description of a natural experiment in psychology

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• Natural Experiment

Whilst oftentimes people tend to think of experiments occurring in laboratories and controlled settings, psychologists also consider real-world environments as opportunities to investigate phenomena. Behaviour changes depending on the setting, and investigating research areas in their natural settings can amplify the validity of the findings. Natural experiments offer researchers the opportunity to investigate human behaviour in everyday life. 

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What is a natural experiment?

What are the advantages of natural experiments?

What are the disadvantages of natural experiments?

What is a quasi-experiment?

What are the advantages of quasi-experiments?

What are the disadvantages of quasi-experiments?

When are natural experiments conducted?

Why can't researchers draw cause-and-effect conclusions from natural experiments and natural setting quasi-experiments?

When might you use a quasi-experiment?

Which of the following experiments does not involve the researcher manipulating the independent variable? 

Why may a researcher use a natural experiment in terms of ethical issues?

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• We are going to explore natural experiments used in psychological research.
• We will start by highlighting the natural experiment definition.
• We will then explore how natural experiments are used in psychology and cover natural experiment examples of research to demonstrate to help illustrate our points.
• Moving on, we will cover natural and field experiments to highlight the differences between the two types of investigations.
• And to finish, we will explore the natural experiment's advantages and disadvantages.

Natural Experiment Defintion

Natural experiments are essentially experiments that investigate naturally occurring phenomena. The natural experiment definition is a research procedure that occurs in the participant's natural setting that requires no manipulation by the researcher.

In experiments, changes in the independent variable (IV) are observed to identify if these changes affect the dependent variable (DV). However, in natural experiments, the researcher does not manipulate the IV. Instead, they observe the natural changes that occur.

Some examples of naturally occurring IVs are sex at birth, whether people have experienced a natural disaster, experienced a traumatic experience, or been diagnosed with a specific illness.

These examples show that it's next to impossible for the researcher to manipulate these.

Natural Experiment: Psychology

Why may researchers choose to use a natural experiment? As we have just discussed, sometimes researchers can't manipulate the IV. But, they may still wish to see how changes in the IV affect the DV, so use a natural experiment.

Sometimes a researcher can manipulate the IV, but it may be unethical or impractical to do so, so they conduct a natural experiment.

In natural experiments, the researcher can see how changes in the IV affect a DV, but unlike in lab experiments, the researcher has to identify how the IV is changing. In contrast, lab experiments pre-determine how the IV will be manipulated.

Natural Experiment: Examples

Natural experiments often take place in real-world settings. An example can be seen in examining the effect of female and male performance in an office environment and if gender plays a role in the retention of customers. Other examples include examining behaviours in schools, and the effect age has on behaviour.

Let's look at a hypothetical study that uses a natural experiment research method.

A research team was interested in investigating attitudes towards the community after experiencing a natural disaster.

The study collected data using interviews. The IV was naturally occurring as the researcher did not manipulate the IV; instead, they recruited participants who had recently experienced a natural disaster.

Natural Experiment vs Field Experiment

The table below summarises the key similarities and differences between natural experiments vs field experiments.

Natural Experiment: Advantages and Disadvantages

In the following section will present the natural experiment's advantages and disadvantages. We will discuss the new research possibilities, causal conclusions, rare opportunities, pre-existing sampling bias and ethical issues.

New Research Opportunities

Natural experiments provide opportunities for research that can't be done for ethical and practical reasons.

For example, it is impossible to manipulate a natural disaster or maternal deprivation on participants.

So, natural experiments are the only ethical way for researchers to investigate the causal relationship of the above topics. Thus, natural experiments open up practical research opportunities to study conditions that cannot be manipulated.

High Ecological Validity

Natural experiments have high ecological validity because natural experiments study real-world problems that occur naturally in real-life settings.

When research is found to use and apply real-life settings and techniques, it is considered to have high mundane realism.

And the advantage of this is that the results are more likely applicable and generalisable to real-life situations.

Rare Opportunities

There are scarce opportunities for researchers to conduct a natural experiment. Most natural events are ‘one-off’ situations. Because natural events are unique, the results have limited generalisability to similar situations.

In addition, it is next to impossible for researchers to replicate natural experiments; therefore, it is difficult to establish the reliability of findings.

Pre-Existing Sampling Bias

In natural experiments, pre-existing sampling bias can be a problem. In natural experiments, researchers cannot randomly assign participants to different conditions because naturally occurring events create them. Therefore, in natural experiments, participant differences may act as confounding variables .

As a result, sample bias in natural experiments can lead to low internal validity and generalisability of the research.

Ethical Issues

Although natural experiments are considered the only ethically acceptable method for studying conditions that can't be manipulated, ethical issues may still arise. Because natural experiments are often conducted after traumatic events, interviewing or observing people after the event could cause psychological harm to participants.

Researchers should prepare for potential ethical issues, such as psychological harm, usually dealt with by offering therapy. However, this can be pretty costly. And the ethical issue may lead participants to drop out of the research, which can also affect the quality of the research.

Natural Experiment - Key takeaways

The natural experiment definition is a research procedure that occurs in the participant's natural setting that requires no manipulation of the researcher.

The advantages of natural experiments are that they provide opportunities for research that researchers cannot do for ethical or practical reasons and have high ecological validity.

The disadvantages of natural experiments are reliability issues, pre-existing sample bias, and ethical issues, such as conducting a study after traumatic events may cause psychological distress.

Flashcards in Natural Experiment 22

In a natural experiment, researchers take advantage of events that occur or have already occurred naturally. Therefore, the researchers cannot change or control the IV of the natural experiment.

Natural experiments provide opportunities for research that may not be conducted due to ethical and practical reasons. Also, natural experiments have good ecological validity.

There are scarce opportunities for researchers to conduct a natural experiment and pre-existing sampling bias can be an issue. Also, ethical issues can still arise in natural experiments.

A quasi-experiment is an experiment that examines pre-existing differences between people. There is an IV, but it already exists and is not manipulated by the researcher.

Lab-based quasi-experiments are conducted in a well-controlled setting, which implies good internal validity and reliability. Also, quasi-experiments allow comparisons between peoples according to their pre-existing differences.

Researchers can only tentatively draw causal conclusions in natural setting quasi-experiments and pre-existing sampling bias can be an issue.

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Frequently Asked Questions about Natural Experiment

The natural experiment definition is a research procedure that occurs in the participant's natural setting that requires no manipulation of the researcher. 

What is an example of natural experiment?

Beckett (2006) investigated the effects of deprivation on children’s IQ at age 11. They compared 128 Romanian children who UK families had adopted at various ages and 50 UK children who had been adopted before six months. They found that Romanian children who had been adopted before six months of age had similar IQs to the UK children; however, Romanian children adopted after six months of age had much worse scores. 

What are the characteristics of a natural experiment?

The characteristics of natural experiments are that they are carried out in a natural setting and the IV is not manipulated in this type of experiment. 

What are the advantages and disadvantages of natural experiments?

And the disadvantages of natural experiments are reliability issues, pre-existing sample bias, and ethical issues, such as conducting a study after traumatic events may cause psychological distress.

What are natural experiments in research?

Natural experiments in psychology research are often used when manipulating a variable is unethical or impractical.

Test your knowledge with multiple choice flashcards

True or false: Similar to lab experiments, natural experiments are conducted in controlled settings.

True or false: Confounding/ extraneous variables can be an issue in natural experiments. 

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Experimental Methods In Psychology

March 7, 2021 - paper 2 psychology in context | research methods.

There are three experimental methods in the field of psychology; Laboratory, Field and Natural Experiments. Each of the experimental methods holds different characteristics in relation to; the manipulation of the IV, the control of the EVs and the ability to accurately replicate the study in exactly the same way.

When conducting research, it is important to create an aim and a hypothesis,  click here  to learn more about the formation of aims and hypotheses.

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Experimental Design: Types, Examples & Methods

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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Experimental design refers to how participants are allocated to different groups in an experiment. Types of design include repeated measures, independent groups, and matched pairs designs.

Probably the most common way to design an experiment in psychology is to divide the participants into two groups, the experimental group and the control group, and then introduce a change to the experimental group, not the control group.

The researcher must decide how he/she will allocate their sample to the different experimental groups.  For example, if there are 10 participants, will all 10 participants participate in both groups (e.g., repeated measures), or will the participants be split in half and take part in only one group each?

Three types of experimental designs are commonly used:

1. Independent Measures

Independent measures design, also known as between-groups , is an experimental design where different participants are used in each condition of the independent variable.  This means that each condition of the experiment includes a different group of participants.

This should be done by random allocation, ensuring that each participant has an equal chance of being assigned to one group.

Independent measures involve using two separate groups of participants, one in each condition. For example:

• Con : More people are needed than with the repeated measures design (i.e., more time-consuming).
• Pro : Avoids order effects (such as practice or fatigue) as people participate in one condition only.  If a person is involved in several conditions, they may become bored, tired, and fed up by the time they come to the second condition or become wise to the requirements of the experiment!
• Con : Differences between participants in the groups may affect results, for example, variations in age, gender, or social background.  These differences are known as participant variables (i.e., a type of extraneous variable ).
• Control : After the participants have been recruited, they should be randomly assigned to their groups. This should ensure the groups are similar, on average (reducing participant variables).

2. Repeated Measures Design

Repeated Measures design is an experimental design where the same participants participate in each independent variable condition.  This means that each experiment condition includes the same group of participants.

Repeated Measures design is also known as within-groups or within-subjects design .

• Pro : As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
• Con : There may be order effects. Order effects refer to the order of the conditions affecting the participants’ behavior.  Performance in the second condition may be better because the participants know what to do (i.e., practice effect).  Or their performance might be worse in the second condition because they are tired (i.e., fatigue effect). This limitation can be controlled using counterbalancing.
• Pro : Fewer people are needed as they participate in all conditions (i.e., saves time).
• Control : To combat order effects, the researcher counter-balances the order of the conditions for the participants.  Alternating the order in which participants perform in different conditions of an experiment.

Counterbalancing

Suppose we used a repeated measures design in which all of the participants first learned words in “loud noise” and then learned them in “no noise.”

We expect the participants to learn better in “no noise” because of order effects, such as practice. However, a researcher can control for order effects using counterbalancing.

The sample would be split into two groups: experimental (A) and control (B).  For example, group 1 does ‘A’ then ‘B,’ and group 2 does ‘B’ then ‘A.’ This is to eliminate order effects.

Although order effects occur for each participant, they balance each other out in the results because they occur equally in both groups.

3. Matched Pairs Design

A matched pairs design is an experimental design where pairs of participants are matched in terms of key variables, such as age or socioeconomic status. One member of each pair is then placed into the experimental group and the other member into the control group .

One member of each matched pair must be randomly assigned to the experimental group and the other to the control group.

• Con : If one participant drops out, you lose 2 PPs’ data.
• Pro : Reduces participant variables because the researcher has tried to pair up the participants so that each condition has people with similar abilities and characteristics.
• Con : Very time-consuming trying to find closely matched pairs.
• Pro : It avoids order effects, so counterbalancing is not necessary.
• Con : Impossible to match people exactly unless they are identical twins!
• Control : Members of each pair should be randomly assigned to conditions. However, this does not solve all these problems.

Experimental design refers to how participants are allocated to an experiment’s different conditions (or IV levels). There are three types:

1. Independent measures / between-groups : Different participants are used in each condition of the independent variable.

2. Repeated measures /within groups : The same participants take part in each condition of the independent variable.

3. Matched pairs : Each condition uses different participants, but they are matched in terms of important characteristics, e.g., gender, age, intelligence, etc.

Learning Check

Read about each of the experiments below. For each experiment, identify (1) which experimental design was used; and (2) why the researcher might have used that design.

1 . To compare the effectiveness of two different types of therapy for depression, depressed patients were assigned to receive either cognitive therapy or behavior therapy for a 12-week period.

The researchers attempted to ensure that the patients in the two groups had similar severity of depressed symptoms by administering a standardized test of depression to each participant, then pairing them according to the severity of their symptoms.

2 . To assess the difference in reading comprehension between 7 and 9-year-olds, a researcher recruited each group from a local primary school. They were given the same passage of text to read and then asked a series of questions to assess their understanding.

3 . To assess the effectiveness of two different ways of teaching reading, a group of 5-year-olds was recruited from a primary school. Their level of reading ability was assessed, and then they were taught using scheme one for 20 weeks.

At the end of this period, their reading was reassessed, and a reading improvement score was calculated. They were then taught using scheme two for a further 20 weeks, and another reading improvement score for this period was calculated. The reading improvement scores for each child were then compared.

4 . To assess the effect of the organization on recall, a researcher randomly assigned student volunteers to two conditions.

Condition one attempted to recall a list of words that were organized into meaningful categories; condition two attempted to recall the same words, randomly grouped on the page.

Experiment Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. Extraneous variables should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of taking part in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

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Experimental Research

23 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.
• Define what a control condition is, explain its purpose in research on treatment effectiveness, and describe some alternative types of control conditions.

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 one variable (referred to as an independent variable ) cause a change in another variable (referred to as 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 exerts control over, 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. 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 are 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 experiment 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 [1] . 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 lesbian women would apply to older gay 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.

Treatment and Control Conditions

In psychological research, a treatment is any intervention meant to change people’s behavior for the better. This intervention 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 bed sheets 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) [2] .

Placebo effects are interesting in their own right (see Note “The Powerful Placebo” ), but they also pose a serious problem for researchers who want to determine whether a treatment works. Figure 5.2 shows some hypothetical results in which participants in a treatment condition improved more on average than participants in a no-treatment control condition. If these conditions (the two leftmost bars in Figure 5.2 ) were the only conditions in this experiment, however, one could not conclude that the treatment worked. It could be instead that participants in the treatment group improved more because they expected to improve, while those in the no-treatment control condition did not.

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. This difference is what is shown by a comparison of the two outer bars in Figure 5.4 .

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 wait-list 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 disclosure 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?

The Powerful Placebo

Many people are not surprised that placebos can have a positive effect on disorders that seem fundamentally psychological, including depression, anxiety, and insomnia. However, placebos can also have a positive effect on disorders that most people think of as fundamentally physiological. These include asthma, ulcers, and warts (Shapiro & Shapiro, 1999) [3] . There is even evidence that placebo surgery—also called “sham surgery”—can be as effective as actual surgery.

Medical researcher J. Bruce Moseley and his colleagues conducted a study on the effectiveness of two arthroscopic surgery procedures for osteoarthritis of the knee (Moseley et al., 2002) [4] . The control participants in this study were prepped for surgery, received a tranquilizer, and even received three small incisions in their knees. But they did not receive the actual arthroscopic surgical procedure. Note that the IRB would have carefully considered the use of deception in this case and judged that the benefits of using it outweighed the risks and that there was no other way to answer the research question (about the effectiveness of a placebo procedure) without it. The surprising result was that all participants improved in terms of both knee pain and function, and the sham surgery group improved just as much as the treatment groups. According to the researchers, “This study provides strong evidence that arthroscopic lavage with or without débridement [the surgical procedures used] is not better than and appears to be equivalent to a placebo procedure in improving knee pain and self-reported function” (p. 85).

• Knecht, S., Dräger, B., Deppe, M., Bobe, L., Lohmann, H., Flöel, A., . . . Henningsen, H. (2000). Handedness and hemispheric language dominance in healthy humans. Brain: A Journal of Neurology, 123 (12), 2512-2518. http://dx.doi.org/10.1093/brain/123.12.2512 ↵
• 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. ↵
• Shapiro, A. K., & Shapiro, E. (1999). The powerful placebo: From ancient priest to modern physician . Baltimore, MD: Johns Hopkins University Press. ↵
• 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. ↵

A type of study designed specifically to answer the question of whether there is a causal relationship between two variables.

The variable the experimenter manipulates.

The variable the experimenter measures (it is the presumed effect).

The different levels of the independent variable to which participants are assigned.

Holding extraneous variables constant in order to separate the effect of the independent variable from the effect of the extraneous variables.

Any variable other than the dependent and independent variable.

Changing the level, or condition, of the independent variable 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.

An experiment design involving a single independent variable with two conditions.

When an experiment has one independent variable that is manipulated to produce more than two conditions.

An extraneous variable that varies systematically with the independent variable, and thus confuses the effect of the independent variable with the effect of the extraneous one.

Any intervention meant to change people’s behavior for the better.

The condition in which participants receive the treatment.

The condition in which participants do not receive the treatment.

An experiment that researches the effectiveness of psychotherapies and medical treatments.

The condition in which participants receive no treatment whatsoever.

A simulated treatment that lacks any active ingredient or element that is hypothesized to make the treatment effective, but is otherwise identical to the treatment.

An effect that is due to the placebo rather than the treatment.

Condition in which the participants receive a placebo rather than the treatment.

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.

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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Naturalistic Observation: Definition, Examples, and Advantages

Categories Research Methods

Naturalistic observation is a psychological research method that involves observing and recording behavior in the natural environment. Unlike experiments, researchers do not manipulate variables. This research method is frequently used in psychology to help researchers investigate human behavior.

This article explores how naturalistic observation is used in psychology. It offers examples and the potential advantages and disadvantages of this type of research. 

Table of Contents

What Is Naturalistic Observation?

In naturalistic observation, the researcher observes the participants’ behavior in their natural setting, taking notes on their behavior and interactions. The researcher may use various tools, such as video or audio recordings, to help capture the behavior accurately. The researcher may also use coding systems or other quantitative measures to systematically record observed behavior.

Naturalistic observation can be used to investigate a wide range of psychological phenomena, such as social interaction patterns, parental behavior, or animal behavior. 

Types of Naturalistic Observation

Naturalistic observation can be:

Unstructured or Structured

The observer can either watch and record everything that happens, or they can have a checklist or form to guide their observations.

Participant or Non-Participant

The observer can be an active participant, or they can remain separate from the subject and view from the sidelines.

Overt or Covert

The observer can either openly watch and record the subjects’ behaviors, or they can keep their presence hidden from the individual or group.

The specific type of naturalistic observation that researchers use depends on the situation, what they are researching, and the resources available. No matter the type, the observation must occur in a natural setting rather than in an experimental lab.

How to Collect Data in Naturalistic Observation

There are a number of methods that researchers might utilize to record data about the behaviors and events they observe. Some of these include:

• Note-taking : Research may opt to take notes about what they witness. This approach tends to be unstructured, allowing the observers to determine what they think is relevant and to include insights that may be helpful.
• Tally counts : In other cases, research may take a more structured approach where they count the frequency of a behavior.
• Audiovisual recordings : In other cases, research may want recordings of participant behavior. This not only allows researchers to refer to the recordings later, it can also be useful for sharing with others.

How Data Is Sampled in Naturalistic Observation

While naturalistic observation is not an experimental design, researchers still want to ensure that the data they collect represents what is happening in the group. To do this, researchers must collect a representative sample. When a sample is representative, it means that it accurately reflects what is happening in a given population.

To do this, researchers may utilize three primary sampling approaches:

Event Sampling

Event sampling involves the researcher creating a set of predefined categories and behaviors they will observe. This method is useful when the researcher wants to collect data on specific behaviors or events, allowing for more precise data collection.

Using this approach, the research would note every occurrence of a specific behavior.

Situation Sampling

Situation sampling involves observing participants in more than one situation. This approach can give researchers more insight and allow them to determine if certain behaviors only occur in specific contexts or settings. 

Time Sampling

Time sampling is a type of systematic observation that involves the researcher observing and recording the subjects’ behavior at predetermined intervals. This method is useful when the researcher wants to collect data on the frequency and duration of specific behaviors.

Each method of data collection has its strengths and weaknesses, and the choice of method depends on the research question and the nature of the subjects being observed.

Examples of Naturalistic Observation

It can be helpful to look at a few different examples to learn more about how naturalistic observation can be used:

• Researchers might observe children in a classroom to learn more about their social interaction patterns. 
• Naturalistic observation can also be used to study animal behavior in their natural habitat, such as observing chimpanzees in the wild to understand their social behavior.

Researchers use this research method in various fields, including animal researchers and anthropologists. 

The work of zoologist Konrad Lorenz, for example, relied on the use of naturalistic observation. Lorenz observed the behavior of ducklings after they hatched and noted that they became attached to the first possible parent figure they saw, a phenomenon known as imprinting. Once imprinted on a parent figure, the duckling would follow and learn from their parent.

From his naturalistic observations, Lorenz hypothesized that there was a critical period immediately after hatching where ducklings needed to imprint on a parent. Based on his observations, Lorenz conducted further experiments that confirmed his hypothesis.

More Examples of Naturalistic Observation

Naturalistic observation is a research method commonly used in various areas of psychology. 

Social Psychology

Naturalistic observation can provide valuable insights into people’s behavior in different social situations. By observing people’s behavior in a crowded public place like a shopping mall or train station, researchers can better understand how social norms are established and maintained and how people interact in various social groups.

Consumer Research

Consumer research is another area where naturalistic observation can be used effectively. By observing shoppers in a grocery store or shopping mall, researchers can study how people make purchasing decisions in real-life situations.

Researchers can gain valuable insights into consumer behavior by analyzing what catches their attention, how they interact with different products, and how they decide what to buy.

Developmental Psychology

Observing children playing in a playground or a classroom can help researchers understand how children develop and learn new skills in natural settings.

Researchers can gain insights into the developmental process by observing children as they interact with each other and learn social skills or as they learn new concepts and skills in a classroom.

Cognitive Psychology

Naturalistic observation can be used to study how people think and process information in real-life situations. For example, observing people using a computer program can help researchers understand how people navigate through it and solve problems.

Similarly, observing people in a conversation can provide insights into how they process and respond to information in real time.

Advantages of Naturalistic Observation

Naturalistic observation offers a number of benefits that can make it a good choice for research. 

Ecological Validity

One of the strengths of naturalistic observation is its ability to capture behavior in a natural setting, providing a more accurate and comprehensive picture of how people or animals behave in their everyday environment.

It is often more realistic than lab research, so it can give insight into how people behave authentically in everyday settings and situations.

Inspiration for Additional Research

Naturalistic observation can also generate new hypotheses and insights that may not be captured in other research methods. 

Research That Can’t Be Done in a Lab

Naturalistic observation allows the study of behaviors that cannot be replicated in a lab. Naturalistic observation is sometimes the only approach for studying behaviors that cannot be reproduced in a lab due to ethical reasons.

For example, researchers might use this approach to research prison behavior or the social impact of domestic violence on emotional health. Those are not situations they can manipulate in a lab, but they can observe the impact on people who have had those experiences.

Disadvantages of Naturalistic Observation

While naturalistic can be a valuable tool, it is not appropriate for every situation. Some potential downsides include: 

Bias and Lack of Control

Naturalistic observation is limited by its lack of environmental control and the potential for observer bias. Researchers must be careful to minimize the influence of their presence on the behavior being observed and to use systematic and objective methods for recording and analyzing the data. 

Inability to Infer Cause and Effect

Naturalistic observation is also limited by its inability to establish causality between variables.

Naturalistic Observation vs. Case Study

Naturalistic observation and case studies are both research methods used in psychology but differ in their approach and purpose. Naturalistic observation involves observing and recording the behavior of individuals or groups in their natural environment without any intervention or manipulation by the researcher.

On the other hand, a case study is an in-depth analysis of a single individual or a small group of individuals, often conducted through interviews, surveys, and other forms of data collection.

The key difference between naturalistic observation and a case study is that the former focuses more on observing and recording behaviors and interactions as they occur naturally, while the latter focuses on gathering detailed information about a specific individual or group.

Naturalistic observation is often used to study social interactions, group dynamics, and other natural behaviors in real-world settings. In contrast, case studies often explore complex psychological phenomena such as mental illness, personality disorders, or unusual behaviors.

Both naturalistic observation and case studies have their strengths and limitations. The choice of method depends on the research question, the level of detail needed, and the feasibility of conducting the study in a particular setting.

Naturalistic Observation Ideas

There are many potential ideas for studies that involve naturalistic observation. A few ideas include:

• Observe the behavior of animals in their natural habitats, studying their patterns of movement, foraging, and communication
• Observe human behavior in public spaces, such as parks or coffee shops, documenting patterns of social interaction and communication
• Focus on the behavior of individuals within specific social groups or communities, studying their interactions and relationships over time
• Watch the behavior of children in a classroom setting could provide insights into their learning and socialization processes

Frequently Asked Questions

Why do we use naturalistic observation.

Naturalistic observation is important because it allows researchers to better understand how individuals behave in their everyday lives. By observing behavior in a natural setting, researchers can obtain a more accurate representation of how people act and interact with each other in their normal environment. 

This method is particularly useful when studying social behavior, as it allows researchers to capture the complexity and nuances of social interactions that might not be apparent in a laboratory setting.

Naturalistic observation can also offer valuable insights into the development of certain behaviors, such as those related to child development or the formation of social groups.

What is the most famous example of naturalistic observation?

The most famous example of naturalistic observation is probably Jane Goodall’s study of chimpanzees in the wild. Goodall spent years observing the behavior of chimpanzees in Tanzania, documenting their social interactions, tool use, and other aspects of their lives. Her work helped to revolutionize our understanding of these animals and their place in the natural world.

In conclusion, naturalistic observation is a powerful research method that can be used effectively in various areas within psychology. Researchers can gain valuable insights into human behavior and cognition by observing people’s behavior in natural settings.

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How to Conduct a Psychology Experiment

Conducting your first psychology experiment can be a long, complicated, and sometimes intimidating process. It can be especially confusing if you are not quite sure where to begin or which steps to take.

Like other sciences, psychology utilizes the  scientific method  and bases conclusions upon empirical evidence. When conducting an experiment, it is important to follow the seven basic steps of the scientific method:

• Ask a testable question
• Define your variables
• Conduct background research
• Design your experiment
• Perform the experiment
• Collect and analyze the data
• Draw conclusions
• Share the results with the scientific community

At a Glance

It's important to know the steps of the scientific method if you are conducting an experiment in psychology or other fields. The processes encompasses finding a problem you want to explore, learning what has already been discovered about the topic, determining your variables, and finally designing and performing your experiment. But the process doesn't end there! Once you've collected your data, it's time to analyze the numbers, determine what they mean, and share what you've found.

Find a Research Problem or Question

Picking a research problem can be one of the most challenging steps when you are conducting an experiment. After all, there are so many different topics you might choose to investigate.

Are you stuck for an idea? Consider some of the following:

Investigate a Commonly Held Belief

Folk knowledge is a good source of questions that can serve as the basis for psychological research. For example, many people believe that staying up all night to cram for a big exam can actually hurt test performance.

You could conduct a study to compare the test scores of students who stayed up all night with the scores of students who got a full night's sleep before the exam.

Review Psychology Literature

Published studies are a great source of unanswered research questions. In many cases, the authors will even note the need for further research. Find a published study that you find intriguing, and then come up with some questions that require further exploration.

Think About Everyday Problems

There are many practical applications for psychology research. Explore various problems that you or others face each day, and then consider how you could research potential solutions. For example, you might investigate different memorization strategies to determine which methods are most effective.

Define Your Variables

Variables are anything that might impact the outcome of your study. An operational definition describes exactly what the variables are and how they are measured within the context of your study.

For example, if you were doing a study on the impact of sleep deprivation on driving performance, you would need to operationally define sleep deprivation and driving performance .

An operational definition refers to a precise way that an abstract concept will be measured. For example, you cannot directly observe and measure something like test anxiety . You can, however, use an anxiety scale and assign values based on how many anxiety symptoms a person is experiencing. 

In this example, you might define sleep deprivation as getting less than seven hours of sleep at night. You might define driving performance as how well a participant does on a driving test.

What is the purpose of operationally defining variables? The main purpose is control. By understanding what you are measuring, you can control for it by holding the variable constant between all groups or manipulating it as an independent variable .

Develop a Hypothesis

The next step is to develop a testable hypothesis that predicts how the operationally defined variables are related. In the recent example, the hypothesis might be: "Students who are sleep-deprived will perform worse than students who are not sleep-deprived on a test of driving performance."

Null Hypothesis

In order to determine if the results of the study are significant, it is essential to also have a null hypothesis. The null hypothesis is the prediction that one variable will have no association to the other variable.

In other words, the null hypothesis assumes that there will be no difference in the effects of the two treatments in our experimental and control groups .

The null hypothesis is assumed to be valid unless contradicted by the results. The experimenters can either reject the null hypothesis in favor of the alternative hypothesis or not reject the null hypothesis.

It is important to remember that not rejecting the null hypothesis does not mean that you are accepting the null hypothesis. To say that you are accepting the null hypothesis is to suggest that something is true simply because you did not find any evidence against it. This represents a logical fallacy that should be avoided in scientific research.  

Conduct Background Research

Once you have developed a testable hypothesis, it is important to spend some time doing some background research. What do researchers already know about your topic? What questions remain unanswered?

You can learn about previous research on your topic by exploring books, journal articles, online databases, newspapers, and websites devoted to your subject.

Reading previous research helps you gain a better understanding of what you will encounter when conducting an experiment. Understanding the background of your topic provides a better basis for your own hypothesis.

After conducting a thorough review of the literature, you might choose to alter your own hypothesis. Background research also allows you to explain why you chose to investigate your particular hypothesis and articulate why the topic merits further exploration.

As you research the history of your topic, take careful notes and create a working bibliography of your sources. This information will be valuable when you begin to write up your experiment results.

Select an Experimental Design

After conducting background research and finalizing your hypothesis, your next step is to develop an experimental design. There are three basic types of designs that you might utilize. Each has its own strengths and weaknesses:

Pre-Experimental Design

A single group of participants is studied, and there is no comparison between a treatment group and a control group. Examples of pre-experimental designs include case studies (one group is given a treatment and the results are measured) and pre-test/post-test studies (one group is tested, given a treatment, and then retested).

Quasi-Experimental Design

This type of experimental design does include a control group but does not include randomization. This type of design is often used if it is not feasible or ethical to perform a randomized controlled trial.

True Experimental Design

A true experimental design, also known as a randomized controlled trial, includes both of the elements that pre-experimental designs and quasi-experimental designs lack—control groups and random assignment to groups.

Standardize Your Procedures

In order to arrive at legitimate conclusions, it is essential to compare apples to apples.

Each participant in each group must receive the same treatment under the same conditions.

For example, in our hypothetical study on the effects of sleep deprivation on driving performance, the driving test must be administered to each participant in the same way. The driving course must be the same, the obstacles faced must be the same, and the time given must be the same.

Choose Your Participants

In addition to making sure that the testing conditions are standardized, it is also essential to ensure that your pool of participants is the same.

If the individuals in your control group (those who are not sleep deprived) all happen to be amateur race car drivers while your experimental group (those that are sleep deprived) are all people who just recently earned their driver's licenses, your experiment will lack standardization.

When choosing subjects, there are some different techniques you can use.

Simple Random Sample

In a simple random sample, the participants are randomly selected from a group. A simple random sample can be used to represent the entire population from which the representative sample is drawn.

Drawing a simple random sample can be helpful when you don't know a lot about the characteristics of the population.

Stratified Random Sample

Participants must be randomly selected from different subsets of the population. These subsets might include characteristics such as geographic location, age, sex, race, or socioeconomic status.

Stratified random samples are more complex to carry out. However, you might opt for this method if there are key characteristics about the population that you want to explore in your research.

Conduct Tests and Collect Data

After you have selected participants, the next steps are to conduct your tests and collect the data. Before doing any testing, however, there are a few important concerns that need to be addressed.

Address Ethical Concerns

First, you need to be sure that your testing procedures are ethical . Generally, you will need to gain permission to conduct any type of testing with human participants by submitting the details of your experiment to your school's Institutional Review Board (IRB), sometimes referred to as the Human Subjects Committee.

Obtain Informed Consent

After you have gained approval from your institution's IRB, you will need to present informed consent forms to each participant. This form offers information on the study, the data that will be gathered, and how the results will be used. The form also gives participants the option to withdraw from the study at any point in time.

Once this step has been completed, you can begin administering your testing procedures and collecting the data.

Analyze the Results

After collecting your data, it is time to analyze the results of your experiment. Researchers use statistics to determine if the results of the study support the original hypothesis and if the results are statistically significant.

Statistical significance means that the study's results are unlikely to have occurred simply by chance.

The types of statistical methods you use to analyze your data depend largely on the type of data that you collected. If you are using a random sample of a larger population, you will need to utilize inferential statistics.

These statistical methods make inferences about how the results relate to the population at large.

Because you are making inferences based on a sample, it has to be assumed that there will be a certain margin of error. This refers to the amount of error in your results. A large margin of error means that there will be less confidence in your results, while a small margin of error means that you are more confident that your results are an accurate reflection of what exists in that population.

Share Your Results After Conducting an Experiment

Your final task in conducting an experiment is to communicate your results. By sharing your experiment with the scientific community, you are contributing to the knowledge base on that particular topic.

One of the most common ways to share research results is to publish the study in a peer-reviewed professional journal. Other methods include sharing results at conferences, in book chapters, or academic presentations.

In your case, it is likely that your class instructor will expect a formal write-up of your experiment in the same format required in a professional journal article or lab report :

• Introduction
• Tables and figures

What This Means For You

Designing and conducting a psychology experiment can be quite intimidating, but breaking the process down step-by-step can help. No matter what type of experiment you decide to perform, always check with your instructor and your school's institutional review board for permission before you begin.

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By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

NATURAL EXPERIMENT

Natural experiments are a type of observational study that can be used to answer questions on the causal effects of an exposure. This type of study has become increasingly popular in the past few decades due to its ability to study real-world settings, as opposed to traditional laboratory experiments. Natural experiments provide the opportunity to study the effects of an exposure in a more realistic setting. This article will discuss the definition of natural experiments, the advantages and disadvantages of this type of study, and examples of natural experiments.

A natural experiment is a type of observational study that uses naturally occurring events to examine the effects of an exposure on an outcome. This type of study is used when it is not possible to conduct a randomized controlled trial to study the causal effect of an exposure. Examples of natural experiments include the effects of laws, policy changes, or natural disasters on outcomes like mortality, fertility, or health.

Natural experiments have several advantages over laboratory experiments. First, they allow for the study of real-world settings, which can provide insights that are not possible with laboratory experiments. Second, they can be used to study rare or long-term outcomes, which may be difficult or impossible to study in a laboratory setting. Third, natural experiments are often cheaper and faster to conduct than laboratory experiments. Finally, they can provide a more representative sample of a population, since participants are not chosen based on their willingness to participate in a study.

Disadvantages

Natural experiments also have several disadvantages. First, it can be difficult to identify a valid control group in a natural experiment, since the exposure and outcome of interest may not be randomly assigned. Second, natural experiments may suffer from selection bias, since the exposure and outcome of interest may be related to other factors. Third, it can be difficult to determine the direction of causation in a natural experiment, since the exposure may have occurred prior to the outcome of interest. Finally, natural experiments may suffer from a lack of control over confounding factors.

Several examples of natural experiments can be found in the literature. For instance, a natural experiment was used to study the effects of the Clean Air Act of 1970 on infant mortality in the United States. Another example is a natural experiment that was conducted to study the effects of an increase in the minimum wage on employment in the United Kingdom. Finally, a natural experiment was used to study the effects of a ban on smoking in public places on heart attack rates in Scotland.

Natural experiments are a powerful tool for studying the causal effects of an exposure. They allow for the study of real-world settings, offer the potential to study rare or long-term outcomes, are often cheaper and faster to conduct than laboratory experiments, and can provide a more representative sample of a population. However, natural experiments also have several disadvantages, including difficulty in identifying a valid control group, selection bias, difficulty in determining the direction of causation, and a lack of control over confounding factors.

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Sassi, F., & Belloni, A. (2009). Smoke-free legislation and hospitalizations for acute coronary syndrome. Circulation, 119(3), 501-507.

Related terms

Nominative self, nonccntral chi-square distribution, nerve fiber, nest building, neural darwinism, neural substrate.

Psychology Sorted

Psychology for all, experimental methods explained.

The easiest one to define is the true experiment.  

Often called a ‘laboratory/lab’ experiment, this does not have to take place in a lab, but can be conducted in a classroom, office, waiting room, or even outside, providing it meets the criteria.  These are that allocation of participants to the two or more experimental (or experimental and control) groups or conditions is random and that the independent variable (IV) is manipulated by the researcher in order to measure the effect on the dependent variable (DV).  Other variables are carefully controlled, such as location, temperature, time of day, time taken for experiment, materials used, etc. This should result in a cause and effect relationship between the IV and the DV. Examples are randomised controlled drug trials or many of the cognitive experiments into memory, such as Glanzer and Cunitz_1966.

A field experiment is similar, in that individuals are usually randomly assigned to groups, where this is possible, and the IV is manipulated by the researcher. However, as this takes place in the participants’ natural surroundings, the extraneous variables that could confound the findings of the research are somewhat more difficult to control.  The implications for causation depend on how well these variables are controlled, and on the random allocation of participants.   Examples are bystander effect studies, and also research into the effect of digital technology on learning, such as that conducted by Hembrooke and Gay_2003 .

A quasi-experiment  is similar to either or both of the above, but the participants are not randomly allocated to groups.  Instead they are allocated on the basis of self-selection as male/female; left or right-handed; preference for coffee or tea; young/old, etc.  or researcher selection as scoring above or below and certain level on a pre-test; measured socio-economic status; psychology student or biology student, etc.  These are therefore, non-equivalent groups.  The IV is often manipulated and the DV measured as before, but the nature of the groups is a potential confounding variable.  If testing the effect of a new reading scheme on the reading ages of 11 year olds, a quasi-experimental design would allocate one class of 11 year olds to read using the scheme, and another to continue with the old scheme (control group), and then measure reading ages after a set period of time.  But there may have been other differences between the groups that mean a cause and effect relationship cannot be reliably established: those in the first class may also have already been better readers, or several months older, than those in the control group. Baseline pre-testing is one way around this, in which the students’ improvement is measured against their own earlier reading age, in a pre-test/post-test design.  In some quasi-experiments, the allocation to groups by certain criteria itself forms the IV, and the effects of gender, age or handedness on memory, for example, are measured. Examples are research into the efficacy of anti-depressants, when some participants are taking one anti-depressant and some another, or Caspi et al._2003 , who investigated whether a polymorphism on the serotonin transporter gene is linked to a higher or lower risk of individual depression in the face of different levels of perceived stress.

Finally, natural experiments are those in which there is no manipulation of the IV, because it is a naturally-occurring variable.  It may be an earthquake (IV) and measurement of people’s fear levels (DV) at living on a fault line before and after the event, or an increase in unemployment as a large factory closes (IV) and measurement of depression levels amongst adults of working age before and after the factory closure (DV). As with field experiments, many of the extraneous variables are difficult to control as the research takes place in people’s natural environment. A good example of a natural experiment is Charlton (1975) research into the effect of the introduction of television to the remote island of St. Helena.

The differences between quasi experiments and correlational research, and between natural experiments and case studies are sometimes hard to determine, so I would always encourage students to explain exactly why they are designating something as one or the other. We can’t always trust the original article either – Bartlett was happy to describe his studies as experiments, which they were not! Here’s hoping these examples have helped.  The following texts are super-useful, and were referred to while writing  this post.:

Campbell, D.T. & Stanley J.C . (1963). Experimental and Quasi-Experimental Designs for Research. Boston: Houghton Mifflin (ISBN 9780528614002)

Coolican, H. (2009, 5th ed.). Research Methods and Statistics in Psychology. UK: Hodder (ISBN 9780340983447)

Shadish, W.R., Cook, T.D. & Campbell, D.T. (2001, 2nd ed.).  Experimental and Quasi-experimental Designs for Generalized Causal Inference. UK: Wadsworth (ISBN 9780395615560)

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True, Natural and Field Experiments An easy lesson idea for learning about experiments.

Travis Dixon September 29, 2016 Research Methodology

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There is a difference between a “true experiment” a “field experiment” and  a “natural experiment”. These separate experimental methods are commonly used in psychological research and they each have their strengths and limitations.

True Experiments

Berry’s classic study compared two cultures in order to understand how economics, parenting and cultural values can influence behaviour. But what type of method would we call this?

A true experiment is one where:

• have randomly assigned participants to a condition (if using independent samples)

Repeated measures designs don’t need random allocation because there is no allocation as all participants do both conditions.

One potential issue in laboratory experiments is that they are conducted in environments that are not natural for the participants, so the behaviour might not reflect what happens in real life.

Field Experiments

A field experiment is one where:

• the researcher conducts an experiment by manipulating an IV,
• …and measuring the effects on the DV in a natural environment.

They still try to minimize the effects of other variables and to control for these, but it’s just happening in a natural environment: the field.

• Natural Experiment

A natural experiment is one where:

• the independent variable is naturally occurring. i.e. it hasn’t been manipulated by the researcher.

There are many instances where naturally occurring events or phenomenon may interest researchers. The issue with natural experiments is that it can’t be guaranteed that it is the independent variable that is having an effect on the dependent variable.

• Quantitative Research Methods Glossary
• Let’s STOP the research methods madness!
• What makes an experiment “quasi”?

Activity Idea

Students can work with a partner to decide if the following are true, field or natural experiments.

If you cant’ decide, what other information do you need?

• Berry’s cross-cultural study on conformity ( Key Study: Conformity Across Cultures (Berry, 1967)
• Bandura’s bobo doll study ( Key Study: Bandura’s Bobo Doll (1963)
• Rosenzweig’s rat study ( Key Study: Animal research on neuroplasticity (Rosenzweig and Bennett, 1961)

Let’s make it a bit trickier:

• Key Study: London Taxi Drivers vs. Bus Drivers (Maguire, 2006)
• Key Study: Evolution of Gender Differences in Sexual Behaviour (Clark and Hatfield, 1989)
• Key Study: Serotonin, tryptophan and the brain (Passamonti et al., 2012)
• Saint Helena Study : television was introduced on the island of Saint Helena in the Atlantic ocean and the researchers measured the behaviour of the kids before and after TV was introduced.
• Light Therapy : the researchers randomly assigned patients with depression into three different groups. The three groups received different forms of light therapy to treat depression (red light, bright light, soft light). The lights were installed in the participants’ bedrooms and were timed to come on naturally. The effects on depression were measured via interviews.

What are the strengths and limitations of:

• True Experiment 
• Field Experiment 

Travis Dixon is an IB Psychology teacher, author, workshop leader, examiner and IA moderator.

• A-Z Publications

Annual Review of Public Health

Volume 38, 2017, review article, open access, natural experiments: an overview of methods, approaches, and contributions to public health intervention research.

• Peter Craig 1 , Srinivasa Vittal Katikireddi 1 , Alastair Leyland 1 , and Frank Popham 1
• View Affiliations Hide Affiliations Affiliations: MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow G2 3QB, United Kingdom; email: [email protected] , [email protected] , [email protected] , [email protected]
• Vol. 38:39-56 (Volume publication date March 2017) https://doi.org/10.1146/annurev-publhealth-031816-044327
• First published as a Review in Advance on January 11, 2017
• Copyright © 2017 Annual Reviews. This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 (CC-BY-SA) International License, which permits unrestricted use, distribution, and reproduction in any medium and any derivative work is made available under the same, similar, or a compatible license. See credit lines of images or other third-party material in this article for license information.

Population health interventions are essential to reduce health inequalities and tackle other public health priorities, but they are not always amenable to experimental manipulation. Natural experiment (NE) approaches are attracting growing interest as a way of providing evidence in such circumstances. One key challenge in evaluating NEs is selective exposure to the intervention. Studies should be based on a clear theoretical understanding of the processes that determine exposure. Even if the observed effects are large and rapidly follow implementation, confidence in attributing these effects to the intervention can be improved by carefully considering alternative explanations. Causal inference can be strengthened by including additional design features alongside the principal method of effect estimation. NE studies often rely on existing (including routinely collected) data. Investment in such data sources and the infrastructure for linking exposure and outcome data is essential if the potential for such studies to inform decision making is to be realized.

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Types of Experiment: Overview

Last updated 6 Sept 2022

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Different types of methods are used in research, which loosely fall into 1 of 2 categories.

Experimental (Laboratory, Field & Natural) & N on experimental ( correlations, observations, interviews, questionnaires and case studies).

All the three types of experiments have characteristics in common. They all have:

• an independent variable (I.V.) which is manipulated or a naturally occurring variable
• a dependent variable (D.V.) which is measured
• there will be at least two conditions in which participants produce data.

Note – natural and quasi experiments are often used synonymously but are not strictly the same, as with quasi experiments participants cannot be randomly assigned, so rather than there being a condition there is a condition.

Laboratory Experiments

These are conducted under controlled conditions, in which the researcher deliberately changes something (I.V.) to see the effect of this on something else (D.V.).

Control – lab experiments have a high degree of control over the environment & other extraneous variables which means that the researcher can accurately assess the effects of the I.V, so it has higher internal validity.

Replicable – due to the researcher’s high levels of control, research procedures can be repeated so that the reliability of results can be checked.

Limitations

Lacks ecological validity – due to the involvement of the researcher in manipulating and controlling variables, findings cannot be easily generalised to other (real life) settings, resulting in poor external validity.

Field Experiments

These are carried out in a natural setting, in which the researcher manipulates something (I.V.) to see the effect of this on something else (D.V.).

Validity – field experiments have some degree of control but also are conducted in a natural environment, so can be seen to have reasonable internal and external validity.

Less control than lab experiments and therefore extraneous variables are more likely to distort findings and so internal validity is likely to be lower.

Natural / Quasi Experiments

These are typically carried out in a natural setting, in which the researcher measures the effect of something which is to see the effect of this on something else (D.V.). Note that in this case there is no deliberate manipulation of a variable; this already naturally changing, which means the research is merely measuring the effect of something that is already happening.

High ecological validity – due to the lack of involvement of the researcher; variables are naturally occurring so findings can be easily generalised to other (real life) settings, resulting in high external validity.

Lack of control – natural experiments have no control over the environment & other extraneous variables which means that the researcher cannot always accurately assess the effects of the I.V, so it has low internal validity.

Not replicable – due to the researcher’s lack of control, research procedures cannot be repeated so that the reliability of results cannot be checked.

• Laboratory Experiment
• Field experiment
• Quasi Experiment
• Natural Experiment
• Field experiments

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