countless acceptable solutions are possible. There are no predetermined or optimal design solutions; multiple feasible solutions may exist for an open-ended design task.

2. Design tasks are ill-defined as finding a solution might change or redefine the original task, leading to new tasks emerging.

3. Various emergent design tasks trigger design knowledge and solutions, which in turn can change or redefine tasks further.

4. The process of completing a design task depends on emerging tasks and the perceived priorities for completion.

5. The criteria to evaluate a design solution are set by the solution itself.

While a lot of lessons learned from creativity neurocognitive research can be borrowed to study design and design creativity neurocognition, new paradigms should be proposed, tested, and validated to advance this new discipline. This advancement will in turn move forward creativity neurocognition research.

3.3 Experiment protocol

Concerning the model described in Figure 1 , we arranged the following sections to cover all the main components of EEG-based design creativity studies. To bring a general picture of the EEG-based design creativity studies, we briefly explain the procedure of such experiments. Since most design creativity neurocognition research inherited more or less procedures in general creativity research, we will focus on AUT and TTCT tasks. The introduction of a loosely controlled paradigm, tEEG, can be found in Zhao et al. (2020) , Jia et al. (2021) , and Jia and Zeng (2021) . Taking a look at Tables 1 , 2 , it can be inferred that almost all included studies record EEG signals while selected participants are performing creativity tasks. The first step is determining the sample size, recruiting participants, and psychometrics according to which participants get selected. In some of these studies, participants take psychometric tests before performing the creativity tasks for screening or categorization. In this review, the tasks used to gauge creativity are the Alternative Uses Test (AUT) and the Torrance Test of Creative Thinking (TTCT). During these tasks, EEG is recorded and then preprocessed to remove any probable artifacts. These artifact-free EEGs are then processed to extract specific features, which are subsequently subjected to either statistical analysis or machine learning methods. Statistical analysis typically compares brain dynamics across different creativity tasks like idea generation, idea evolution, and idea evaluation. Machine learning, on the other hand, categorizes EEG signals based on associated creativity tasks. The final stage involves data analysis, which aims to deduce how brain dynamics correlate with the creativity tasks given to participants. This data analysis also compares EEG results with psychometric test findings to discern any significant differences in EEG dynamics and neural activity between groups.

3.3.1 Participants

The first factor of the studies is their participants. In most studies, participants are right-handed, non-medicated, and have normal or corrected to normal vision. In some cases, the Edinburgh Handedness Inventory ( Oldfield, 1971 ) (with 11 elements) or hand dominance test (HDT) ( Steingrüber et al., 1971 ) were employed to determine participants’ handedness ( Rominger et al., 2020 ; Gubler et al., 2023 ; Mazza et al., 2023 ). While in several creativity studies, right-handedness has been considered; relatively, in design creativity studies it has been less mentioned.

In most studies, participants are undergraduate or graduate students with different educational backgrounds and an age range of 18 to 30 years. In the included papers, participants did not report any history of psychiatric or neurological disorders, or treatment. It should be noted that some studies such as Ayoobi et al. (2022) and Gubler et al. (2022) analyzed creativity in health conditions like multiple sclerosis or participants with chronic pain, respectively. These studies usually conduct statistical analysis to investigate the results of creativity tasks such as AUT or Remote Association Task (RAT) and then associate the results with the health condition. In some studies, it is reported that participants were asked not to smoke cigarettes for 1 h, not to have coffee for 2 h, alcohol for 12 h, or other stimulating beverages for several hours before experiments. As mentioned in some design creativity studies, similar rules apply to design creativity experiments (participants are not allowed to have stimulating beverages).

In most studies, the sample size of participants was as large as 15 up to 45 participants except for a few studies ( Jauk et al., 2012 ; Perchtold-Stefan et al., 2020 ; Rominger et al., 2022a , b ) which had larger numbers such as 100, 55, 93, and 74 participants, respectively. Some studies such as Agnoli et al. (2020) and Rominger et al. (2020) calculated their required sample size through G*power software ( Faul et al., 2007 ) concerning their desirable chance (or power) of detecting a specific interaction effect involving the response, hemisphere, and position ( Agnoli et al., 2020 ). Considering design creativity studies, the same trend can be seen as the minimum and maximum numbers of participants are 8 and 84, respectively. Similarly, in a few studies, sample sizes were estimated through statistical methods such as G*power ( Giannopulu et al., 2022 ).

In most studies, a considerable number of participants were excluded due to several reasons such as not being fluent in the language used in the experiment, left-handedness, poor quality of recorded signals, extensive EEG artifacts, misunderstanding the procedure of the experiment correctly, technical errors, losing the data during the experiment, no variance in the ratings, and insufficient behavioral data. This shows that recording a high-quality dataset is quite challenging as several factors determine whether the quality is acceptable. Two datasets (in design and creativity) with public access have recently been published in Mendeley Data ( Zangeneh Soroush et al., 2023a , b ). Except for these two datasets, to the best of our knowledge, there is no publicly available dataset of EEG signals recorded in design and design creativity experiments.

Regarding the gender analysis, among the included papers, there were a few studies which directly focused on the association between gender, design creativity, and brain dynamics ( Vieira et al., 2021 , 2022a ). In addition, most of the included papers did not choose the participants’ gender to include or exclude them. In some cases, participants’ genders were not reported.

3.3.2 Psychometric tests

Before the EEG recording sessions, participants are often screened using psychometric tests, which are usually employed to categorize participants based on different aspects of intellectual abilities, ideational fluency, and cognitive development. These tests provide scores on various cognitive abilities. Additionally, personality tests are used to create personas for participants. Self-report questionnaires measure traits such as anxiety, mood, and depression. Some of the psychometric tests include the Intelligenz-Struktur-Test 2000-R (I-S-T 2000 R), which assesses general mental ability and specific intellectual abilities like visuospatial, numerical, and verbal abilities. The big five test is used for measuring personality traits like conscientiousness, extraversion, neuroticism, openness to experience, and agreeableness. Other tests such as Spielberger’s state–trait anxiety inventory (STAI) are used for mood and anxiety, while the Eysenck Personality Questionnaire (EPQ-R) investigates possible personality correlates of task performance ( Fink and Neubauer, 2006 , 2008 ; Fink et al., 2009a ; Jauk et al., 2012 ; Wang et al., 2019 ). To the best of our knowledge, the included design creativity studies have not directly utilized psychometrics ( Table 2 ) to explore the association between participants’ cognitive characteristics and brain behavior. There exist a few studies which have indirectly used cognitive characteristics. For instance, Eymann et al. (2022) assessed the shared mechanisms of creativity and intelligence in creative reasoning and their correlations with EEG characteristics.

3.3.3 Creativity and design creativity tasks

In this section, we introduce some experimental creativity tasks such as the Alternate Uses Task (AUT), and the Torrance Test of Creative Thinking (TTCT). Here, we would like to shed light on these tasks and their correlation with design creativity. One of the main characteristics of design creativity is divergent thinking as its first phase which is addressed by these two creativity tasks. In addition, AUT and TTCT are adopted and modified by several studies such as Hartog et al. (2020) , Hartog (2021) , Jia et al. (2021) , Jia and Zeng (2021) , and Li et al. (2021) for design creativity neurocognition studies. The figural version of TTCT is aligned with the goals of design creativity tasks where designers (specifically in engineering domains) create or draw their ideas, generate solutions, and evaluate and evolve generated solutions ( Srinivasan, 2007 ; Mayseless et al., 2014 ; Jia et al., 2021 ).

Furthermore, design creativity studies have introduced different types of design tasks from sequence of simple design problems to constrained and open design tasks ( Nguyen et al., 2018 ; Vieira et al., 2022a ). This variety of tasks opens new perspectives to the design creativity neurocognition studies. For example, the six design problems have been employed in some studies ( Nguyen and Zeng, 2014b ); ill-defined design tasks are used to explore brain dynamics differences between novice and expert designers ( Vieira et al., 2020d ).

The Alternate Uses Task (AUT), established by Guilford (1967) , is a prominent tool in psychological evaluations for assessing divergent thinking, an essential element of creativity. In AUT ( Guilford, 1967 ), participants are prompted to think of new and unconventional uses for everyday objects. Each object is usually shown twice – initially in the normal (common) condition and subsequently in the uncommon condition. In the common condition, participants are asked to consider regular, everyday uses for the objects. Conversely, in uncommon conditions, they are encouraged to come up with unique, inventive uses for the objects ( Stevens and Zabelina, 2020 ). The test includes several items for consideration, e.g., brick, foil, hanger, helmet, key, magnet, pencil, and pipe. In the uncommon condition, participants are asked to come up with as many uses as they can for everyday objects, such as shoes. It requires them to think beyond the typical uses (e.g., foot protection) and envision novel uses (e.g., a plant pot or ashtray). The responses in this classic task do not distinguish between the two key elements of creativity: originality (being novel and unique) and appropriateness (being relevant and meaningful) ( Runco and Mraz, 1992 ; Wang et al., 2017 ). For instance, when using a newspaper in the AUT, responses can vary from common uses like reading or wrapping to more inventive ones like creating a temporary umbrella. The AUT requires participants to generate multiple uses for everyday objects thereby measuring creativity through four main criteria: fluency (quantity of ideas), originality (uniqueness of ideas), flexibility (diversity of idea categories), and elaboration (detail in ideas) ( Cropley, 2000 ; Runco and Acar, 2012 ). In addition to the original indices of AUT, there are some creativity tests which include other indices such as fluency-valid and usefulness. Usefulness refers to how functional the ideas are ( Cropley, 2000 ; Runco and Acar, 2012 ) whereas fluency-valid, which only counts unique and non-repeated ideas, is defined as a valid number of ideas ( Prent and Smit, 2020 ). The AUT’s straightforward design and versatility make it a favored method for gauging creative capacity in diverse groups and settings, reflecting its universal applicability in creativity assessment ( Runco and Acar, 2012 ).

Developed by E. Paul Torrance in the late 1960s, the Torrance Test of Creative Thinking (TTCT) ( Torrance, 1966 ) is a foundational instrument for evaluating creative thinking. TTCT is recognized as a highly popular and extensively utilized tool for assessing creativity. Unlike the AUT, the TTCT is more structured and exists in two versions: verbal and figural. The verbal part of the TTCT, known as TTCT-Verbal, includes several subtests ( Almeida et al., 2008 ): (a) Asking Questions and Making Guesses (subtests 1, 2, and 3), where participants are required to pose questions and hypothesize about potential causes and effects; (b) Improvement of a Product (subtest 4), which involves suggesting modifications to the product; (c) Unusual Uses (subtest 5), where participants think of creative and atypical uses; and (d) Supposing (subtest 6), where participants imagine the outcomes of an unlikely event, as per Torrance. The figural component, TTCT-Figural, contains three tasks ( Almeida et al., 2008 ): (a) creating a drawing; (b) completing an unfinished drawing; and (c) developing a new drawing starting from parallel lines. An example of a figural TTCT task might involve uniquely finishing a partially drawn image, with evaluations based on the aforementioned criteria ( Rominger et al., 2018 ).

The TTCT includes a range of real-world reflective activities that encourage diverse thinking styles, essential for daily life and professional tasks. The TTCT assesses abilities in Questioning, Hypothesizing Causes and Effects, and Product Enhancement, each offering insights into an individual’s universal creative potential and originality ( Boden, 2004 ; Runco and Jaeger, 2012 ; Sternberg, 2020 ). It acts like a comprehensive test battery, evaluating multiple facets of creativity’s complex nature ( Guzik et al., 2023 ).

There are also other creativity tests such as Remote Associates Test (RAT), Runco Creativity Assessment Battery (rCAB), and Consensual Assessment Technique (CAT). TTCT is valued for its extensive historical database of human responses, which serves as a benchmark for comparison, owing to the consistent demographic profile of participants over many years and the systematic gathering of responses for evaluation ( Kaufman et al., 2008 ). The Alternate Uses Task (AUT) and the Remote Associates Test (RAT) are appreciated for their straightforward administration, scoring, and analysis. The Creative Achievement Test (CAT) is notable for its adaptability to specific fields, made possible by employing a panel of experts in relevant domains to assess creative works. Consequently, the CAT is particularly suited for evaluating creative outputs in historical contexts or significant “Big-C” creativity ( Kaufman et al., 2010 ). In contrast, the AUT and TTCT are more relevant for examining creativity in everyday, psychological, and professional contexts. As such, AUT and TTCT tests will establish a solid baseline for more complex design creativity studies employing more realistic design problems.

3.4 EEG recording and analysis: methods and algorithms

Electroencephalogram (EEG) signal analysis is a crucial component in the study of creativity whereby brain behavior associated with creativity tasks can be explored. Due to its advantages, EEG has emerged as one of the most suitable neuroimaging techniques for investigating brain activity during creativity tasks. Its affordability and suitability for studies involving physical movement, ease of recording and usage, and notably high temporal resolution make EEG a preferred choice in creativity research.

The dynamics during creative tasks are complex, nonlinear, and self-organized ( Nguyen and Zeng, 2012 ). It can thus be assumed that the brain could exhibits the similar characteristics, which shall be reflected in EEG signals. Capturing these complex and nonlinear patterns of brain behavior can be challenging for other neuroimaging methods ( Soroush et al., 2018 ).

3.4.1 Preprocessing: artifact removal

In design creativity studies utilizing EEG, the susceptibility of EEG signals to noise and artifacts is a significant concern due to the accompanying physical movements inherent in these tasks. Consequently, EEG preprocessing becomes indispensable in ensuring data quality and reliability. Unfortunately, not all the included studies in this review have clearly explained their pre-processing and artifact removal approaches. There also exist some well-known preprocessing pipelines such as HAPPE ( Gabard-Durnam et al., 2018 ) which (in contrast to their high efficiency) have been rarely used in design creativity neurocognition ( Jia et al., 2021 ; Jia and Zeng, 2021 ). The included papers in our analysis have introduced various preprocessing methods, including wavelet analysis, frequency-based filtering, and independent component analysis (ICA) ( Beaty et al., 2017 ; Fink et al., 2018 ; Lou et al., 2020 ). The primary objective of preprocessing remains consistent: to obtain high-quality EEG data devoid of noise or artifacts while minimizing information loss. Achieving this goal is crucial for the accurate interpretation and analysis of EEG signals in design creativity research.

3.4.2 Preprocessing: segmentation

Design creativity studies often encompass a multitude of cognitive tasks occurring simultaneously or sequentially, rendering them ill-defined and unstructured. This complexity leads to the generation of unstructured EEG data, posing a challenge for subsequent analysis ( Zhao et al., 2020 ). Therefore, segmentation methods play a crucial role in classifying recorded EEG signals into distinct cognitive tasks, such as idea generation, idea evolution, and idea evaluation.

Several segmentation methods have been adopted, including the ones relying on Task-Related Potential (TRP) analysis and microstate analysis, followed by clustering techniques like K-means clustering ( Nguyen and Zeng, 2014a ; Nguyen et al., 2019 ; Zhao et al., 2020 ; Jia et al., 2021 ; Jia and Zeng, 2021 ; Rominger et al., 2022b ). These methods aid in organizing EEG data into meaningful segments corresponding to different phases of the design creativity process, facilitating more targeted and insightful analysis. In addition, they provide possibilities to look into a more comprehensive list of design-related cognitions implied in but not intended by conventional AUT and TTCT experiments.

While there are some uniform segmentation methods (such as the ones based on TRP) employing frequency-based methods. Nguyen et al. (2019) introduced a fully automatic dynamic method based on microstate analysis. Since then, microstate analysis has been used in several studies to categorize the EEG dynamics in design creativity tasks ( Jia et al., 2021 ; Jia and Zeng, 2021 ). Microstate analysis provides a novel method for EEG-based design creativity studies with the capabilities of high temporal resolution and topography results ( Yuan et al., 2012 ; Custo et al., 2017 ; Jia et al., 2021 ; Jia and Zeng, 2021 ).

3.4.3 Feature extraction

The EEG data, after undergoing preprocessing, is directed to feature extraction, where relevant attributes are extracted to delve deeper into EEG dynamics and brain activity. These extracted features serve as the basis for conducting statistical analyses or employing machine learning algorithms.

In our review of the literature, we found that EEG frequency, time, and time-frequency analyses are the most commonly employed methods among the papers we considered. Specifically, the EEG alpha, beta, and gamma bands are often highlighted as critical indicators for studying brain dynamics in creativity and design creativity. Significant variations in the EEG bands have been observed during different stages of design creation tasks, including idea generation, idea evaluation, and idea elaboration ( Nguyen and Zeng, 2010 ; Liu et al., 2016 ; Rominger et al., 2019 ; Giannopulu et al., 2022 ; Lukačević et al., 2023 ; Mazza et al., 2023 ). For instance, the very first creativity studies used EEG alpha asymmetry to explore the relationship between creativity and left-hemisphere and right-hemisphere brain activity ( Martindale and Mines, 1975 ; Martindale and Hasenfus, 1978 ; Martindale et al., 1984 ). Other studies divided the EEG alpha band into lower (8–10 Hz) and upper alpha (10–13 Hz) and concluded that low alpha is more significant compared to the high EEG alpha band. Although the alpha band has been extensively explored by previous studies, several studies have also analyzed other EEG sub-bands such as beta, gamma, and delta and later concluded that these sub-bands are also significantly associated with creativity mechanisms, and can explain the differences between genders in different creativity experiments ( Razumnikova, 2004 ; Volf et al., 2010 ; Nair et al., 2020 ; Vieira et al., 2022a ).

Several studies have utilized Task-related power changes (TRP) to compare the EEG dynamics in different creativity tasks. TRP analysis is a high-temporal resolution method used to examine changes in brain activity associated with specific tasks or cognitive processes. In TRP analysis, the power of EEG signals, typically measured in terms of frequency bands (like alpha, beta, theta, etc.), is analyzed to identify how brain activity varies during the performance of a task compared to baseline or resting states. This method is particularly useful for understanding the dynamics of brain function as it allows researchers to pinpoint which areas of the brain are more active or less active during specific cognitive or motor tasks ( Rominger et al., 2022b ; Gubler et al., 2023 ). Reportedly, TRP has wide usage in EEG-based design creativity studies ( Jia et al., 2021 ; Jia and Zeng, 2021 ; Gubler et al., 2022 ).

Event-related synchronization (ERS) and de-synchronization (ERD) have also been reported to be effective in creativity studies ( Wang et al., 2017 ). ERD refers to a decrease in EEG power (in a specific frequency band) compared to a baseline state. The reduction in alpha power, for instance, is often interpreted as an increase in cortical activity. Conversely, ERS denotes an increase in EEG power. The increase in alpha power, for example, is associated with a relative decrease in cortical activity ( Doppelmayr et al., 2002 ; Babiloni et al., 2014 ). Researchers have concluded that these two indicators play a pivotal role in creativity studies as they are significantly correlated with brain dynamics during creativity tasks ( Srinivasan, 2007 ; Babiloni et al., 2014 ; Fink and Benedek, 2014 ).

Brain functional connectivity analysis, EEG source localization, brain topography maps, and event-related potentials analysis are other EEG processing methods which have been employed in a few studies ( Srinivasan, 2007 ; Dietrich and Kanso, 2010 ; Giannopulu et al., 2022 ; Kuznetsov et al., 2023 ). Considering that these methods have not been employed in several studies and with respect to their potential to provide insight into brain activity in transient modes or the correlations between the brain lobes, future studies are suggested to utilize such methods.

3.4.4 Data analysis and knowledge extraction

What was mentioned indicates that EEG frequency analysis is an effective approach for examining brain behavior in creativity and design creativity processes ( Fink and Neubauer, 2006 ; Nguyen and Zeng, 2010 ; Benedek et al., 2011 , 2014 ; Wang et al., 2017 ; Rominger et al., 2018 ; Vieira et al., 2022b ). Analyzing EEG channels in the time or frequency domains across various creativity tasks helps identify key channels contributing to these experiments. TRP and ERD/ERS are well-known EEG analysis methods widely applied in the included studies. Some studies have used other EEG sub-bands such as delta or gamma ( Boot et al., 2017 ; Stevens and Zabelina, 2020 ; Mazza et al., 2023 ). Besides these methods, other studies have utilized EEG connectivity and produced brain topography maps to explore different stages of design creativity. The final stage of EEG-based research involves statistical analysis and classification.

In statistical analysis, researchers examine EEG characteristics like power or alpha band amplitude to determine if there are notable differences during creativity tasks. Comparisons are made across different brain lobes and participants to identify which brain regions are more active during various stages of creativity. Techniques such as TRP, ERD, and ERS are scrutinized using statistical hypothesis testing to see if brain dynamics vary among participants or across different creativity tasks. Additionally, the relationship between EEG features and creativity scores is explored. For instance, researchers might investigate whether there is a link between EEG alpha power and creativity scores like originality and fluency. These statistical analyses can be conducted through either temporal or frequency EEG data.

In the classification phase, EEG data are classified according to different cognitive states of the brain. For example, EEG recordings might be classified based on the stages of creativity tasks, such as idea generation and idea evolution ( Hu et al., 2017 ; Stevens and Zabelina, 2020 ; Lloyd-Cox et al., 2022 ; Ahad et al., 2023 ; Şekerci et al., 2024 ). Except for a few studies which employed machine learning, other studies targeted EEG analysis and statistical methods. In these studies, the main objective is reported to be the classification of designers’ cognitive states, their emotional states, or the level of their creativity. In the included papers, traditional classifiers such as support vector machines and k-nearest neighbor have been employed. Modern deep learning approaches can be used in future studies to extract the hidden valuable information of EEG in design creativity states ( Jia, 2021 ). In open-ended loosely controlled creativity studies, where the phases of creativity are not clearly defined, clustering techniques are employed to categorize or segment EEG time intervals according to the corresponding creativity tasks ( Jia et al., 2021 ; Jia and Zeng, 2021 ). While loosely controlled design creativity studies results in more reliable and natural outcomes compared to strictly controlled ones, analyzing EEG signals in loosely controlled experiments is challenging as the recorded signals are not structured. Clustering methods are applied to microstate analysis to segment EEG signals into pre-defined states and have structured blocks that may align with certain cognitive functions ( Nguyen et al., 2019 ; Jia et al., 2021 ; Jia and Zeng, 2021 ). Therefore, statistical analysis, classification, and clustering form the core methods of data analysis in studies of creativity.

Table 2 represents EEG-based design studies with details about the number of participants, probable psychometric tests, experiment protocol, EEG analysis methods, and main findings. These studies are reported in this paper to highlight some of the differences between creativity and design creativity.

In addition to the studies reported in Table 2 , previous reviews and studies ( Srinivasan, 2007 ; Nguyen and Zeng, 2010 ; Lazar, 2018 ; Chrysikou and Gero, 2020 ; Hu and Shepley, 2022 ; Kim et al., 2022 ; Balters et al., 2023 ) can be found, which comprehensively reported approaches in design creativity neurocognition. Moreover, neurophysiological studies in design creativity are not limited to EEG or the components in Table 2 . For instance, in Liu et al. (2014) , EEG, heart rate (HR), and galvanic skin response (GSR) was used to detect the designer’s emotions in computer-aided design tasks. They determined the emotional states of CAD design tasks by processing CAD operators’ physiological signals and a fuzzy logic model. Aiello (2022) investigated the effects of external factors (such as light) and human ones on design processes, which also explored the association between the behavioral and neurophysiological responses in design creativity experiments. They employed ANOVA tests and found a significant correlation between neurophysiological recordings and daytime, participants’ stress, and their performance in terms of novelty and quality. They also recognized different patterns of brain dynamics corresponding to different kinds of performance measures. Montagna et al. ( Montagna and Candusso, n.d. ; Montagna and Laspia, 2018 ) analyzed brain behavior during the creative ideation process in the earliest phases of product development. In addition to EEG, they employed eye tracking to analyze the correlations between brain responses and eye movements. They utilized statistical analysis to recognize significant differences in brain hemispheres and lobes with respect to participants’ background, academic degree, and gender during the two modes of divergent and convergent thinking. Although some of their results are not consistent with those from the literature, these experiments shed light on the experiment design and provide insights and a framework for future experiments.

4 Discussion

In the present paper, we reviewed EEG-based design creativity studies in terms of their main components such as participants, psychometrics, and creativity tasks. Numerous studies have delved into brain activities associated with design creativity tasks, examined from various angles. While Table 1 showcases studies centered on the Alternate Uses Test (AUT), and the Torrance Tests of Creative Thinking (TTCT), Table 2 summarizes the EEG-based studies on design and design creativity-related tasks. In this section, we are going to discuss the impact of some most important factors including participants, experiment design, and EEG recording and processing on EEG-based design creativity studies. Research gaps and open questions are thus presented based on the discussion.

4.1 Participants

4.1.1 psychometrics: do we have a population that we wished for.

Psychometric testing is crucial for participant selection, with participant screening often based merely on self-reported information or based on their educational background. Examining Tables 1 , 2 reveals that psychometrics are not frequently utilized in design creativity studies, indicating a notable gap in these investigations. Future research should consider establishing a standard set of psychometric tests to create comprehensive participant profiles, particularly focusing on intellectual capabilities ( Jauk et al., 2015 ; Ueno et al., 2015 ; Razumnikova, 2022 ). Taking a look at the studies which employed psychometrics, it could be inferred that there is a correlation between cognitive abilities such as intelligence and creativity ( Arden et al., 2010 ; Jung and Haier, 2013 ). The few psychometric tests employed primarily focus on determining and providing a cognitive profile, encompassing factors such as mood, stress, IQ, anxiety, memory, and intelligence. Notably, intelligence-related assessments are more commonly used compared to other tests. These psychometrics are subject to social masking according to which there is the possibility of unreliable self-report psychometrics being recorded in the experiments. These results might yield less accurate findings.

4.1.2 Sample size and participants’ characteristics

Participant numbers in these studies vary widely, indicating a broad spectrum of sample sizes in this research area. The populations in the studies varied in size, with most having around 40 participants, predominantly students. In the design of experiments, it is important to highlight that the sample size in the selected studies had a mean of 43.76 and a standard deviation of 20.50. It is worth noting that while some studies employed specific experimental designs to determine sample size, many did not have clear and specific criteria for sample size determination, leaving the ideal sample size in such studies an open question. Any studies determine their sample sizes using G* power ( Erdfelder et al., 1996 ; Faul et al., 2007 ), a prevalent tool for power analysis in social and behavioral research.

Initial investigations typically involved healthy adults to more thoroughly understand creativity’s underlying mechanisms. These foundational studies, conducted under optimal conditions, aimed to capture the essence of brain behavior during creative tasks. A handful of studies ( Ayoobi et al., 2022 ; Gubler et al., 2022 , 2023 ) have begun exploring creativity in the context of chronic pain or multiple sclerosis, but broader participant diversity remains an area for further research. Additionally, not all studies provided information on the ages of their participants. There is a noticeable gap in research involving older adults or those with health conditions, suggesting an area ripe for future exploration. Diversity in participant backgrounds, such as varying academic disciplines, could offer richer insights, given creativity’s multifaceted nature and its link to individual skills, affect, and perceived workload ( Yang et al., 2022 ). For instance, the creative approaches of students with engineering thinking might differ significantly from those with art thinking.

Gender was not examined in most included studies. There are just a few studies analyzing the effects of gender on creativity and design creativity ( Razumnikova, 2004 ; Volf et al., 2010 ; Vieira et al., 2020b , 2022a ; Gubler et al., 2022 ). There is a notable need for further investigation to fully understand the impact of gender on the brain dynamics of design creativity.

4.2 Experiment design

While the Alternate Uses Test (AUT) and the Torrance Tests of Creative Thinking (TTCT) are commonly used in creativity research, other tasks like the Remote Associate Task are also prevalent ( Schuler et al., 2019 ; Zhang et al., 2020 ). AUT and figural TTCT are particularly favored in design creativity experiments for their compatibility with design tasks, surpassing verbal or other creativity tasks in applicability ( Boot et al., 2017 ). When considering the creativity tasks in the studies, it is notable that the AUT is more frequently utilized than TTCT, owing to its simplicity and ease of quantifying creativity scores. In contrast, TTCT often requires subjective assessments and expert ratings for scoring ( Rogers et al., 2023 ). However, both TTCT and AUT have undergone modifications in several studies to investigate their potential characteristics further ( Nguyen and Zeng, 2014a ).

While the majority of studies have adhered to strictly controlled frameworks for their experiments, two studies ( Nguyen and Zeng, 2017 ; Nguyen et al., 2019 ; Jia, 2021 ; Jia et al., 2021 ) have adopted novel, loosely controlled approaches, which reportedly yield more natural and reliable results compared to the strictly controlled ones. The rigidity from strictly controlled creativity experiments can exert additional cognitive stress on participants, potentially impacting experimental outcomes. In contrast, the loosely controlled experiments are characterized as self-paced and open-ended, allowing participants ample time to comprehend the design problem, generate ideas, evaluate them, and iterate upon them as needed. Recent behavioral and theoretical research suggests that creativity is better explored within a loosely controlled framework, where sufficient flexibility and freedom are essential. This approach, which contrasts with the highly regulated nature of traditional creativity studies, aims to capture the unpredictable elements of design activities ( Zhao et al., 2020 ). Loosely controlled design studies offer a more realistic portrayal of the actual design process. In these settings, participants enjoy the liberty to develop ideas at their own pace, reflecting true design practices ( Jia, 2021 ). The flexibility in such experiments allows for a broader range of scenarios and outcomes, depending on the complexity and the designers’ understanding of the tests and processes. Prior research has confirmed the effectiveness of this approach, examining its validity from both neuropsychological and design perspectives. Despite their less rigid structure, these loosely controlled experiments are valid and consistent with previous studies. Loosely controlled creativity experiments allow researchers to engage with the nonlinear, ill-defined, open-ended, and intricate nature of creativity tasks. However, it is important to note that data collection and processing can pose challenges in loosely controlled experiments due to the resulting unstructured data. These challenges can be handled through machine learning and signal processing methods ( Zhao et al., 2020 ). For further details regarding the loosely controlled experiments, readers can refer to the provided references ( Zhao et al., 2020 ; Jia et al., 2021 ; Jia and Zeng, 2021 ; Zangeneh Soroush et al., 2024 ).

Participants are affected by external or internal sources during the experiments. Participants are asked not to have caffeine, alcohol, or other stimulating beverages. The influence of stimulants like caffeine, alcohol, and other substances on creative brain dynamics is another under-researched area. While some studies have investigated the impact of cognitive and affective stimulation on creativity [such as pain ( Gubler et al., 2022 , 2023 )], more extensive research is needed. The study concerning environmental factors like temperature, humidity, and lighting, has been noted to significantly influence creativity ( Kimura et al., 2023 ; Lee and Lee, 2023 ). Investigating these environmental aspects could lead to more conclusive findings. Understanding these variables related to participants and their surroundings will enable more holistic and comprehensive creativity studies.

4.3.1 Advantages and disadvantages of EEG being used in design creativity experiments

As previously discussed and generally known in the neuroscience research community, EEG stands out as a simple and cost-effective biosignal with high temporal resolution, facilitating the exploration of microseconds of brain dynamics and providing detailed insights into neural activity, which was summarized in Balters and Steinert (2017) and Soroush et al. (2018) . However, despite its advantages in creativity experiments, EEG recording is prone to high levels of noise and artifacts due to its low amplitude and bandwidth ( Zangeneh Soroush et al., 2022 ). The inclusion of physical movements in design creativity experiments further increases the likelihood of artifacts such as movement and electrode replacement artifacts. Additionally, it is essential to acknowledge that EEG does have limitations, including relatively low spatial resolution. It also provides less information regarding brain behavior compared to other methods such as fMRI which provides detailed spatial brain activity.

4.3.2 EEG processing and data analysis

In design creativity experiments, EEG preprocessing is an inseparable phase ensuring the quality of EEG data in design creativity experiments. Widely employed artifact removal methods include frequency-based filters and independent component analysis. Unfortunately, not all studies provide a detailed description of their artifact removal procedures ( Zangeneh Soroush et al., 2022 ), compromising the reproducibility of the findings. Moreover, while there are standard evaluation metrics for assessing the quality of preprocessed EEG data, these metrics are often overlooked or not discussed in the included papers. It is essential to note that EEG preprocessing extends beyond artifact removal to include the segmentation of unstructured EEG data into well-defined structured EEG windows each of which corresponds to a specific cognitive task. This presents a challenge, particularly in loosely controlled experiments where the cognitive activities of designers during drawing tasks may not be clearly delineated since design tasks are recursive, nonlinear, self-paced, and complex, further complicating the segmentation process ( Nguyen and Zeng, 2012 ; Yang et al., 2022 ).

EEG analysis methods in creativity research predominantly utilize frequency-based analysis, with the alpha band (particularly the upper alpha band, 10–13 Hz) being a key focus due to its effectiveness in capturing various phases of creativity, including divergent and convergent thinking. Across studies, a consistent pattern of decreases in EEG power during design creativity compared to rest has been observed in the low-frequency delta and theta bands, as well as in the lower and upper alpha bands in bilateral frontal, central, and occipital brain regions ( Fink and Benedek, 2014 , 2021 ). This phenomenon, known as task-related desynchronization (TRD), is a common finding in EEG analysis during creativity tasks ( Jausovec and Jausovec, 2000 ; Pidgeon et al., 2016 ). A recurrent observation in numerous studies is the link between alpha band activity and creative cognition, particularly original idea generation and divergent thinking. Alpha synchronization, especially in the right hemisphere and frontal regions, is commonly associated with creative tasks and the generation of original ideas ( Rominger et al., 2022a ). Task-Related Power (TRP) analysis in the alpha band is widely used to decipher creativity-related brain activities. Creativity tasks typically result in increased alpha power, with more innovative responses correlating with stronger alpha synchronization in the posterior cortices. The TRP dynamics, marked by an initial rise, subsequent fall, and a final increase in alpha power, reflect the cognitive processes underlying creative ideation ( Rominger et al., 2018 ). Creativity is influenced by both cognitive processes and affective states, with studies showing that cognitive and affective interventions can enhance creative cognition through stronger prefrontal alpha activity. Different creative phases (e.g., idea generation, evolution, evaluation) exhibit unique EEG activity patterns. For instance, idea evolution is linked to a smaller decrease in lower alpha power, indicating varying attentional demands ( Fink and Benedek, 2014 , 2021 ; Rominger et al., 2019 , 2022a ; Jia and Zeng, 2021 ).

Hemispheric asymmetry plays a crucial role in creativity, with increased alpha power in the right hemisphere linked to the generation of more novel ideas. This asymmetry intensifies as the creative process unfolds. The frontal cortex, particularly through alpha synchronization, is frequently involved in creative cognition and idea evaluation, indicating a role in top-down control and internal attention ( Benedek et al., 2014 ). The parietal cortex, especially the right parietal cortex, is significant for focused internal attention during creative tasks ( Razumnikova, 2004 ; Benedek et al., 2011 , 2014 ).

EEG phase locking is another frequently employed analysis method. Most studies have focused on EEG coherence, EEG power and frequency analysis, brain asymmetry methods (hemispheric lateralization), and EEG temporal methods ( Rominger et al., 2020 ). However, creativity, being a higher-order, complex, nonlinear, and non-stationary cognitive task, suggests that linear and deterministic methods like frequency-based analysis might not fully capture its intricacies. This raises the possibility of incorporating alternative, specifically nonlinear EEG processing methods, which, to our knowledge, have been sparingly used in creativity research ( Stevens and Zabelina, 2020 ; Jia and Zeng, 2021 ). Additional analyses such as wavelet analysis, brain source separation, and source localization hold promise for future research endeavors in this domain.

As mentioned in the previous section, most studies have considered participants without their cognitive profile and characteristics. In addition, the included studies have chosen two main approaches including traditional statistical analysis and machine learning methods ( Goel, 2014 ; Stevens and Zabelina, 2020 ; Fink and Benedek, 2021 ). It should be noted that almost all of the included studies have employed the traditional statistical methods to examine their hypotheses or explore the differences between participants performing creativity tasks ( Fink and Benedek, 2014 , 2021 ; Rominger et al., 2019 , 2022a ; Stevens and Zabelina, 2020 ; Jia and Zeng, 2021 ).

Individual differences, such as intelligence, personality traits, and humor comprehension, also affect EEG patterns during creative tasks. For example, individuals with higher monitoring skills and creative potential exhibit distinct alpha power changes during creative ideation and evaluation ( Perchtold-Stefan et al., 2020 ). The diversity in creativity tasks (e.g., AUT, TTCT, verbal tasks) and EEG analysis methods (e.g., ERD/ERS, TRP, phase locking) used in studies highlights the methodological variety in this field, emphasizing the complexity of creativity research and the necessity for multiple approaches to fully grasp its neurocognitive mechanisms ( Goel, 2014 ; Gero and Milovanovic, 2020 ; Rominger et al., 2020 ; Fink and Benedek, 2021 ; Jia and Zeng, 2021 ).

In statistical analysis, studies often assess the differences in extracted features across different categories. For instance, in a study ( Gopan et al., 2022 ), various features, including nonlinear and temporal features, are extracted from single-channel EEG data to evaluate levels of Visual Creativity during sketching tasks. This involves comparing different groups within the experimental population based on specific features. Notably, the traditional statistical analyses not only provide insights into differences between experimental groups but also offer valuable information for machine learning methods ( Stevens and Zabelina, 2020 ). In another study ( Gubler et al., 2023 ), researchers conducted statistical analysis on frequency-based features to explore the impact of experimentally induced pain on creative ideation among female participants using an adaptation of the Alternate Uses Task (AUT). The analysis involved examining EEG features across channels and brain hemispheres under pain and pain-free conditions. Similarly, in another study ( Benedek et al., 2014 ), researchers conducted statistical analysis on EEG alpha power to investigate the functional significance of alpha power increases in the right parietal cortex, which reflects focused internal attention. They found that the Alternate Uses Task (AUT) inherently relies on internal attention (sensory-independence). Specifically, enforcing internal attention led to increased alpha power only in tasks requiring sensory intake but not in tasks requiring sensory independence. Moreover, sensory-independent tasks generally exhibited higher task-related alpha power levels than sensory intake tasks across both experimental conditions ( Benedek et al., 2011 , 2014 ).

Although most studies have employed statistical measures and analyses to investigate brain dynamics in a limited number of participants, there is a considerable lack of within-subjects and between-subjects analyses ( Rominger et al., 2022b ). There exist several studies which differentiate the brain dynamics of expert and novice designers or engineering students in different fields ( Vieira et al., 2020c , d ); however, more investigations with a larger number of participants are required.

While statistical approaches are commonly employed in EEG-based design creativity studies, there is a notable absence of machine learning methods within this domain. Among the included studies, only one ( Gopan et al., 2022 ) utilized machine learning techniques. In this study, statistical and nonlinear features were extracted from preprocessed EEG signals to classify EEG data into predefined cognitive tasks based on EEG characteristics. The study employed machine learning algorithms such as Long Short-Term Memory (LSTM), Support Vector Machines (SVM), and k-Nearest Neighbor (KNN) to classify EEG samples. These methods were utilized to enhance the understanding of the relationship between EEG signals and cognitive tasks, offering a promising avenue for further exploration in EEG-based design creativity research ( Stevens and Zabelina, 2020 ).

4.4 Research gaps and open questions

In this review, we aimed to empower readers to decide on experiments, EEG markers, feature extraction algorithms, and processing methods based on their study objectives, requirements, and limitations. However, it is essential to acknowledge that this review, while valuable in exploring EEG-based creativity and design creativity, has certain limitations which are summarized below:

1. Our review focuses on just the neuroscientific aspects of prior creativity and design creativity studies. Design methodologies and creativity models should be reviewed in other studies.

2. Included studies have employed only a limited number of adult participants with no mental or physical disorder.

3. Most studies have utilized fNIRS or EEG as they are more suitable for design creativity experiments, but we only focused on EEG based studies.

According to what was discussed above, it is obvious that EEG-based design creativity studies have been quite recently introduced to the field of design. This indicates that research gaps and open questions should be addressed for future studies. The following provides ten open questions we extracted from this review.

1. What constitutes an optimal protocol for participant selection, creativity task design, and procedural guidelines in EEG-based design creativity research?

2. How can we reconcile inconsistencies arising from variations in creativity tests and procedures across different studies? Furthermore, how can we address disparities between findings in EEG and fMRI studies?

3. What notable disparities exist in brain dynamics when comparing different creativity tests within the realm of design creativity?

4. In what ways can additional physiological markers, such as ECG and eye tracking, contribute to understanding neurocognition in design creativity?

5. How can alternative EEG processing methods beyond frequency-based analysis enhance the study of brain behavior during design creativity tasks?

6. What strategies can be employed to integrate combinational methods like EEG-fMRI to investigate design creativity?

7. How can the utilization of advanced wearable recording systems facilitate the implementation of more naturalistic and ecologically valid design creativity experiments?

8. What are the most effective approaches for transforming unstructured data into organized formats in loosely controlled creativity experiments?

9. What neural mechanisms are associated with design creativity in various mental and physical disorders?

10. In what ways can the application of advanced EEG processing methods offer deeper insights into the neurocognitive aspects of design creativity?

5 Conclusion

Design creativity stands as one of the most intricate high-order cognitive tasks, encompassing both mental and physical activities. It is a domain where design and creativity are intertwined, each representing a complex cognitive process. The human brain, an immensely sophisticated biological system, undergoes numerous intricate dynamics to facilitate creative abilities. The evolution of neuroimaging techniques, computational technologies, and machine learning has now enabled us to delve deeper into the brain behavior in design creativity tasks.

This literature review aims to scrutinize and highlight pivotal, and foundational research in this area. Our goal is to provide essential, comprehensive, and practical insights for future investigators in this field. We employed the snowball search method to reach the final set of papers which met our inclusion criteria. In this review, more than 1,500 studies were monitored and assessed as EEG-based creativity and design creativity studies. We reviewed over 120 studies with respect to their experimental details including participants, (design) creativity tasks, EEG analyses methods, and their main findings. Our review reports the most important experimental details of EEG-based studies and it also highlights research gaps, potential future trends, and promising avenues for future investigations.

Author contributions

MZ: Formal analysis, Investigation, Writing – original draft, Writing – review & editing. YZ: Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by NSERC Discovery Grant (RGPIN-2019-07048), NSERC CRD Project (CRDPJ514052-17), and NSERC Design Chairs Program (CDEPJ 485989-14).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: design creativity, creativity, neurocognition, EEG, higher-order cognitive tasks, thematic analysis

Citation: Zangeneh Soroush M and Zeng Y (2024) EEG-based study of design creativity: a review on research design, experiments, and analysis. Front. Behav. Neurosci . 18:1331396. doi: 10.3389/fnbeh.2024.1331396

Received: 01 November 2023; Accepted: 07 May 2024; Published: 01 August 2024.

Reviewed by:

Copyright © 2024 Zangeneh Soroush and Zeng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yong Zeng, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Utilizing potential field mechanisms and distributed learning to discover collective behavior on complex social systems.

1. Introduction

2. mobile individual network, 2.1. models for mobile perception individual, 2.2. potential field model, 2.3. collective behavior, 2.4. graph-theoretic representation, 3. distributed learning and cooperative control, 3.1. distributed learning, 3.2. cooperative control, 4. convergence analysis.

• C1: The assumptions regarding measurement noise in Equations (25) and (26), as well as Remark 2 in M1 and M3, can be satisfied.
• C2: Assuming that the radial basis functions in M2 and ∇ C ^ ( φ , q ) have smooth and bounded derivatives with respect to q achieves this.
• C3: In Equation (51), A and B are smooth within D R due to their dependence on smooth radial basis functions.
• C4: A similar rationale from Brus is introduced [ 48 ], A ( t ;   x ) | x ~ ≤ 1 − δ   < 1 ,   ∀ t .
• C5: For a fixed x ¯ .
• C6: The element of Q in (51) is considered to be a deterministic function of x ∈ D R , except for ∇ C ^ ( φ ( t ) ,   q ) . Due to M1 and (36), for a fixed q , it is described by:
• C7: Because of the measurement noise assumption in (25), it can be satisfied.
• C8, C9, C10, C11: The conditions can be verified through the time-varying gain sequences shown in (42).

5. Simulation Results

5.1. standard environment, 5.2. noise environment, 5.3. density environment, 5.4. without communication environment, 5.5. multiple potential field environment, 5.6. multiple negative potential field environment, 6. conclusions, author contributions, data availability statement, conflicts of interest.

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Zhang, J.; Qu, Q.; Chen, X. Utilizing Potential Field Mechanisms and Distributed Learning to Discover Collective Behavior on Complex Social Systems. Symmetry 2024 , 16 , 1014. https://doi.org/10.3390/sym16081014

Zhang J, Qu Q, Chen X. Utilizing Potential Field Mechanisms and Distributed Learning to Discover Collective Behavior on Complex Social Systems. Symmetry . 2024; 16(8):1014. https://doi.org/10.3390/sym16081014

Zhang, Junqiao, Qiang Qu, and Xuebo Chen. 2024. "Utilizing Potential Field Mechanisms and Distributed Learning to Discover Collective Behavior on Complex Social Systems" Symmetry 16, no. 8: 1014. https://doi.org/10.3390/sym16081014

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• Published: 02 August 2024

Online communities come with real-world consequences for individuals and societies

• Atte Oksanen   ORCID: orcid.org/0000-0003-4143-5580 1 ,
• Magdalena Celuch   ORCID: orcid.org/0000-0001-8941-0396 1 ,
• Reetta Oksa   ORCID: orcid.org/0000-0002-8007-4653 1 &
• Iina Savolainen   ORCID: orcid.org/0000-0002-8811-965X 1  

Communications Psychology volume  2 , Article number:  71 ( 2024 ) Cite this article

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• Human behaviour

Online communities have become a central part of the internet. Understanding what motivates users to join these communities, and how they affect them and others, spans various psychological domains, including organizational psychology, political and social psychology, and clinical and health psychology. We focus on online communities that are exemplary for three domains: work, hate, and addictions. We review the risks that emerge from these online communities but also recognize the opportunities that work and behavioral addiction communities present for groups and individuals. With the continued evolution of online spheres, online communities are likely to have an increasingly significant role in all spheres of life, ranging from personal to professional and from individual to societal. Psychological research provides critical insights into understanding the formation of online communities, and the implications for individuals and society. To counteract risks, it needs to identify opportunities for prevention and support.

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Introduction.

Online communities are social networks on the internet that utilize technology for interaction. They began to gain popularity in the 1990s with the development of the internet and information and communications technologies 1 , 2 , 3 . The emergence of online communities was further accelerated by Web 2.0 and social media starting in the mid-2000s 4 . Social media platforms provide users fast access to likeminded others, and they speed up communication and offer new ways for interaction 5 , 6 , 7 .

The progress of these interactive technologies has been remarkably fast, and they carry both opportunities and risks. The work context is a good example of the complexity of online communities. On one hand, online communication is flexible, fast, and effective regardless of location, and it provides workers with new ways to collaborate and socialize with each other 8 . On the other hand, online communication bears risks, such as workplace cyberbullying 9 , 10 and misinterpretation of messages and feedback, endangering the mental well-being of employees by inducing technostress, psychological distress, and work exhaustion 11 , 12 . We recognize that online communities can be supportive and enhance well-being in many ways, but there are also online communities that carry risks for participants and wider society such as hate and addiction communities.

In this perspective article, we review the characteristics of online communities along with the opportunities and risks they present to their users. We cover the role of online communities in the contexts of 1) work, 2) hate and harassment, and 3) addiction, as three domains of outstanding relevance for the society that showcase the multifaceted nature of online communities. These diverse domains provide an excellent starting point for the theoretical overview of online communities. The first topic of work recognizes that online communication and online communities have a growing importance in today’s work life. The second topic concerns research evidence of online hate communities that are based on harmful ideas and actions against other people. This topic has had massive implications on political and societal discussions starting in the 2010s. The third topic talks about online communities in the context of addictions. The online dissemination and proliferation of various views and behaviors have further led to people discovering new, potentially harmful activities or becoming excessively engaged in the digital world. This has prompted significant research into online addictions 13 , 14 , 15 .

How and why online communities form

Human beings are inherently social and seek companionship and social engagement whenever possible 16 . Online communication responds to this social need of belonging. Online communities emerge and thrive in digital spaces, comprised of members who engage in active communication in a shared topic or interest area 17 , 18 . Online communities can form in a variety of online contexts, including but not limited to social media platforms, discussion forums, and chatrooms 19 . Online communities are significant for finding companionship, fostering connection, accessing information, and receiving support 20 , 21 . Like any group or community, online communities vary in size, cohesion, and network and focus area. The degree of members’ anonymity may also vary considerably 4 , 22 . A distinct feature of the global online sphere is that no matter how unusual or rare one’s interests are, they are likely to find others with similar interests 23 . Members of online communities are often heterogenous in their social characteristics, including socioeconomic status, life stage, ethnicity, and gender, but tend to be likeminded and homogeneous in terms of their shared interests and attitudes 24 , 25 .

The online sphere provides an ideal environment for the building of networks that hold significance for the social and personal identity of their participants 5 . Social identity theory (SIT), as initially proposed by Tajfel and Turner 26 , describes a process in which an individual’s identity is partially shaped by their sense of belonging to preferred social groups. This concept is often measured by evaluating an individual’s subjective feeling of being a part of the desired groups 27 . Theories and models that use the social identity approach 28 , 29 are very relevant for understanding online communities and online group behavior.

One of the most important models proposed over the years has been the social identity model of deindividuation effects (SIDE) 30 , 31 . The model was originally motivated by the topic of online communication’s anonymity – an issue that had already drawn the attention of social psychologists in the early 1980s 32 . According to the SIDE model, the deindividuation effect of social identification is especially prevalent in online interactions that are characterized by at least a certain level of anonymity, as it promotes a shift from individual to group self and therefore facilitates behaviors benefiting the group as well as stereotyping outgroup members and viewing them as a representative of their group rather than an individual 33 , 34 , 35 , 36 , 37 . Relatedly, lack of social cues, such as eye contact, in online interactions has been found to lead to behavioral disinhibition through the so-called online sense of unidentifiability 38 .

Context collapse occurs very commonly in social media. This means that boundaries between different social spheres blur together (e.g. interacting with people from different life spheres such as work, family, and friends on the same platform). This can influence and challenge the users’ self-presentation and their navigation within different online discussions and audiences 39 . In these diverse social contexts, which can vary greatly in terms of values and norms, users may need to balance their personal authenticity based on their audience expectations. Additionally, they can face situations that compromise their privacy or necessitate self-censorship 40 , 41 . However, social media simultaneously provides multiple features or opportunities (i.e., affordances) for users to control and maintain their public identities and social networks. In other words, users can manage how they present themselves to the public online and how they interact with their social networks through the tools and functions provided by social media platforms. Typically, these tools include the customization of ones’ profile, maintaining visibility, allowing access, editing content, and providing links and connections to other platforms 39 , 42 , 43 , 44 . Affordances essentially link to the question of how users maintain and create smaller groups or wider communities within certain platforms. This depends on the features and design of each social media platform or internet site.

Characteristics of both internet platforms and applications strongly influence online human behavior. In particular, commercial platforms are designed to attract people’s interests in the content. Moral and emotional contents spread faster on social media as they capture users’ attention more effectively as compared to neutral content 45 . The process is also facilitated by the algorithms of social media platforms 5 , 46 , and social influence, which lead people to engage with content that is already popular 47 . This has a profound impact on online communities and the way they communicate, especially as these effects have been found to be much stronger within networks comprised of individuals who share similar views than between such networks 48 . Thus, a tendency can be induced toward likeminded individuals who support certain opinions and behaviors, strengthening their existing attitudes and providing a stronger identification with the ingroup 49 , 50 . In this context, even exposure to differing opinions may primarily serve as a tool to further distinguish between “us” and “them” and hinder productive dialogue 4 , 51 .

These mechanisms are further captured by the Identity Bubble Reinforcement Model (IBRM), which focuses on explaining how characteristics of online communication facilitate the formation of tight-knit networks, namely social media identity bubbles 4 , 5 . Such bubbles or echo chambers are characterized by three mutually reinforcing features: high identification with the other members (ingroup), homophily or the strong tendency to interact with likeminded others, and information bias, namely heavy reliance on information obtained from the community 4 , 5 . Involvement in such communities has been found to be associated with compulsive internet use 5 , cyberaggression 52 and problem gambling 53 . At the same time, involvement in online identity bubbles facilitates social support and may buffer mental well-being in some situations 54 .

In summary, there are three core aspects to consider in online communities. First, technological design is a critical component that impacts what people can do within online communities. The phenomenon of online communities has existed as long as the internet 2 , but current social media platforms use different interfaces and AI algorithms than their earlier versions, being essentially engineered to provide content for users. This technological side impacts significantly how people behave and react online. Second, contextual issues are highly important in online communities. Generally, contexts in the online sphere may collapse easily. At the same time, the internet and social media platforms facilitate development of very closed online communities that are based on shared interests. These interests may sometimes be very specific. Core social psychological group theories and their updates provide good tools for understanding evolving online group behavior. SIDE and IBRM are examples of theories that have been proven very useful in empirical research.

Social media communities at work

The accelerated development of information technology in recent decades has significantly reshaped the workplace. The foundation for this technological advancement was laid in the 1960s with the development of ARPANET, a forerunner to the internet 4 . The same era also witnessed the birth of the Open Diary – an internet-based diary community allowing user participation through messages, thereby serving as a precursor to social media 55 . During the emergence of the internet in the 1990s, known now as the Web 1.0 era, personal web pages, content creation, and numerous work communication tools, such as online telecommunications and email, became prevalent 55 , 56 . The term Web 2.0 was first coined in 2004, concurrently when Facebook became popular, symbolizing the internet’s transition to a more socially diverse and interactive era 55 . The change in user behavior from passive web content consumers to active, bidirectional information creators and editors was an evident part of the transition from Web 1.0 to Web 2.0 4 .

In recent years, there has been a substantial increase in the use of digital communication technologies in the workplace, primarily driven by advancements of the internet and social media services 12 , 57 . Numerous expert organizations are now leveraging corporate social media platforms, such as Microsoft Teams and Workplace from Meta, for their communication needs 8 , 58 . Networking in enterprise social media platforms facilitates real-time messaging, task organisation, and formal and informal team collaboration, synchronously and asynchronously across organisational groups and in different geographic locations 8 , 59 , 60 . Work communication also unfolds through instant-messaging applications, such as WhatsApp, and general social media platforms, such as Facebook, X (formerly Twitter), and LinkedIn, which are being utilised for professional purposes. These social platforms have the potential to encourage professionals to engage in collaboration, share information and ideas, and expand their expertise on a global scale, extending beyond their specific job responsibilities and organisational boundaries 8 , 61 . Notably, social communication tools have expanded to encompass traditional white-collar environments and now provide value for blue-collar workers as well, for example, as a medium for communication and task organisation 11 .

Social media messaging and networking for professional purposes not only enhance knowledge transfer and flow but also nurture the human need for social belonging 62 , 63 . Given the growing prevalence of remote and hybrid forms of work, social media has the potential to maintain and foster social interactions regardless of location 57 , 64 . Remote and hybrid work arrangements can, however, reduce the chances of establishing and nurturing high-quality work relationships 65 , 66 . Recent studies have also indicated a link between resistance to remote work and having quality workplace relationships 67 . Indeed, working far from the physical work community can increase the growing phenomenon of loneliness at work 65 , 68 , 69 . At the same time, in some circumstances, online networking among colleagues nurtures social connections and alleviates feelings of loneliness 54 . Social connections and feelings of belongingness in the work community and one’s professional circles are vital to support employees’ mental well-being and combat loneliness at work 54 , 70 . Feelings of loneliness at work can, for example, lower professionals’ work engagement, increase their dissatisfaction at work 71 and burnout 72 .

The use of social media platforms for professional objectives can enrich communication and foster meaningful connections 8 , 73 . Professional online relationships can be formed and maintained individually person to person or as a part of bigger professional online communities. In the professional sphere, online communities are commonly referred to as communities of practice due to their origins within the cultural framework of either virtual or traditional organisations 74 . Communication visibility in these online communities of practice can foster knowledge sharing and social learning, trust, and innovation 75 , 76 , 77 . The sense of belonging and togetherness with colleagues can also be enhanced in these online communities 8 , 78 . Online communities of practice can be a source of affective social support that promotes experiencing group identification and meaningfulness, which in turn can foster employees’ engagement in their work 79 . Employees’ social media collaboration is also associated with increased team and employee performance 78 , and employees perceived social media–enabled productivity 80 . Both formal and informal online communities are known to accelerate professional development 8 , 81 , 82 .

However, online communities at work can have downsides. These include tensions within the organisation due to employees sharing nonwork-related information that can tighten the bonds and build trust but, interestingly, can also hinder work-related information sharing 76 . Furthermore, stress arising from technology use (i.e., technostress), psychological distress, and burnout are pervasive challenges of professional online collaboration in technologised work environments 11 , 12 . Concentration problems can emerge, and the boundaries of work and private life can also be blurred and stimulate conflicts 83 , 84 . In addition, social relationships at work can be challenged 85 by discrimination, ostracism, and face-to-face bullying. These issues are also present in online communication, where they take on new forms and meanings. Work-related cyberbullying 9 , 10 , 86 and hate and harassment, which may also come from fellow work community members, can be detrimental for the targets and lead to lowered well-being 87 .

Hate communities

The ease of online communication facilitates the dissemination and proliferation of negative and dangerous views and behaviors. Subsequently, online hate (i.e., cyberhate) and online hate crime have emerged as a prominent area of research in the context of online communication, with the same ease of access contributing to their prevalence 88 , 89 . Online hate covers a wide range of intensive and hostile actions that target individuals or groups based on their beliefs and demographic factors, such as ideology, sexual orientation, ethnic background, or appearance 90 . The rise of hostile online communication has been considered a growing societal concern over the past decade 4 , 87 , 91 , 92 , 93 .

The history of hate in online communication goes back to the first internet networks. Organised hate groups have always been interested in the latest technologies to recruit new members and disseminate information. For instance, White supremacists in the US were pioneers in adopting electronic communication networks during the 1980s. Notably, in 1983, neo-Nazi publisher George P. Dietz established the first dial-up bulletin board system (BBS), marking an early utilisation of online communication methods 94 . Shortly after the inception of the World Wide Web, hate groups marked their online presence. Stormfront.org, launched in 1995, was one of the first and most important hate sites during the Web 1.0. era 95 . Since then, over the past 30 years, continuous technological advancements have significantly enhanced their communication capabilities 4 .

Particularly the rise of social media since the mid-2000s was an important game changer in the dissemination and development of online hate. Foxman and Wolf 96 (p. 11) summarized this change concerning the Web 2.0 era of social media: “In the interactive community environment of Web 2.0, social networking connects hundreds of millions of people around the globe; it takes just one ‘friend of a friend’ to infect a circle of hundreds or thousands of individuals with weird, hateful lies that may go unchallenged, twisting mind in unpredictable ways.” The last 10 years of the internet have been, however, striking, as online hate has lurked from the margins and started to become a tool of political populists in the Western world 1 , 97 . Uncertainty of the times with various crises related to terrorism, economy, and the global COVID-19 pandemic have also accelerated the phenomenon.

Research on online hate associated with the COVID-19 pandemic has suggested that, in crisis situations, hate communities can organise quickly and rapidly develop new narratives 98 , 99 , reactively focusing on recent and highly debated issues 100 . Such hateful messages spread most effectively in smaller, hierarchical, and isolated online communities 99 , highlighting the dangers of online echo chambers or identity bubbles 4 , 5 , 101 . Even if hateful narratives are not endorsed by most users on the platform, the flow of such information tends to be sustained over time, as members of echo chambers encourage each other and amplify their shared worldview 102 . This is often done by referring to and contesting opposing views in a marginalising and undermining way, making counter-messaging ineffective or even counter-effective 103 . Various options of demonstrating (dis)agreement and promoting content on social media are used for creating echo chambers and disseminating hateful content 104 . However, it is worth noting that even on social media sites derived of content-promoting algorithms and vanity metrics present on many of the major platforms, users can quickly learn to recognise and promote extremist content as important and worthy of attention 105 .

The example of COVID-19-related hateful activity showed how hate communities effectively spread malicious content across various social media sites, incapacitating moderation attempts of any single platform 98 . Gaming sites are another type of environment where hate and extremist communities organise, recruit, and communicate. It has been argued that the development and characteristics of the gaming industry and the games themselves make online gaming platforms a suitable place for spreading hateful ideologies 106 . Hate communities also commonly use less moderated online spaces as an alternative to mainstream social media platforms, moving toward the creation of parallel ecosystems 107 , 108 , 109 . The need to leave mainstream spaces due to the risk of moderation and censorship is often used for community building by means of leveraging the sense of online persecution and victimisation 109 , 110 .

Hate communities, especially their influential members, use various other techniques and activities for community building. These activities include, for example, the development and promotion of jargon and coded language that underline the “us vs. them” dichotomy, often using derogatory and offensive phrasing 100 , 104 , 105 , 106 , 110 , 111 , 112 , 113 as well as the use of various audiovisual and interactive materials to capture the recipients’ attention 106 , 107 , 109 . These strategies can be used differently in different contexts and adapted to groups’ needs 114 . The incel (i.e., “involuntarily celibate males”) online community is an interesting example of how these strategies are used in practice. According to research, all active participants of online incel discussions commonly use derogatory terms to refer to women 115 and they create powerful dichotomies between themselves and outgroups: both women and society at large, using memes, reels, and other forms of online content to carry their message 115 , 116 .

Another commonly utilised method is ironic and humorous messaging in the form of memes and jokes that further allows for the spread of radical ideologies using seemingly unserious content 104 , 106 , 117 , 118 , 119 . Such jokes and memes are often part of conspiracy talk, which is a type of everyday discourse common among hate communities, referring to conspiracy theories through implicit references and anecdotal evidence from community members’ own experiences, often in reaction to news coverage from mainstream sources 119 , 120 . Research has suggested a strong community-building potential of this type of online discourse, as it allows users to share their concerns and worries and make sense of their experiences 119 . These uncertainties are used by extremist groups to create new anxieties and introduce new problems, as well as to strengthen the community, as evoking feelings of threat can boost the sense of belonging and reinforce the ingroup’s worldview 100 . This is especially concerning considering evidence on the associations of supporting far-right ideologies with distrust toward traditional media outlets 121 . Individuals distrustful toward established broadcasters may be motivated to search for alternative sources of information and, as a result, get involved in online hate communities, where they may become further radicalised through community-building practices such as those described above 107 .

Research has suggested that, over time, as online communities develop, both positive and negative sentiments in their content increase, and this effect may be stronger in hate communities than in comparable non-hateful groups 111 . This is attributed to the group-formation processes as shared outgroups are established, leading to more negative emotions being expressed. Simultaneously, involvement in a likeminded community results in more positive affect 111 . Interestingly, influential users in online hate communities commonly use seemingly neutral and value-free language, often referring to news from mainstream sources. This is, however, done in a way that is meant to evoke emotion and provoke hateful discussion 122 . This helps to avoid content deletion or user suspension and may further endanger new users looking for alternative sources of information by exposing them to hateful discussions and possibly fostering their radicalisation and involvement in the community 123 .

Hateful online content is likely to increase as a result of offline hateful acts 124 , 125 and local socio-political events that are significant to the group and their worldviews. Together, these can have long-term effects on online hate communities, resulting in increased activity and group cohesion 126 . Although online communities might avoid encouraging offline violence for fear of the discussion being moderated or even completely banned by site administrators 104 , they nevertheless contribute to the creation of an environment where hate – both online and offline – is seen as more acceptable and justified 119 , 127 , 128 . Indeed, perpetrators of violent extremist acts offline have been previously found to be involved in extremist online communities prior to the act 129 , and the spread of hateful content in social media has been tied to subsequent offline hate crimes 93 , 130 .

Addiction and online communities

There is a complex relationship between addiction and online communities which can be explained through three core factors. First, fast internet connection and mobile devices have enabled unlimited, easy, and continuous online access. Studies have reported that heavy online use symptoms are comparable to substance-related addiction, including mood modification, withdrawal symptoms, conflict, and relapses 15 , 131 . Second, major social media sites use algorithms to attract and engage their users 4 . Connectedness to others and positive emotions arising from actions and vanity metrics, such as “likes” and supportive comments, reinforce usage and can lead people to become addicted 131 , 132 . Third, participation in online communities has addictive power. For instance, Naranjo-Zolotov and colleagues 133 , who investigated Latin American individuals, found that the sense of a virtual community was the primary factor fueling addiction to social media usage. There is a symbiotic relationship between online communities and technology: technology provides the means for a wide range of activities and it’s those activities, rather than the devices themselves, that users typically become addicted to. These online activities often concern the most recognised behavioral addictions such as sex, shopping, gaming or gambling.

When discussing addiction related to online use, it should be acknowledged that, in current terminology, there is a wide variety of terms expressing the excessive use of the internet or social media. For example, compulsive internet use and problematic internet use are commonly used 134 , 135 , 136 . Technological devices and social media sites are designed to be as engaging as possible. Features such as notifications, personalised content, and interactive elements are strategically implemented to capture users’ attention and encourage prolonged usage 137 . These devices and the features within have greatly transformed social interactions, especially in technologically advanced countries and particularly among younger generations who have grown up with smart technology. Current reviews underline a need to build a more complex understanding of different ways of social media use 138 . This involves investigating the geographical, sociocultural, and digital environments within which problematic behaviors arise and unfold 15 .

In this Perspective, our focus is on exploring the role of online communities in reinforcing certain problem behaviors. Our examples come from gambling and digital gaming online communities. Online communities centered around gambling and digital gaming are growing in popularity, drawing users to engage and exchange ideas and experiences with others who share similar interests in these activities 21 , 139 . Online gambling communities usually manifest independently from the actual games, often taking the form of discussion forums dedicated to all aspects of gambling. These forums serve as platforms for participants to engage in dialogues typically including the exchange of tips, strategies, and personal experiences related to gambling 139 . A review of research on online gambling communities indicates that content on these types of online platforms commonly presents gambling in a predominantly positive light 21 . This positive portrayal also seems to resonate with individuals who have a preexisting affinity for gambling, drawing them to participate in the communities online. Joining gambling communities online also appears to be a socially transmitted behavior, as existing members frequently invite their friends or online contacts to join these communities, often through social media where gambling operators also admin and promote communities for their followers 21 , 140 . The existence of online communities dedicated to gambling provides a convenient platform for gamblers to express interests and emotions they might otherwise hesitate to share in face-to-face interactions. The risk associated with online communities, like those that unite individuals based on a common interest, goals, and norms, is that they might normalise gambling activities and encourage the development of new gambling habits and behaviors. Notably, research has linked active participation in online gambling communities to an increased risk of problem gambling 21 , 139 , 141 , 142 .

Online gaming communities are distinct from gambling communities as they inherently exist within the games they are tied to 139 . Virtual social groups that form within games tend to be persistent, and players utilise them to collaborate with each other and enhance their in-game success 143 . Within these communities, members freely exchange skills, knowledge, and virtual assets, including currency used in the game. Players can have different roles and responsabilities within gaming communities. These include sharing responsibilities and communal resources such as in-game items and money 139 . Gaming communities can significantly contribute to the construction of gamers’ online identities, which could explain the remarkable success of these communities. This process acts as a validating influence, enabling players to reintegrate themselves through features like avatars and virtual belongings within their communities 144 . Social engagement with fellow players serves as a primary motivator for gaming and can lead to positive social capital gains 145 , 146 , but it can also immerse players in the games, which can lead to excessive time spent on gaming and even to online gaming addiction 147 , 148 . Further, some in-game activities, such as forms of microtransactions that bear resemblance to gambling, seem to gain support within the gaming community, posing challenges to prevention 149 .

Although involvement in various online communities can potentially lead to harmful behaviors and even the initiation or maintenance of addiction, it is crucial to recognise that these communities also serve as a valuable resource for their users. For instance, gamers who harness social bonds within video games often report favorable social outcomes, including support from in-game friends 150 . Online discussion forums have proven to be a valuable source of support for gamblers, especially those experiencing gambling-related problems or harms 21 . Engaging in conversations online with peers who share similar experiences provides a natural and easily accessible safe space where they can narrate experiences without the fear of judgment. Participants can openly discuss how behaviors like gambling have impacted their lives and share their current self-perceptions. Members of communities focusing on recovery actively exchange information about available resources and offer insights into how to effectively utilise online forums that aid and encourage the recovery process 21 , 139 .

Growing relevance of online communities

Online communities have growing importance in people’s lives today. We are in the middle of remarkable technological change with increasingly ubiquitous computing, which includes major leaps in the development of artificial intelligence technologies and extended realities 151 , 152 . In some visions, the metaverse is the future of the internet and the 3D model of the internet. The term has been hyped during the early 2020 s, partially so because one of the biggest technology companies, Facebook, renamed itself to Meta and envisioned a metaverse-integrated, immersive ecosystem 152 . Part of the development of the metaverse is tied to technologies and gadgets, but it is hardware independent and functions globally, also within the mobile devices we already use in 2024 152 , 153 . At this point, it is too early to say how important the metaverse will be in the forthcoming years 154 , but it is certain that online communities will play a role in any future development of the internet.

Online communities are fundamentally enabled by the human need for social relatedness 16 , 155 . Social psychological evidence has shown that group formation takes place easily in any context – also online 2 , 26 , 156 , 157 . This has been shown in both the SIDE and IBRM 4 , 37 . Characteristics of online communication are tied to the mediated nature of the communication, but, with the help of advanced technologies, the line between on- and offline has become increasingly blurred. Today’s research evidence emphasizes the increasing significance of online communities in shaping social connections within both work and everyday life. However, the full extent of this impact is challenging to predict due to the rapid development of internet and social media platforms. Going forward, social psychological theory stands as a cornerstone in understanding the intricate mechanisms of online communities. However, it is crucial to maximise its significance by integrating and considering methodologies and findings from other disciplines of psychology.

In this Perspective, we focused on online communities at work, online hate communities, and online communities based on addiction, and how they contribute to both benefits and risks of human interaction, behavior, and well-being, and what implications such communities hold for the society at large. In the context of work, online communities can facilitate efficient collaboration, knowledge transfer, and social belonging. However, virtual workplace environments may also lead to exclusion, cyberbullying, psychological distress, and technology-induced technostress. Online hate communities pose a worrisome phenomenon, spreading extremist ideas, false information, and conspiracy theories. These activities can have real-world consequences, including increased distrust in institutions and offline deviant behavior. Additionally, online communities related to addiction impact users’ time, sleep, relationships, and finances. Despite challenges, online communities offer potential for intervention and support. Research in this multidisciplinary field is urgently relevant, considering technological, societal, and psychological aspects.

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Between-Subjects vs. Within-Subjects Study Design

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In a within-subject design, each participant experiences all experimental conditions, whereas, in a between-subject design, different participants are assigned to each condition, with each experiencing only one condition.

Within-subjects (or repeated-measures) is an experimental design in which all study participants are exposed to the same treatments or independent variable conditions.

In within-subjects studies, the participants are compared to one another, so there is no control group. The data comparison occurs within the group of study participants, and each participant serves as their own baseline.

In a between-subjects design (or between-groups, independent measures), the study participants are divided into groups, and each group is exposed to one treatment or condition.

Each participant is only assigned to a single treatment. This should be done by random allocation , ensuring that each participant has an equal chance of being assigned to one group.

The differences between the two groups are then compared to a control group that does not receive any treatment. The groups that undergo a treatment or condition are typically called the experimental groups.

• In a within-subjects design, all participants receive every treatment. 
• In a between-subjects design, participants only receive one treatment.

Design Similarities

• Both types of study designs tend to be used to assess the impact of a treatment or condition on a given study population.
• The goal of both types of studies is to compare several test conditions in a single study.
• Both within-subjects designs and between-subjects designs have a group of subjects that serve as study participants and are exposed to a given treatment.
• Both experimental designs are utilized in quantitative studies and aim to result in findings that are statistically likely to generalize to a whole population.
• Between-subjects and within-subjects design both have an independent variable that is manipulated or controlled by the study’s investigators and a dependent variable that is measured. 
• Random assignment is essential for both types of designs.

Design Differences

•  In a within-subjects design, all participants receive all treatments. In a between-subjects design, participants receive only one treatment.
• In a within-subjects design, the participants are compared to each other, so there is no control group. In a between-subjects design, there is a control group that doesn’t receive any treatment and serves as a source of comparison for the treatment groups. 
• Between-subjects designs require significantly more participants than within-subjects designs in order to detect a statistically significant difference between the two conditions. In within-subjects designs, on the other hand, fewer participants are required as each participant provides a data point for each level of the independent variable. A similar experiment in a between-subject design would require twice as many participants as a within-subject design. This means that they also require more resources and funding to recruit a larger sample, administer sessions, and cover costs.
• Between-subjects designs tend to be easier and quicker to administer as each participant is only given one treatment. In contrast, within-subjects designs take longer to implement because every participant is given multiple treatments.
• Within-subjects designs are vulnerable to fatigue and carryover effects. Participant fatigue occurs when participants become tired, bored, or unmotivated after taking part in multiple treatments in a row. Carryover effects are when the act of having participants take part in one condition impacts the performance or behavior on all other conditions. With between-subjects design, though, participants are exposed to fewer conditions, so fatigue and carryover effects are less of a challenge.
• Because different participants provide data for each condition in between-subjects designs, individual differences among participants may threaten internal validity. Within-subjects designs are less affected by individual differences among participants because the participants are compared to themselves and thus higher statistical power can be achieved.

What is a 2×2 within subject design?

A 2×2 within-subjects design is one in which there are two independent variables each having two different levels. This design allows researchers to understand the effects of two independent variables (each with two levels) on a single dependent variable.

When would you use a within-subjects design?

You typically would use a within-subjects design when you want to investigate a causal or correlational relationship between variables with a relatively small sample.

The primary goal of a within-subjects design is to determine if one treatment condition is more effective than another.

Within-subjects are typically used for longitudinal studies or observational studies conducted over an extended period.

When should a within-subjects design not be used?

A within-subjects design should not be used if researchers are concerned about the potential interferences of practice effects. 

If the researcher is interested in treatment effects under minimum practice, the within-subjects design is inappropriate because subjects are providing data for two of the three treatments under more than minimum practice.

When should you use a between-subjects design?

Between-subjects designs are used when you have multiple independent variables. This type of design enables researchers to determine if one treatment condition is superior to another.

A between-subjects design is also useful when you want to compare groups that differ on a key characteristic.

This key characteristic would be the independent variable, with varying levels of the characteristic differentiating the groups from each other.

When can a between-subjects design not be used?

Between-subjects cannot be used with small sample sizes because they will not be statistically powerful enough.

Between-subjects studies require at least twice as many participants as a within-subject design, which also means twice the cost and resources. When funding is limited,  between-subjects design can likely not be used.

Can I use a within- and between-subjects design in the same study?

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design).

Factorial designs are a type of experiment where multiple independent variables are tested.

Each level of one independent variable (a factor) is combined with each level of every other independent variable to produce different conditions.

Is between-subjects or within-subjects design more powerful?

Within-subjects designs have more statistical power due to the lack of variation between the individuals in the study because participants are compared to themselves.

A between-subjects design would require a large participant pool in order to reach a similar level of statistical significance as a within-subjects design.

Workers crave autonomy and flexibility. Here are ways to achieve that balance

Respecting personal boundaries, reducing work stress, and having predictable work schedules are among the steps both employees and employers can take to achieve work-life harmony

• Healthy Workplaces
• Managing Human Capital

Today’s employees want control over where they work—whether fully in-person, fully remote, or a hybrid approach—and they’re more satisfied when they are given the chance to decide, according to APA’s 2024 Work in America survey.  

Still, 1 in 3 workers (33%) said they do not have enough flexibility at work to keep their professional life and personal life in balance, the survey found. Employers and employees can take steps to increase workers’ autonomy and flexibility, which research has found improves work-life balance.

Here are tips from industrial-organizational psychologists and workplace experts Tammy Allen, PhD, a psychology professor at the University of South Florida in Tampa, and Mindy Shoss, PhD, a psychology professor at the University of Central Florida in Orlando.

How can employers offer more job autonomy? Mindy Shoss: Employers can give employees greater control over how tasks are performed, the scope and pace of work, the scheduling and location of work, and more. But it’s important that  job autonomy be provided  within the context of a supportive supervisor-employee relationship. That way, the expectations and goals of both the organization and worker can be openly discussed and navigated.

How can workers negotiate policies that are better for work-life harmony? Tammy Allen: Employees can advocate for themselves by discussing their needs with supervisors; they should emphasize how addressing these needs benefit the employee, the organization, and the bottom line. They can provide evidence that the support or accommodation, such as working remotely several days a week, will not harm productivity or team dynamics. If the supervisor is hesitant, ask for a trial period.

Only 25% of workers in manual labor jobs say their employers offer a culture where time off is respected, compared with 48% of office workers. How can employers address this issue with blue-collar workers? Mindy Shoss: Schedules should be predictable and flexible. Work schedules should be planned far enough in advance for any worker to coordinate other aspects of their life.  Income should be relatively stable . Organizations can also look for ways to help employees fulfill personal needs at work. For example,  Rosen Hotels and Resorts  offers an advanced medical center for its employees and other initiatives on-site so that workers can more easily address their health, fitness, and well-being needs.

What can employees do if their supervisor expects them to check email on their personal time and not take vacation when they want to? Tammy Allen: Expectations should be aligned with the nature of the job and based on a mutual understanding between the employee and the boss. If the understanding is violated (for example, you’ve agreed on no emails on Sundays but the boss consistently expects emails to be answered on Sundays), then a discussion about realignment may be needed. You can discuss the  importance of establishing boundaries  and the importance of detachment from work to enable  recovery from work for your health and well-being . That allows you to be at your best for the organization.

What kind of boundaries can employers set to better respect workers’ free time? Mindy Shoss: Working with employees to find solutions that best fit the  individual’s, team’s, and leader’s needs  can result in new norms around when, and for what purpose, it’s acceptable to send emails or text messages, for example. As with most things, open, respectful, and bidirectional communication , and creative problem solving are key.

What mental health strategies can employees use to help minimize work stress overall? Tammy Allen:   Mindfulness-based stress reduction practices such as yoga, meditation, and deep breathing have been shown to help reduce employee work stress and enhance employee health.

Further reading

Work-family research: A review and next steps Allen, T. D., & French, K. A., Personnel Psychology , 2022

Clarifying work–family intervention processes: The roles of work–family conflict and family-supportive supervisor behaviors Hammer, L. B., et al. Journal of Applied Psychology , 2011

Precarious work schedules as a source of economic insecurity and institutional distrust Lambert, S. J., et al.  RSF: The Russell Sage Foundation Journal of the Social Sciences , 2019

Recommended Reading

Work in america 2024.

• U.S. workers adjust to the changing nature of employment
• Psychological safety in the changing workplace

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Experimental Design

Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton

Learning Objectives

• Explain the difference between between-subjects and within-subjects experiments, list some of the pros and cons of each approach, and decide which approach to use to answer a particular research question.
• Define random assignment, distinguish it from random sampling, explain its purpose in experimental research, and use some simple strategies to implement it
• Define several types of carryover effect, give examples of each, and explain how counterbalancing helps to deal with them.

In this section, we look at some different ways to design an experiment. The primary distinction we will make is between approaches in which each participant experiences one level of the independent variable and approaches in which each participant experiences all levels of the independent variable. The former are called between-subjects experiments and the latter are called within-subjects experiments.

Between-Subjects Experiments

In a  between-subjects experiment , each participant is tested in only one condition. For example, a researcher with a sample of 100 university students might assign half of them to write about a traumatic event and the other half write about a neutral event. Or a researcher with a sample of 60 people with severe agoraphobia (fear of open spaces) might assign 20 of them to receive each of three different treatments for that disorder. It is essential in a between-subjects experiment that the researcher assigns participants to conditions so that the different groups are, on average, highly similar to each other. Those in a trauma condition and a neutral condition, for example, should include a similar proportion of men and women, and they should have similar average IQs, similar average levels of motivation, similar average numbers of health problems, and so on. This matching is a matter of controlling these extraneous participant variables across conditions so that they do not become confounding variables.

Random Assignment

The primary way that researchers accomplish this kind of control of extraneous variables across conditions is called  random assignment , which means using a random process to decide which participants are tested in which conditions. Do not confuse random assignment with random sampling. Random sampling is a method for selecting a sample from a population, and it is rarely used in psychological research. Random assignment is a method for assigning participants in a sample to the different conditions, and it is an important element of all experimental research in psychology and other fields too.

In its strictest sense, random assignment should meet two criteria. One is that each participant has an equal chance of being assigned to each condition (e.g., a 50% chance of being assigned to each of two conditions). The second is that each participant is assigned to a condition independently of other participants. Thus one way to assign participants to two conditions would be to flip a coin for each one. If the coin lands heads, the participant is assigned to Condition A, and if it lands tails, the participant is assigned to Condition B. For three conditions, one could use a computer to generate a random integer from 1 to 3 for each participant. If the integer is 1, the participant is assigned to Condition A; if it is 2, the participant is assigned to Condition B; and if it is 3, the participant is assigned to Condition C. In practice, a full sequence of conditions—one for each participant expected to be in the experiment—is usually created ahead of time, and each new participant is assigned to the next condition in the sequence as they are tested. When the procedure is computerized, the computer program often handles the random assignment.

One problem with coin flipping and other strict procedures for random assignment is that they are likely to result in unequal sample sizes in the different conditions. Unequal sample sizes are generally not a serious problem, and you should never throw away data you have already collected to achieve equal sample sizes. However, for a fixed number of participants, it is statistically most efficient to divide them into equal-sized groups. It is standard practice, therefore, to use a kind of modified random assignment that keeps the number of participants in each group as similar as possible. One approach is block randomization . In block randomization, all the conditions occur once in the sequence before any of them is repeated. Then they all occur again before any of them is repeated again. Within each of these “blocks,” the conditions occur in a random order. Again, the sequence of conditions is usually generated before any participants are tested, and each new participant is assigned to the next condition in the sequence.  Table 5.2  shows such a sequence for assigning nine participants to three conditions. The Research Randomizer website ( http://www.randomizer.org ) will generate block randomization sequences for any number of participants and conditions. Again, when the procedure is computerized, the computer program often handles the block randomization.

Random assignment is not guaranteed to control all extraneous variables across conditions. The process is random, so it is always possible that just by chance, the participants in one condition might turn out to be substantially older, less tired, more motivated, or less depressed on average than the participants in another condition. However, there are some reasons that this possibility is not a major concern. One is that random assignment works better than one might expect, especially for large samples. Another is that the inferential statistics that researchers use to decide whether a difference between groups reflects a difference in the population takes the “fallibility” of random assignment into account. Yet another reason is that even if random assignment does result in a confounding variable and therefore produces misleading results, this confound is likely to be detected when the experiment is replicated. The upshot is that random assignment to conditions—although not infallible in terms of controlling extraneous variables—is always considered a strength of a research design.

Matched Groups

An alternative to simple random assignment of participants to conditions is the use of a matched-groups design . Using this design, participants in the various conditions are matched on the dependent variable or on some extraneous variable(s) prior the manipulation of the independent variable. This guarantees that these variables will not be confounded across the experimental conditions. For instance, if we want to determine whether expressive writing affects people’s health then we could start by measuring various health-related variables in our prospective research participants. We could then use that information to rank-order participants according to how healthy or unhealthy they are. Next, the two healthiest participants would be randomly assigned to complete different conditions (one would be randomly assigned to the traumatic experiences writing condition and the other to the neutral writing condition). The next two healthiest participants would then be randomly assigned to complete different conditions, and so on until the two least healthy participants. This method would ensure that participants in the traumatic experiences writing condition are matched to participants in the neutral writing condition with respect to health at the beginning of the study. If at the end of the experiment, a difference in health was detected across the two conditions, then we would know that it is due to the writing manipulation and not to pre-existing differences in health.

Within-Subjects Experiments

In a  within-subjects experiment , each participant is tested under all conditions. Consider an experiment on the effect of a defendant’s physical attractiveness on judgments of his guilt. Again, in a between-subjects experiment, one group of participants would be shown an attractive defendant and asked to judge his guilt, and another group of participants would be shown an unattractive defendant and asked to judge his guilt. In a within-subjects experiment, however, the same group of participants would judge the guilt of both an attractive  and  an unattractive defendant.

The primary advantage of this approach is that it provides maximum control of extraneous participant variables. Participants in all conditions have the same mean IQ, same socioeconomic status, same number of siblings, and so on—because they are the very same people. Within-subjects experiments also make it possible to use statistical procedures that remove the effect of these extraneous participant variables on the dependent variable and therefore make the data less “noisy” and the effect of the independent variable easier to detect. We will look more closely at this idea later in the book .  However, not all experiments can use a within-subjects design nor would it be desirable to do so.

Carryover Effects and Counterbalancing

The primary disadvantage of within-subjects designs is that they can result in order effects. An order effect   occurs when participants’ responses in the various conditions are affected by the order of conditions to which they were exposed. One type of order effect is a carryover effect. A  carryover effect  is an effect of being tested in one condition on participants’ behavior in later conditions. One type of carryover effect is a  practice effect , where participants perform a task better in later conditions because they have had a chance to practice it. Another type is a fatigue effect , where participants perform a task worse in later conditions because they become tired or bored. Being tested in one condition can also change how participants perceive stimuli or interpret their task in later conditions. This  type of effect is called a  context effect (or contrast effect) . For example, an average-looking defendant might be judged more harshly when participants have just judged an attractive defendant than when they have just judged an unattractive defendant. Within-subjects experiments also make it easier for participants to guess the hypothesis. For example, a participant who is asked to judge the guilt of an attractive defendant and then is asked to judge the guilt of an unattractive defendant is likely to guess that the hypothesis is that defendant attractiveness affects judgments of guilt. This knowledge could lead the participant to judge the unattractive defendant more harshly because he thinks this is what he is expected to do. Or it could make participants judge the two defendants similarly in an effort to be “fair.”

Carryover effects can be interesting in their own right. (Does the attractiveness of one person depend on the attractiveness of other people that we have seen recently?) But when they are not the focus of the research, carryover effects can be problematic. Imagine, for example, that participants judge the guilt of an attractive defendant and then judge the guilt of an unattractive defendant. If they judge the unattractive defendant more harshly, this might be because of his unattractiveness. But it could be instead that they judge him more harshly because they are becoming bored or tired. In other words, the order of the conditions is a confounding variable. The attractive condition is always the first condition and the unattractive condition the second. Thus any difference between the conditions in terms of the dependent variable could be caused by the order of the conditions and not the independent variable itself.

There is a solution to the problem of order effects, however, that can be used in many situations. It is  counterbalancing , which means testing different participants in different orders. The best method of counterbalancing is complete counterbalancing   in which an equal number of participants complete each possible order of conditions. For example, half of the participants would be tested in the attractive defendant condition followed by the unattractive defendant condition, and others half would be tested in the unattractive condition followed by the attractive condition. With three conditions, there would be six different orders (ABC, ACB, BAC, BCA, CAB, and CBA), so some participants would be tested in each of the six orders. With four conditions, there would be 24 different orders; with five conditions there would be 120 possible orders. With counterbalancing, participants are assigned to orders randomly, using the techniques we have already discussed. Thus, random assignment plays an important role in within-subjects designs just as in between-subjects designs. Here, instead of randomly assigning to conditions, they are randomly assigned to different orders of conditions. In fact, it can safely be said that if a study does not involve random assignment in one form or another, it is not an experiment.

A more efficient way of counterbalancing is through a Latin square design which randomizes through having equal rows and columns. For example, if you have four treatments, you must have four versions. Like a Sudoku puzzle, no treatment can repeat in a row or column. For four versions of four treatments, the Latin square design would look like:

You can see in the diagram above that the square has been constructed to ensure that each condition appears at each ordinal position (A appears first once, second once, third once, and fourth once) and each condition precedes and follows each other condition one time. A Latin square for an experiment with 6 conditions would by 6 x 6 in dimension, one for an experiment with 8 conditions would be 8 x 8 in dimension, and so on. So while complete counterbalancing of 6 conditions would require 720 orders, a Latin square would only require 6 orders.

Finally, when the number of conditions is large experiments can use  random counterbalancing  in which the order of the conditions is randomly determined for each participant. Using this technique every possible order of conditions is determined and then one of these orders is randomly selected for each participant. This is not as powerful a technique as complete counterbalancing or partial counterbalancing using a Latin squares design. Use of random counterbalancing will result in more random error, but if order effects are likely to be small and the number of conditions is large, this is an option available to researchers.

There are two ways to think about what counterbalancing accomplishes. One is that it controls the order of conditions so that it is no longer a confounding variable. Instead of the attractive condition always being first and the unattractive condition always being second, the attractive condition comes first for some participants and second for others. Likewise, the unattractive condition comes first for some participants and second for others. Thus any overall difference in the dependent variable between the two conditions cannot have been caused by the order of conditions. A second way to think about what counterbalancing accomplishes is that if there are carryover effects, it makes it possible to detect them. One can analyze the data separately for each order to see whether it had an effect.

When 9 Is “Larger” Than 221

Researcher Michael Birnbaum has argued that the  lack  of context provided by between-subjects designs is often a bigger problem than the context effects created by within-subjects designs. To demonstrate this problem, he asked participants to rate two numbers on how large they were on a scale of 1-to-10 where 1 was “very very small” and 10 was “very very large”.  One group of participants were asked to rate the number 9 and another group was asked to rate the number 221 (Birnbaum, 1999) [1] . Participants in this between-subjects design gave the number 9 a mean rating of 5.13 and the number 221 a mean rating of 3.10. In other words, they rated 9 as larger than 221! According to Birnbaum, this  difference  is because participants spontaneously compared 9 with other one-digit numbers (in which case it is  relatively large) and compared 221 with other three-digit numbers (in which case it is relatively  small).

Simultaneous Within-Subjects Designs

So far, we have discussed an approach to within-subjects designs in which participants are tested in one condition at a time. There is another approach, however, that is often used when participants make multiple responses in each condition. Imagine, for example, that participants judge the guilt of 10 attractive defendants and 10 unattractive defendants. Instead of having people make judgments about all 10 defendants of one type followed by all 10 defendants of the other type, the researcher could present all 20 defendants in a sequence that mixed the two types. The researcher could then compute each participant’s mean rating for each type of defendant. Or imagine an experiment designed to see whether people with social anxiety disorder remember negative adjectives (e.g., “stupid,” “incompetent”) better than positive ones (e.g., “happy,” “productive”). The researcher could have participants study a single list that includes both kinds of words and then have them try to recall as many words as possible. The researcher could then count the number of each type of word that was recalled. 

Between-Subjects or Within-Subjects?

Almost every experiment can be conducted using either a between-subjects design or a within-subjects design. This possibility means that researchers must choose between the two approaches based on their relative merits for the particular situation.

Between-subjects experiments have the advantage of being conceptually simpler and requiring less testing time per participant. They also avoid carryover effects without the need for counterbalancing. Within-subjects experiments have the advantage of controlling extraneous participant variables, which generally reduces noise in the data and makes it easier to detect any effect of the independent variable upon the dependent variable. Within-subjects experiments also require fewer participants than between-subjects experiments to detect an effect of the same size.

A good rule of thumb, then, is that if it is possible to conduct a within-subjects experiment (with proper counterbalancing) in the time that is available per participant—and you have no serious concerns about carryover effects—this design is probably the best option. If a within-subjects design would be difficult or impossible to carry out, then you should consider a between-subjects design instead. For example, if you were testing participants in a doctor’s waiting room or shoppers in line at a grocery store, you might not have enough time to test each participant in all conditions and therefore would opt for a between-subjects design. Or imagine you were trying to reduce people’s level of prejudice by having them interact with someone of another race. A within-subjects design with counterbalancing would require testing some participants in the treatment condition first and then in a control condition. But if the treatment works and reduces people’s level of prejudice, then they would no longer be suitable for testing in the control condition. This difficulty is true for many designs that involve a treatment meant to produce long-term change in participants’ behavior (e.g., studies testing the effectiveness of psychotherapy). Clearly, a between-subjects design would be necessary here.

Remember also that using one type of design does not preclude using the other type in a different study. There is no reason that a researcher could not use both a between-subjects design and a within-subjects design to answer the same research question. In fact, professional researchers often take exactly this type of mixed methods approach.

• Birnbaum, M.H. (1999). How to show that 9>221: Collect judgments in a between-subjects design. Psychological Methods, 4 (3), 243-249. ↵

An experiment in which each participant is tested in only one condition.

Means using a random process to decide which participants are tested in which conditions.

All the conditions occur once in the sequence before any of them is repeated.

An experiment design in which the participants in the various conditions are matched on the dependent variable or on some extraneous variable(s) prior the manipulation of the independent variable.

An experiment in which each participant is tested under all conditions.

An effect that occurs when participants' responses in the various conditions are affected by the order of conditions to which they were exposed.

An effect of being tested in one condition on participants’ behavior in later conditions.

An effect where participants perform a task better in later conditions because they have had a chance to practice it.

An effect where participants perform a task worse in later conditions because they become tired or bored.

Unintended influences on respondents’ answers because they are not related to the content of the item but to the context in which the item appears.

Varying the order of the conditions in which participants are tested, to help solve the problem of order effects in within-subjects experiments.

A method in which an equal number of participants complete each possible order of conditions. 

A method in which the order of the conditions is randomly determined for each participant.

Experimental Design Copyright © by Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Aug 6, 2024, 2:46 PM

This fall, Vanderbilt Peabody College of education and human development seeks to strengthen and advance its global reach. The Peabody Research Office will expand its mission and strategy to include Peabody Global Initiatives. Peabody Global Initiatives will support faculty to pursue international research and disseminate knowledge as well as develop impactful partnerships and networks through a culturally and contextually responsive, collaborative, and data-driven approach.

“Peabody Global Initiatives reflects our commitment to crossing geographical boundaries through research and partnerships in education and human development,” said Ellen Goldring , Patricia and Rodes Hart Professor of Education and Leadership and vice dean of Peabody College. “As we continue to expand on our recent global engagement successes we will further support faculty to collaborate around the world to develop and research policy and practice that strengthens learning and human development.”

The establishment of the new office dovetails with two Peabody faculty members beginning research collaborations supported by Vanderbilt Global Engagement Research Seed Grants . These grants provide financial support to new and innovative faculty research endeavors that involve global engagement. They are meant to seed projects with larger funding capacities.

Jason Chow , associate professor of special education, is leading efforts to establish the infrastructure for a collaborative focused on synthesizing implementation science research across the globe. Implementation science is the study of the best methods and strategies for ensuring evidence-based approaches are applied by practitioners and policy makers to improve people’s lives. Chow and colleagues at Monash University and the Centre for Evidence and Implementation, both in Australia, and the National University of Singapore plan to identify the collaborative’s resources and needs and to develop its initial infrastructure. Their goal is to improve the sustained use of evidence in global practices and policies. As part of this project, his team plans to develop workflows for producing context-specific research reviews for practitioners and policymakers. They will collaborate with local community partners to ensure the results and recommendations from evidence syntheses are tailored to each nation’s local contexts.

Xiu Cravens , professor of the practice of education policy and leadership, is spearheading a new collaboration to advance teacher development in the Asia-Pacific region through improvement science. Improvement science is a problem-solving approach to develop, adapt, contextualize, and scale innovations that improve educational processes and outcomes. The seed grant will support Cravens’ review of peer practices and help identify research partners with the support from four regional learning hubs: the National Institute of Education in Singapore, the Center for Educational Research and Innovation at National Taiwan Normal University, the National Training Center for Secondary Principals at East China Normal University, and the Asia Pacific Centre for Leadership and Change at the Education University of Hong Kong. Working together, they will pursue additional funding for multi-country collaborations and capacity building through research-practice partnerships. The seed grant will also lay the foundation for the Peabody-Asia Consortium for Education, providing a platform for Vanderbilt researchers to engage with academic and professional institutions and individuals in the region.

These awards and the newly established Peabody Global Initiatives reflect Peabody’s commitment to worldwide collaboration that enhances learning and development in diverse contexts and translates discoveries into more effective practice and policy.

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