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Correlational Research in Psychology: Definition and How It Works
Correlational research is a type of scientific investigation in which a researcher looks at the relationships between variables but does not vary, manipulate, or control them. It can be a useful research method for evaluating the direction and strength of the relationship between two or more different variables. When examining how variables are related to…
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Correlational research is a type of scientific investigation in which a researcher looks at the relationships between variables but does not vary, manipulate, or control them. It can be a useful research method for evaluating the direction and strength of the relationship between two or more different variables.
When examining how variables are related to one another, researchers may find that the relationship is positive or negative. Or they may also find that there is no relationship at all.
How Does Correlational Research Work?
In correlational research, the researcher measures the values of the variables of interest and calculates a correlation coefficient, which quantifies the strength and direction of the relationship between the variables.
The correlation coefficient ranges from -1.0 to +1.0, where -1.0 represents a perfect negative correlation, 0 represents no correlation, and +1.0 represents a perfect positive correlation.
A negative correlation indicates that as the value of one variable increases, the value of the other variable decreases, while a positive correlation indicates that as the value of one variable increases, the value of the other variable also increases. A zero correlation indicates that there is no relationship between the variables.
Correlational Research vs. Experimental Research
Correlational research differs from experimental research in that it does not involve manipulating variables. Instead, it focuses on analyzing the relationship between two or more variables.
In other words, correlational research seeks to determine whether there is a relationship between two variables and, if so, the nature of that relationship.
Experimental research, on the other hand, involves manipulating one or more variables to determine the effect on another variable. Because of this manipulation and control of variables, experimental research allows for causal conclusions to be drawn, while correlational research does not.
Both types of research are important in understanding the world around us, but they serve different purposes and are used in different situations.
Types of Correlational Research
There are three main types of correlational studies:
Cohort Correlational Study
This type of study involves following a cohort of participants over a period of time. This type of research can be useful for understanding how certain events might influence outcomes.
For example, researchers might study how exposure to a traumatic natural disaster influences the mental health of a group of people over time.
By examining the data collected from these individuals, researchers can determine whether there is a correlation between the two variables under investigation. This information can be used to develop strategies for preventing or treating certain conditions or illnesses.
Cross-Sectional Correlational Study
A cross-sectional design is a research method that examines a group of individuals at a single time. This type of study collects information from a diverse group of people, usually from different backgrounds and age groups, to gain insight into a particular phenomenon or issue.
The data collected from this type of study is used to analyze relationships between variables and identify patterns and trends within the group.
Cross-sectional studies can help identify potential risk factors for certain conditions or illnesses, and can also be used to evaluate the prevalence of certain behaviors, attitudes, or beliefs within a population.
Case-Control Correlational Study
A case-control correlational study is a type of research design that investigates the relationship between exposure and health outcomes. In this study, researchers identify a group of individuals with the health outcome of interest (cases) and another group of individuals without the health outcome (controls).
The researchers then compare the exposure history of the cases and controls to determine whether the exposure and health outcome correlate.
This type of study design is often used in epidemiology and can provide valuable information about potential risk factors for a particular disease or condition.
When to Use Correlational Research
There are a number of situations where researchers might opt to use a correlational study instead of some other research design.
Correlational research can be used to investigate a wide range of psychological phenomena, including the relationship between personality traits and academic performance, the association between sleep duration and mental health, and the correlation between parental involvement and child outcomes.
To Generate Hypotheses
Correlational research can also be used to generate hypotheses for further research by identifying variables that are associated with each other.
To Investigate Variables Without Manipulating Them
Researchers should use correlational research when they want to investigate the relationship between two variables without manipulating them. This type of research is useful when the researcher cannot or should not manipulate one of the variables or when it is impossible to conduct an experiment due to ethical or practical concerns.
To Identify Patterns
Correlational research allows researchers to identify patterns and relationships between variables, which can inform future research and help to develop theories. However, it is important to note that correlational research does not prove that one variable causes changes in the other.
While correlational research has its limitations, it is still a valuable tool for researchers in many fields, including psychology, sociology, and education.
How to Collect Data in Correlational Research
Researchers can collect data for correlational research in a few different ways. To conduct correlational research, data can be collected using the following:
- Surveys : One method is through surveys, where participants are asked to self-report their behaviors or attitudes. This approach allows researchers to gather large amounts of data quickly and affordably.
- Naturalistic observation : Another method is through observation, where researchers observe and record behaviors in a natural or controlled setting. This method allows researchers to learn more about the behavior in question and better generalize the results to real-world settings.
- Archival, retrospective data : Additionally, researchers can collect data from archival sources, such as medical, school records, official records, or past polls.
The key is to collect data from a large and representative sample to measure the relationship between two variables accurately.
Pros and Cons of Correlational Research
There are some advantages of using correlational research, but there are also some downsides to consider.
- One of the strengths of correlational research is its ability to identify patterns and relationships between variables that may be difficult or unethical to manipulate in an experimental study.
- Correlational research can also be used to examine variables that are not under the control of the researcher , such as age, gender, or socioeconomic status.
- Correlational research can be used to make predictions about future behavior or outcomes, which can be valuable in a variety of fields.
- Correlational research can be conducted quickly and inexpensively , making it a practical option for researchers with limited resources.
- Correlational research is limited by its inability to establish causality between variables. Correlation does not imply causation, and it is possible that a third variable may be influencing both variables of interest, creating a spurious correlation. Therefore, it is important for researchers to use multiple methods of data collection and to be cautious when interpreting correlational findings.
- Correlational research relies heavily on self-reported data , which can be biased or inaccurate.
- Correlational research is limited in its ability to generalize findings to larger populations, as it only measures the relationship between two variables in a specific sample.
Frequently Asked Questions About Correlational Research
What are the main problems with correlational research.
Some of the main problems that can occur in correlational research include selection bias, confounding variables. and misclassification.
- Selecting participants based on their exposure to an event means that the sample might be biased since the selection was not randomized.
- Correlational studies may also be impacted by extraneous factors that researchers cannot control.
- Finally, there may be problems with how accurately data is recorded and classified, which can be particularly problematic in retrospective studies.
What are the variables in a correlational study?
In a correlational study, variables refer to any measurable factors being examined for their potential relationship or association with each other. These variables can be continuous (meaning they can take on a range of values) or categorical (meaning they fall into distinct categories or groups).
For example, in a study examining the correlation between exercise and mental health, the independent variable would be exercise frequency (measured in times per week), while the dependent variable would be mental health (measured using a standardized questionnaire).
What is the goal of correlational research?
The goal of correlational research is to examine the relationship between two or more variables. It involves analyzing data to determine if there is a statistically significant connection between the variables being studied.
Correlational research is useful for identifying patterns and making predictions but cannot establish causation. Instead, it helps researchers to better understand the nature of the relationship between variables and to generate hypotheses for further investigation.
How do you identify correlational research?
To identify correlational research, look for studies that measure two or more variables and analyze their relationship using statistical techniques. The results of correlational studies are typically presented in the form of correlation coefficients or scatterplots, which visually represent the degree of association between the variables being studied.
Correlational research can be useful for identifying potential causal relationships between variables but cannot establish causation on its own.
Curtis EA, Comiskey C, Dempsey O. Importance and use of correlational research . Nurse Researcher . 2016;23(6):20-25. doi10.7748/nr.2016.e1382
Lau F. Chapter 12 Methods for Correlational Studies . University of Victoria; 2017.
Mitchell TR. An evaluation of the validity of correlational research conducted in organizations . The Academy of Management Review . 1985;10(2):192. doi:10.5465/amr.1985.4277939
Seeram E. An overview of correlational research . Radiol Technol . 2019;91(2):176-179.
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Kendra Cherry, MS.Ed., is a writer, editor, psychosocial therapist, and founder of Explore Psychology, an online psychology resource. She is a Senior Writer for Verywell Mind and is the author of the Everything Psychology Book (Adams Media).
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Correlation in Psychology: Meaning, Types, Examples & coefficient
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Correlation means association – more precisely, it measures the extent to which two variables are related. There are three possible results of a correlational study: a positive correlation, a negative correlation, and no correlation.
- A positive correlation is a relationship between two variables in which both variables move in the same direction. Therefore, one variable increases as the other variable increases, or one variable decreases while the other decreases. An example of a positive correlation would be height and weight. Taller people tend to be heavier.
- A negative correlation is a relationship between two variables in which an increase in one variable is associated with a decrease in the other. An example of a negative correlation would be the height above sea level and temperature. As you climb the mountain (increase in height), it gets colder (decrease in temperature).
- A zero correlation exists when there is no relationship between two variables. For example, there is no relationship between the amount of tea drunk and the level of intelligence.
Scatter Plots
A correlation can be expressed visually. This is done by drawing a scatter plot (also known as a scattergram, scatter graph, scatter chart, or scatter diagram).
A scatter plot is a graphical display that shows the relationships or associations between two numerical variables (or co-variables), which are represented as points (or dots) for each pair of scores.
A scatter plot indicates the strength and direction of the correlation between the co-variables.
When you draw a scatter plot, it doesn’t matter which variable goes on the x-axis and which goes on the y-axis.
Remember, in correlations, we always deal with paired scores, so the values of the two variables taken together will be used to make the diagram.
Decide which variable goes on each axis and then simply put a cross at the point where the two values coincide.
Uses of Correlations
- If there is a relationship between two variables, we can make predictions about one from another.
- Concurrent validity (correlation between a new measure and an established measure).
Reliability
- Test-retest reliability (are measures consistent?).
- Inter-rater reliability (are observers consistent?).
Theory verification
- Predictive validity.
Correlation Coefficients
Instead of drawing a scatter plot, a correlation can be expressed numerically as a coefficient, ranging from -1 to +1. When working with continuous variables, the correlation coefficient to use is Pearson’s r.
The correlation coefficient ( r ) indicates the extent to which the pairs of numbers for these two variables lie on a straight line. Values over zero indicate a positive correlation, while values under zero indicate a negative correlation.
A correlation of –1 indicates a perfect negative correlation, meaning that as one variable goes up, the other goes down. A correlation of +1 indicates a perfect positive correlation, meaning that as one variable goes up, the other goes up.
There is no rule for determining what correlation size is considered strong, moderate, or weak. The interpretation of the coefficient depends on the topic of study.
When studying things that are difficult to measure, we should expect the correlation coefficients to be lower (e.g., above 0.4 to be relatively strong). When we are studying things that are easier to measure, such as socioeconomic status, we expect higher correlations (e.g., above 0.75 to be relatively strong).)
In these kinds of studies, we rarely see correlations above 0.6. For this kind of data, we generally consider correlations above 0.4 to be relatively strong; correlations between 0.2 and 0.4 are moderate, and those below 0.2 are considered weak.
When we are studying things that are more easily countable, we expect higher correlations. For example, with demographic data, we generally consider correlations above 0.75 to be relatively strong; correlations between 0.45 and 0.75 are moderate, and those below 0.45 are considered weak.
Correlation vs. Causation
Causation means that one variable (often called the predictor variable or independent variable) causes the other (often called the outcome variable or dependent variable).
Experiments can be conducted to establish causation. An experiment isolates and manipulates the independent variable to observe its effect on the dependent variable and controls the environment in order that extraneous variables may be eliminated.
A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.
While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable , is actually causing the systematic movement in our variables of interest.
Correlation does not always prove causation, as a third variable may be involved. For example, being a patient in a hospital is correlated with dying, but this does not mean that one event causes the other, as another third variable might be involved (such as diet and level of exercise).
“Correlation is not causation” means that just because two variables are related it does not necessarily mean that one causes the other.
A correlation identifies variables and looks for a relationship between them. An experiment tests the effect that an independent variable has upon a dependent variable but a correlation looks for a relationship between two variables.
This means that the experiment can predict cause and effect (causation) but a correlation can only predict a relationship, as another extraneous variable may be involved that it not known about.
1. Correlation allows the researcher to investigate naturally occurring variables that may be unethical or impractical to test experimentally. For example, it would be unethical to conduct an experiment on whether smoking causes lung cancer.
2 . Correlation allows the researcher to clearly and easily see if there is a relationship between variables. This can then be displayed in a graphical form.
Limitations
1 . Correlation is not and cannot be taken to imply causation. Even if there is a very strong association between two variables, we cannot assume that one causes the other.
For example, suppose we found a positive correlation between watching violence on T.V. and violent behavior in adolescence.
It could be that the cause of both these is a third (extraneous) variable – for example, growing up in a violent home – and that both the watching of T.V. and the violent behavior is the outcome of this.
2 . Correlation does not allow us to go beyond the given data. For example, suppose it was found that there was an association between time spent on homework (1/2 hour to 3 hours) and the number of G.C.S.E. passes (1 to 6).
It would not be legitimate to infer from this that spending 6 hours on homework would likely generate 12 G.C.S.E. passes.
How do you know if a study is correlational?
A study is considered correlational if it examines the relationship between two or more variables without manipulating them. In other words, the study does not involve the manipulation of an independent variable to see how it affects a dependent variable.
One way to identify a correlational study is to look for language that suggests a relationship between variables rather than cause and effect.
For example, the study may use phrases like “associated with,” “related to,” or “predicts” when describing the variables being studied.
Another way to identify a correlational study is to look for information about how the variables were measured. Correlational studies typically involve measuring variables using self-report surveys, questionnaires, or other measures of naturally occurring behavior.
Finally, a correlational study may include statistical analyses such as correlation coefficients or regression analyses to examine the strength and direction of the relationship between variables.
Why is a correlational study used?
Correlational studies are particularly useful when it is not possible or ethical to manipulate one of the variables.
For example, it would not be ethical to manipulate someone’s age or gender. However, researchers may still want to understand how these variables relate to outcomes such as health or behavior.
Additionally, correlational studies can be used to generate hypotheses and guide further research.
If a correlational study finds a significant relationship between two variables, this can suggest a possible causal relationship that can be further explored in future research.
What is the goal of correlational research?
The ultimate goal of correlational research is to increase our understanding of how different variables are related and to identify patterns in those relationships.
This information can then be used to generate hypotheses and guide further research aimed at establishing causality.
6.2 Correlational Research
Learning objectives.
- Define correlational research and give several examples.
- Explain why a researcher might choose to conduct correlational research rather than experimental research or another type of non-experimental research.
- Interpret the strength and direction of different correlation coefficients.
- Explain why correlation does not imply causation.
What Is Correlational Research?
Correlational research is a type of non-experimental research in which the researcher measures two variables and assesses the statistical relationship (i.e., the correlation) between them with little or no effort to control extraneous variables. There are many reasons that researchers interested in statistical relationships between variables would choose to conduct a correlational study rather than an experiment. The first is that they do not believe that the statistical relationship is a causal one or are not interested in causal relationships. Recall two goals of science are to describe and to predict and the correlational research strategy allows researchers to achieve both of these goals. Specifically, this strategy can be used to describe the strength and direction of the relationship between two variables and if there is a relationship between the variables then the researchers can use scores on one variable to predict scores on the other (using a statistical technique called regression).
Another reason that researchers would choose to use a correlational study rather than an experiment is that the statistical relationship of interest is thought to be causal, but the researcher cannot manipulate the independent variable because it is impossible, impractical, or unethical. For example, while I might be interested in the relationship between the frequency people use cannabis and their memory abilities I cannot ethically manipulate the frequency that people use cannabis. As such, I must rely on the correlational research strategy; I must simply measure the frequency that people use cannabis and measure their memory abilities using a standardized test of memory and then determine whether the frequency people use cannabis use is statistically related to memory test performance.
Correlation is also used to establish the reliability and validity of measurements. For example, a researcher might evaluate the validity of a brief extraversion test by administering it to a large group of participants along with a longer extraversion test that has already been shown to be valid. This researcher might then check to see whether participants’ scores on the brief test are strongly correlated with their scores on the longer one. Neither test score is thought to cause the other, so there is no independent variable to manipulate. In fact, the terms independent variable and dependent variabl e do not apply to this kind of research.
Another strength of correlational research is that it is often higher in external validity than experimental research. Recall there is typically a trade-off between internal validity and external validity. As greater controls are added to experiments, internal validity is increased but often at the expense of external validity. In contrast, correlational studies typically have low internal validity because nothing is manipulated or control but they often have high external validity. Since nothing is manipulated or controlled by the experimenter the results are more likely to reflect relationships that exist in the real world.
Finally, extending upon this trade-off between internal and external validity, correlational research can help to provide converging evidence for a theory. If a theory is supported by a true experiment that is high in internal validity as well as by a correlational study that is high in external validity then the researchers can have more confidence in the validity of their theory. As a concrete example, correlational studies establishing that there is a relationship between watching violent television and aggressive behavior have been complemented by experimental studies confirming that the relationship is a causal one (Bushman & Huesmann, 2001) [1] . These converging results provide strong evidence that there is a real relationship (indeed a causal relationship) between watching violent television and aggressive behavior.
Data Collection in Correlational Research
Again, the defining feature of correlational research is that neither variable is manipulated. It does not matter how or where the variables are measured. A researcher could have participants come to a laboratory to complete a computerized backward digit span task and a computerized risky decision-making task and then assess the relationship between participants’ scores on the two tasks. Or a researcher could go to a shopping mall to ask people about their attitudes toward the environment and their shopping habits and then assess the relationship between these two variables. Both of these studies would be correlational because no independent variable is manipulated.
Correlations Between Quantitative Variables
Correlations between quantitative variables are often presented using scatterplots . Figure 6.3 shows some hypothetical data on the relationship between the amount of stress people are under and the number of physical symptoms they have. Each point in the scatterplot represents one person’s score on both variables. For example, the circled point in Figure 6.3 represents a person whose stress score was 10 and who had three physical symptoms. Taking all the points into account, one can see that people under more stress tend to have more physical symptoms. This is a good example of a positive relationship , in which higher scores on one variable tend to be associated with higher scores on the other. A negative relationship is one in which higher scores on one variable tend to be associated with lower scores on the other. There is a negative relationship between stress and immune system functioning, for example, because higher stress is associated with lower immune system functioning.
Figure 6.3 Scatterplot Showing a Hypothetical Positive Relationship Between Stress and Number of Physical Symptoms. The circled point represents a person whose stress score was 10 and who had three physical symptoms. Pearson’s r for these data is +.51.
The strength of a correlation between quantitative variables is typically measured using a statistic called Pearson’s Correlation Coefficient (or Pearson’s r ) . As Figure 6.4 shows, Pearson’s r ranges from −1.00 (the strongest possible negative relationship) to +1.00 (the strongest possible positive relationship). A value of 0 means there is no relationship between the two variables. When Pearson’s r is 0, the points on a scatterplot form a shapeless “cloud.” As its value moves toward −1.00 or +1.00, the points come closer and closer to falling on a single straight line. Correlation coefficients near ±.10 are considered small, values near ± .30 are considered medium, and values near ±.50 are considered large. Notice that the sign of Pearson’s r is unrelated to its strength. Pearson’s r values of +.30 and −.30, for example, are equally strong; it is just that one represents a moderate positive relationship and the other a moderate negative relationship. With the exception of reliability coefficients, most correlations that we find in Psychology are small or moderate in size. The website http://rpsychologist.com/d3/correlation/ , created by Kristoffer Magnusson, provides an excellent interactive visualization of correlations that permits you to adjust the strength and direction of a correlation while witnessing the corresponding changes to the scatterplot.
Figure 6.4 Range of Pearson’s r, From −1.00 (Strongest Possible Negative Relationship), Through 0 (No Relationship), to +1.00 (Strongest Possible Positive Relationship)
There are two common situations in which the value of Pearson’s r can be misleading. Pearson’s r is a good measure only for linear relationships, in which the points are best approximated by a straight line. It is not a good measure for nonlinear relationships, in which the points are better approximated by a curved line. Figure 6.5, for example, shows a hypothetical relationship between the amount of sleep people get per night and their level of depression. In this example, the line that best approximates the points is a curve—a kind of upside-down “U”—because people who get about eight hours of sleep tend to be the least depressed. Those who get too little sleep and those who get too much sleep tend to be more depressed. Even though Figure 6.5 shows a fairly strong relationship between depression and sleep, Pearson’s r would be close to zero because the points in the scatterplot are not well fit by a single straight line. This means that it is important to make a scatterplot and confirm that a relationship is approximately linear before using Pearson’s r . Nonlinear relationships are fairly common in psychology, but measuring their strength is beyond the scope of this book.
Figure 6.5 Hypothetical Nonlinear Relationship Between Sleep and Depression
The other common situations in which the value of Pearson’s r can be misleading is when one or both of the variables have a limited range in the sample relative to the population. This problem is referred to as restriction of range . Assume, for example, that there is a strong negative correlation between people’s age and their enjoyment of hip hop music as shown by the scatterplot in Figure 6.6. Pearson’s r here is −.77. However, if we were to collect data only from 18- to 24-year-olds—represented by the shaded area of Figure 6.6—then the relationship would seem to be quite weak. In fact, Pearson’s r for this restricted range of ages is 0. It is a good idea, therefore, to design studies to avoid restriction of range. For example, if age is one of your primary variables, then you can plan to collect data from people of a wide range of ages. Because restriction of range is not always anticipated or easily avoidable, however, it is good practice to examine your data for possible restriction of range and to interpret Pearson’s r in light of it. (There are also statistical methods to correct Pearson’s r for restriction of range, but they are beyond the scope of this book).
Figure 6.6 Hypothetical Data Showing How a Strong Overall Correlation Can Appear to Be Weak When One Variable Has a Restricted Range.The overall correlation here is −.77, but the correlation for the 18- to 24-year-olds (in the blue box) is 0.
Correlation Does Not Imply Causation
You have probably heard repeatedly that “Correlation does not imply causation.” An amusing example of this comes from a 2012 study that showed a positive correlation (Pearson’s r = 0.79) between the per capita chocolate consumption of a nation and the number of Nobel prizes awarded to citizens of that nation [2] . It seems clear, however, that this does not mean that eating chocolate causes people to win Nobel prizes, and it would not make sense to try to increase the number of Nobel prizes won by recommending that parents feed their children more chocolate.
There are two reasons that correlation does not imply causation. The first is called the directionality problem . Two variables, X and Y , can be statistically related because X causes Y or because Y causes X . Consider, for example, a study showing that whether or not people exercise is statistically related to how happy they are—such that people who exercise are happier on average than people who do not. This statistical relationship is consistent with the idea that exercising causes happiness, but it is also consistent with the idea that happiness causes exercise. Perhaps being happy gives people more energy or leads them to seek opportunities to socialize with others by going to the gym. The second reason that correlation does not imply causation is called the third-variable problem . Two variables, X and Y , can be statistically related not because X causes Y , or because Y causes X , but because some third variable, Z , causes both X and Y . For example, the fact that nations that have won more Nobel prizes tend to have higher chocolate consumption probably reflects geography in that European countries tend to have higher rates of per capita chocolate consumption and invest more in education and technology (once again, per capita) than many other countries in the world. Similarly, the statistical relationship between exercise and happiness could mean that some third variable, such as physical health, causes both of the others. Being physically healthy could cause people to exercise and cause them to be happier. Correlations that are a result of a third-variable are often referred to as spurious correlations.
Some excellent and funny examples of spurious correlations can be found at http://www.tylervigen.com (Figure 6.7 provides one such example).
“Lots of Candy Could Lead to Violence”
Although researchers in psychology know that correlation does not imply causation, many journalists do not. One website about correlation and causation, http://jonathan.mueller.faculty.noctrl.edu/100/correlation_or_causation.htm , links to dozens of media reports about real biomedical and psychological research. Many of the headlines suggest that a causal relationship has been demonstrated when a careful reading of the articles shows that it has not because of the directionality and third-variable problems.
One such article is about a study showing that children who ate candy every day were more likely than other children to be arrested for a violent offense later in life. But could candy really “lead to” violence, as the headline suggests? What alternative explanations can you think of for this statistical relationship? How could the headline be rewritten so that it is not misleading?
As you have learned by reading this book, there are various ways that researchers address the directionality and third-variable problems. The most effective is to conduct an experiment. For example, instead of simply measuring how much people exercise, a researcher could bring people into a laboratory and randomly assign half of them to run on a treadmill for 15 minutes and the rest to sit on a couch for 15 minutes. Although this seems like a minor change to the research design, it is extremely important. Now if the exercisers end up in more positive moods than those who did not exercise, it cannot be because their moods affected how much they exercised (because it was the researcher who determined how much they exercised). Likewise, it cannot be because some third variable (e.g., physical health) affected both how much they exercised and what mood they were in (because, again, it was the researcher who determined how much they exercised). Thus experiments eliminate the directionality and third-variable problems and allow researchers to draw firm conclusions about causal relationships.
Key Takeaways
- Correlational research involves measuring two variables and assessing the relationship between them, with no manipulation of an independent variable.
- Correlation does not imply causation. A statistical relationship between two variables, X and Y , does not necessarily mean that X causes Y . It is also possible that Y causes X , or that a third variable, Z , causes both X and Y .
- While correlational research cannot be used to establish causal relationships between variables, correlational research does allow researchers to achieve many other important objectives (establishing reliability and validity, providing converging evidence, describing relationships and making predictions)
- Correlation coefficients can range from -1 to +1. The sign indicates the direction of the relationship between the variables and the numerical value indicates the strength of the relationship.
- A cognitive psychologist compares the ability of people to recall words that they were instructed to “read” with their ability to recall words that they were instructed to “imagine.”
- A manager studies the correlation between new employees’ college grade point averages and their first-year performance reports.
- An automotive engineer installs different stick shifts in a new car prototype, each time asking several people to rate how comfortable the stick shift feels.
- A food scientist studies the relationship between the temperature inside people’s refrigerators and the amount of bacteria on their food.
- A social psychologist tells some research participants that they need to hurry over to the next building to complete a study. She tells others that they can take their time. Then she observes whether they stop to help a research assistant who is pretending to be hurt.
2. Practice: For each of the following statistical relationships, decide whether the directionality problem is present and think of at least one plausible third variable.
- People who eat more lobster tend to live longer.
- People who exercise more tend to weigh less.
- College students who drink more alcohol tend to have poorer grades.
- Bushman, B. J., & Huesmann, L. R. (2001). Effects of televised violence on aggression. In D. Singer & J. Singer (Eds.), Handbook of children and the media (pp. 223–254). Thousand Oaks, CA: Sage. ↵
- Messerli, F. H. (2012). Chocolate consumption, cognitive function, and Nobel laureates. New England Journal of Medicine, 367 , 1562-1564. ↵
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