difference between lab and field experiment psychology

Experimental Method In Psychology

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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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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The experimental method involves the manipulation of variables to establish cause-and-effect relationships. The key features are controlled methods and the random allocation of participants into controlled and experimental groups .

What is an Experiment?

An experiment is an investigation in which a hypothesis is scientifically tested. An independent variable (the cause) is manipulated in an experiment, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

An advantage is that experiments should be objective. The researcher’s views and opinions should not affect a study’s results. This is good as it makes the data more valid  and less biased.

There are three types of experiments you need to know:

1. Lab Experiment

A laboratory experiment in psychology is a research method in which the experimenter manipulates one or more independent variables and measures the effects on the dependent variable under controlled conditions.

A laboratory experiment is conducted under highly controlled conditions (not necessarily a laboratory) where accurate measurements are possible.

The researcher uses a standardized procedure to determine where the experiment will take place, at what time, with which participants, and in what circumstances.

Participants are randomly allocated to each independent variable group.

Examples are Milgram’s experiment on obedience and  Loftus and Palmer’s car crash study .

• Strength : It is easier to replicate (i.e., copy) a laboratory experiment. This is because a standardized procedure is used.
• Strength : They allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.
• Limitation : The artificiality of the setting may produce unnatural behavior that does not reflect real life, i.e., low ecological validity. This means it would not be possible to generalize the findings to a real-life setting.
• Limitation : Demand characteristics or experimenter effects may bias the results and become confounding variables .

2. Field Experiment

A field experiment is a research method in psychology that takes place in a natural, real-world setting. It is similar to a laboratory experiment in that the experimenter manipulates one or more independent variables and measures the effects on the dependent variable.

However, in a field experiment, the participants are unaware they are being studied, and the experimenter has less control over the extraneous variables .

Field experiments are often used to study social phenomena, such as altruism, obedience, and persuasion. They are also used to test the effectiveness of interventions in real-world settings, such as educational programs and public health campaigns.

An example is Holfing’s hospital study on obedience .

• Strength : behavior in a field experiment is more likely to reflect real life because of its natural setting, i.e., higher ecological validity than a lab experiment.
• Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied. This occurs when the study is covert.
• Limitation : There is less control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

3. Natural Experiment

A natural experiment in psychology is a research method in which the experimenter observes the effects of a naturally occurring event or situation on the dependent variable without manipulating any variables.

Natural experiments are conducted in the day (i.e., real life) environment of the participants, but here, the experimenter has no control over the independent variable as it occurs naturally in real life.

Natural experiments are often used to study psychological phenomena that would be difficult or unethical to study in a laboratory setting, such as the effects of natural disasters, policy changes, or social movements.

For example, Hodges and Tizard’s attachment research (1989) compared the long-term development of children who have been adopted, fostered, or returned to their mothers with a control group of children who had spent all their lives in their biological families.

Here is a fictional example of a natural experiment in psychology:

Researchers might compare academic achievement rates among students born before and after a major policy change that increased funding for education.

In this case, the independent variable is the timing of the policy change, and the dependent variable is academic achievement. The researchers would not be able to manipulate the independent variable, but they could observe its effects on the dependent variable.

• Strength : behavior in a natural experiment is more likely to reflect real life because of its natural setting, i.e., very high ecological validity.
• Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied.
• Strength : It can be used in situations in which it would be ethically unacceptable to manipulate the independent variable, e.g., researching stress .
• Limitation : They may be more expensive and time-consuming than lab experiments.
• Limitation : There is no control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

Key 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. EVs 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 participating 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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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.

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• 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.

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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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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6.1 Experiment Basics

Learning objectives.

• Explain what an experiment is and recognize examples of studies that are experiments and studies that are not experiments.
• Explain what internal validity is and why experiments are considered to be high in internal validity.
• Explain what external validity is and evaluate studies in terms of their external validity.
• Distinguish between the manipulation of the independent variable and control of extraneous variables and explain the importance of each.
• Recognize examples of confounding variables and explain how they affect the internal validity of a study.

What Is an Experiment?

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

Internal and External Validity

Internal validity.

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

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

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

External Validity

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

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

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

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

Manipulation of the Independent Variable

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

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

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

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

Control of Extraneous Variables

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

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 6.1 “Hypothetical Noiseless Data and Realistic Noisy Data” 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 6.1 “Hypothetical Noiseless Data and Realistic Noisy Data” . Even in the happy mood condition, some participants would recall fewer happy memories because they have fewer to draw on, use less effective 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 6.1 “Hypothetical Noiseless Data and Realistic Noisy Data” , which makes the effect of the independent variable is easier to detect (although real data never look quite that good).

Table 6.1 Hypothetical Noiseless Data and Realistic Noisy Data

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

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

Extraneous Variables as Confounding Variables

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

To confound means to confuse, and this is exactly what confounding variables do. Because they differ across conditions—just like the independent variable—they provide an alternative explanation for any observed difference in the dependent variable. Figure 6.1 “Hypothetical Results From a Study on the Effect of Mood on Memory” shows the results of a hypothetical study, in which participants in a positive mood condition scored higher on a memory task than participants in a negative mood condition. But if IQ is a confounding variable—with participants in the positive mood condition having higher IQs on average than participants in the negative mood condition—then it is unclear whether it was the positive moods or the higher IQs that caused participants in the first condition to score higher. One way to avoid confounding variables is by holding extraneous variables constant. For example, one could prevent IQ from becoming a confounding variable by limiting participants only to those with IQs of exactly 100. But this approach is not always desirable for reasons we have already discussed. A second and much more general approach—random assignment to conditions—will be discussed in detail shortly.

Figure 6.1 Hypothetical Results From a Study on the Effect of Mood on Memory

Because IQ also differs across conditions, it is a confounding variable.

Key Takeaways

• An experiment is a type of empirical study that features the manipulation of an independent variable, the measurement of a dependent variable, and control of extraneous variables.
• Studies are high in internal validity to the extent that the way they are conducted supports the conclusion that the independent variable caused any observed differences in the dependent variable. Experiments are generally high in internal validity because of the manipulation of the independent variable and control of extraneous variables.
• Studies are high in external validity to the extent that the result can be generalized to people and situations beyond those actually studied. Although experiments can seem “artificial”—and low in external validity—it is important to consider whether the psychological processes under study are likely to operate in other people and situations.
• Practice: List five variables that can be manipulated by the researcher in an experiment. List five variables that cannot be manipulated by the researcher in an experiment.

Practice: For each of the following topics, decide whether that topic could be studied using an experimental research design and explain why or why not.

• Effect of parietal lobe damage on people’s ability to do basic arithmetic.
• Effect of being clinically depressed on the number of close friendships people have.
• Effect of group training on the social skills of teenagers with Asperger’s syndrome.
• Effect of paying people to take an IQ test on their performance on that test.

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

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

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

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Field experiments, explained

Editor’s note: This is part of a series called “The Day Tomorrow Began,” which explores the history of breakthroughs at UChicago.  Learn more here.

A field experiment is a research method that uses some controlled elements of traditional lab experiments, but takes place in natural, real-world settings. This type of experiment can help scientists explore questions like: Why do people vote the way they do? Why do schools fail? Why are certain people hired less often or paid less money?

University of Chicago economists were early pioneers in the modern use of field experiments and conducted innovative research that impacts our everyday lives—from policymaking to marketing to farming and agriculture.  

Jump to a section:

What is a field experiment, why do a field experiment, what are examples of field experiments, when did field experiments become popular in modern economics, what are criticisms of field experiments.

Field experiments bridge the highly controlled lab environment and the messy real world. Social scientists have taken inspiration from traditional medical or physical science lab experiments. In a typical drug trial, for instance, participants are randomly assigned into two groups. The control group gets the placebo—a pill that has no effect. The treatment group will receive the new pill. The scientist can then compare the outcomes for each group.

A field experiment works similarly, just in the setting of real life.

It can be difficult to understand why a person chooses to buy one product over another or how effective a policy is when dozens of variables affect the choices we make each day. “That type of thinking, for centuries, caused economists to believe you can't do field experimentation in economics because the market is really messy,” said Prof. John List, a UChicago economist who has used field experiments to study everything from how people use  Uber and  Lyft to  how to close the achievement gap in Chicago-area schools . “There are a lot of things that are simultaneously moving.”

The key to cleaning up the mess is randomization —or assigning participants randomly to either the control group or the treatment group. “The beauty of randomization is that each group has the same amount of bad stuff, or noise or dirt,” List said. “That gets differenced out if you have large enough samples.”

Though lab experiments are still common in the social sciences, field experiments are now often used by psychologists, sociologists and political scientists. They’ve also become an essential tool in the economist’s toolbox.  

Some issues are too big and too complex to study in a lab or on paper—that’s where field experiments come in.

In a laboratory setting, a researcher wants to control as many variables as possible. These experiments are excellent for testing new medications or measuring brain functions, but they aren’t always great for answering complex questions about attitudes or behavior.

Labs are highly artificial with relatively small sample sizes—it’s difficult to know if results will still apply in the real world. Also, people are aware they are being observed in a lab, which can alter their behavior. This phenomenon, sometimes called the Hawthorne effect, can affect results.

Traditional economics often uses theories or existing data to analyze problems. But, when a researcher wants to study if a policy will be effective or not, field experiments are a useful way to look at how results may play out in real life.

In 2019, UChicago economist Michael Kremer (then at Harvard) was awarded the Nobel Prize alongside Abhijit Banerjee and Esther Duflo of MIT for their groundbreaking work using field experiments to help reduce poverty . In the 1990s and 2000s, Kremer conducted several randomized controlled trials in Kenyan schools testing potential interventions to improve student performance. 

In the 1990s, Kremer worked alongside an NGO to figure out if buying students new textbooks made a difference in academic performance. Half the schools got new textbooks; the other half didn’t. The results were unexpected—textbooks had no impact.

“Things we think are common sense, sometimes they turn out to be right, sometimes they turn out to be wrong,” said Kremer on an episode of  the Big Brains podcast. “And things that we thought would have minimal impact or no impact turn out to have a big impact.”

In the early 2000s, Kremer returned to Kenya to study a school-based deworming program. He and a colleague found that providing deworming pills to all students reduced absenteeism by more than 25%. After the study, the program was scaled nationwide by the Kenyan government. From there it was picked up by multiple Indian states—and then by the Indian national government.

“Experiments are a way to get at causal impact, but they’re also much more than that,” Kremer said in  his Nobel Prize lecture . “They give the researcher a richer sense of context, promote broader collaboration and address specific practical problems.”    

Among many other things, field experiments can be used to:

Study bias and discrimination

A 2004 study published by UChicago economists Marianne Bertrand and Sendhil Mullainathan (then at MIT) examined racial discrimination in the labor market. They sent over 5,000 resumes to real job ads in Chicago and Boston. The resumes were exactly the same in all ways but one—the name at the top. Half the resumes bore white-sounding names like Emily Walsh or Greg Baker. The other half sported African American names like Lakisha Washington or Jamal Jones. The study found that applications with white-sounding names were 50% more likely to receive a callback.

Examine voting behavior

Political scientist Harold Gosnell , PhD 1922, pioneered the use of field experiments to examine voting behavior while at UChicago in the 1920s and ‘30s. In his study “Getting out the vote,” Gosnell sorted 6,000 Chicagoans across 12 districts into groups. One group received voter registration info for the 1924 presidential election and the control group did not. Voter registration jumped substantially among those who received the informational notices. Not only did the study prove that get-out-the-vote mailings could have a substantial effect on voter turnout, but also that field experiments were an effective tool in political science.

Test ways to reduce crime and shape public policy

Researchers at UChicago’s  Crime Lab use field experiments to gather data on crime as well as policies and programs meant to reduce it. For example, Crime Lab director and economist Jens Ludwig co-authored a  2015 study on the effectiveness of the school mentoring program  Becoming a Man . Developed by the non-profit Youth Guidance, Becoming a Man focuses on guiding male students between 7th and 12th grade to help boost school engagement and reduce arrests. In two field experiments, the Crime Lab found that while students participated in the program, total arrests were reduced by 28–35%, violent-crime arrests went down by 45–50% and graduation rates increased by 12–19%.

The earliest field experiments took place—literally—in fields. Starting in the 1800s, European farmers began experimenting with fertilizers to see how they affected crop yields. In the 1920s, two statisticians, Jerzy Neyman and Ronald Fisher, were tasked with assisting with these agricultural experiments. They are credited with identifying randomization as a key element of the method—making sure each plot had the same chance of being treated as the next.

The earliest large-scale field experiments in the U.S. took place in the late 1960s to help evaluate various government programs. Typically, these experiments were used to test minor changes to things like electricity pricing or unemployment programs.

Though field experiments were used in some capacity throughout the 20th century, this method didn’t truly gain popularity in economics until the 2000s. Kremer and List were early pioneers and first began experimenting with the method in the 1990s.

In 2004, List co-authored  a seminal paper defining field experiments and arguing for the importance of the method. In 2008,  he and UChicago economist Steven Levitt published another study tracing the history of field experiments and their impact on economics.

In the past few decades, the use of field experiments has exploded. Today, economists often work alongside NGOs or nonprofit organizations to study the efficacy of programs or policies. They also partner with companies to test products and understand how people use services.  

There are several  ethical discussions happening among scholars as field experiments grow in popularity. Chief among them is the issue of informed consent. All studies that involve human test subjects must be approved by an institutional review board (IRB) to ensure that people are protected.

However, participants in field experiments often don’t know they are in an experiment. While an experiment may be given the stamp of approval in the research community, some argue that taking away peoples’ ability to opt out is inherently unethical. Others advocate for stricter review processes as field experiments continue to evolve.

According to List, another major issue in field experiments is the issue of scale . Many experiments only test small groups—say, dozens to hundreds of people. This may mean the results are not applicable to broader situations. For example, if a scientist runs an experiment at one school and finds their method works there, does that mean it will also work for an entire city? Or an entire country?

List believes that in addition to testing option A and option B, researchers need a third option that accounts for the limitations that come with a larger scale. “Option C is what I call critical scale features. I want you to bring in all of the warts, all of the constraints, whether they're regulatory constraints, or constraints by law,” List said. “Option C is like your reality test, or what I call policy-based evidence.”

This problem isn’t unique to field experiments, but List believes tackling the issue of scale is the next major frontier for a new generation of economists.

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Field Experiments

Last updated 22 Mar 2021

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Experiments look for the effect that manipulated variables (independent variables) have on measured variables (dependent variables), i.e. causal effects.

Field experiments are conducted in a natural setting (e.g. at a sports event or on public transport), as opposed to the artificial environment created in laboratory experiments. Some variables cannot be controlled due to the unpredictability of these real-life settings (e.g. the public interacting with participants), but an independent variable will still be altered for a dependent variable to be measured against.

Evaluation of field experiments:

- Field experiments generally yield results with higher ecological validity than laboratory experiments, as the natural settings will relate to real life.

- Demand characteristics are less of an issue with field experiments than laboratory experiments (i.e. participants are less likely to adjust their natural behaviour according to their interpretation of the study’s purpose, as they might not know they are in a study).

- Extraneous variables could confound results due to the reduced control experimenters have over them in non-artificial environments, which makes it difficult to find truly causal effects between independent and dependent variables.

- Ethical principles have to be considered, such as the lack of informed consent; if participants are not made aware of their participation in an experiment, privacy must be respected during observations and participants must be debriefed appropriately when observations come to an end.

- Precise replication of the natural environment of field experiments is understandably difficult, so they have poor reliability, unlike laboratory experiments where the exact conditions can be recreated.

- Field experiments are more susceptible to sample bias, as participants are often not randomly allocated to experimental conditions (i.e. participants’ groups are already pre-set rather than randomly assigned).

• Field experiments

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How Does Experimental Psychology Study Behavior?

Purpose, methods, and history

• Why It Matters

What factors influence people's behaviors and thoughts? Experimental psychology utilizes scientific methods to answer these questions by researching the mind and behavior. Experimental psychologists conduct experiments to learn more about why people do certain things.

Overview of Experimental Psychology

Why do people do the things they do? What factors influence how personality develops? And how do our behaviors and experiences shape our character?

These are just a few of the questions that psychologists explore, and experimental methods allow researchers to create and empirically test hypotheses. By studying such questions, researchers can also develop theories that enable them to describe, explain, predict, and even change human behaviors.

For example, researchers might utilize experimental methods to investigate why people engage in unhealthy behaviors. By learning more about the underlying reasons why these behaviors occur, researchers can then search for effective ways to help people avoid such actions or replace unhealthy choices with more beneficial ones.

Why Experimental Psychology Matters

While students are often required to take experimental psychology courses during undergraduate and graduate school , think about this subject as a methodology rather than a singular area within psychology. People in many subfields of psychology use these techniques to conduct research on everything from childhood development to social issues.

Experimental psychology is important because the findings play a vital role in our understanding of the human mind and behavior.

By better understanding exactly what makes people tick, psychologists and other mental health professionals can explore new approaches to treating psychological distress and mental illness. These are often topics of experimental psychology research.

Experimental Psychology Methods

So how exactly do researchers investigate the human mind and behavior? Because the mind is so complex, it seems like a challenging task to explore the many factors that contribute to how we think, act, and feel.

Experimental psychologists use a variety of different research methods and tools to investigate human behavior. Methods in the experimental psychology category include experiments, case studies, correlational research, and naturalistic observations.

Experiments

Experimentation remains the primary standard in psychological research. In some cases, psychologists can perform experiments to determine if there is a cause-and-effect relationship between different variables.

The basics of conducting a psychology experiment involve:

• Randomly assigning participants to groups
• Operationally defining variables
• Developing a hypothesis
• Manipulating independent variables
• Measuring dependent variables

One experimental psychology research example would be to perform a study to look at whether sleep deprivation impairs performance on a driving test. The experimenter could control other variables that might influence the outcome, varying the amount of sleep participants get the night before.

All of the participants would then take the same driving test via a simulator or on a controlled course. By analyzing the results, researchers can determine if changes in the independent variable (amount of sleep) led to differences in the dependent variable (performance on a driving test).

Case Studies

Case studies allow researchers to study an individual or group of people in great depth. When performing a case study, the researcher collects every single piece of data possible, often observing the person or group over a period of time and in a variety of situations. They also collect detailed information about their subject's background—including family history, education, work, and social life—is also collected.

Such studies are often performed in instances where experimentation is not possible. For example, a scientist might conduct a case study when the person of interest has had a unique or rare experience that could not be replicated in a lab.

Correlational Research

Correlational studies are an experimental psychology method that makes it possible for researchers to look at relationships between different variables. For example, a psychologist might note that as one variable increases, another tends to decrease.

While such studies can look at relationships, they cannot be used to imply causal relationships. The golden rule is that correlation does not equal causation.

Naturalistic Observations

Naturalistic observation gives researchers the opportunity to watch people in their natural environments. This experimental psychology method can be particularly useful in cases where the investigators believe that a lab setting might have an undue influence on participant behaviors.

What Experimental Psychologists Do

Experimental psychologists work in a wide variety of settings, including colleges, universities, research centers, government, and private businesses. Some of these professionals teach experimental methods to students while others conduct research on cognitive processes, animal behavior, neuroscience, personality, and other subject areas.

Those who work in academic settings often teach psychology courses in addition to performing research and publishing their findings in professional journals. Other experimental psychologists work with businesses to discover ways to make employees more productive or to create a safer workplace—a specialty area known as human factors psychology .

Experimental Psychology Research Examples

Some topics that might be explored in experimental psychology research include how music affects motivation, the impact social media has on mental health , and whether a certain color changes one's thoughts or perceptions.

History of Experimental Psychology

To understand how experimental psychology got where it is today, it can be helpful to look at how it originated. Psychology is a relatively young discipline, emerging in the late 1800s. While it started as part of philosophy and biology, it officially became its own field of study when early psychologist Wilhelm Wundt founded the first laboratory devoted to the study of experimental psychology.

Some of the important events that helped shape the field of experimental psychology include:

• 1874 - Wilhelm Wundt published the first experimental psychology textbook, "Grundzüge der physiologischen Psychologie" ("Principles of Physiological Psychology").
• 1875 - William James opened a psychology lab in the United States. The lab was created for the purpose of class demonstrations rather than to perform original experimental research.
• 1879 - The first experimental psychology lab was founded in Leipzig, Germany. Modern experimental psychology dates back to the establishment of the very first psychology lab by pioneering psychologist Wilhelm Wundt during the late nineteenth century.
• 1883 - G. Stanley Hall opened the first experimental psychology lab in the United States at John Hopkins University.
• 1885 - Herman Ebbinghaus published his famous "Über das Gedächtnis" ("On Memory"), which was later translated to English as "Memory: A Contribution to Experimental Psychology." In the work, Ebbinghaus described learning and memory experiments that he conducted on himself.
• 1887 - George Truball Ladd published his textbook "Elements of Physiological Psychology," the first American book to include a significant amount of information on experimental psychology.
• 1887 - James McKeen Cattell established the world's third experimental psychology lab at the University of Pennsylvania.
• 1890 - William James published his classic textbook, "The Principles of Psychology."
• 1891 - Mary Whiton Calkins established an experimental psychology lab at Wellesley College, becoming the first woman to form a psychology lab.
• 1893 - G. Stanley Hall founded the American Psychological Association , the largest professional and scientific organization of psychologists in the United States.
• 1920 - John B. Watson and Rosalie Rayner conducted their now-famous Little Albert Experiment , in which they demonstrated that emotional reactions could be classically conditioned in people.
• 1929 - Edwin Boring's book "A History of Experimental Psychology" was published. Boring was an influential experimental psychologist who was devoted to the use of experimental methods in psychology research.
• 1955 - Lee Cronbach published "Construct Validity in Psychological Tests," which popularized the use of construct validity in psychological studies.
• 1958 - Harry Harlow published "The Nature of Love," which described his experiments with rhesus monkeys on attachment and love.
• 1961 - Albert Bandura conducted his famous Bobo doll experiment, which demonstrated the effects of observation on aggressive behavior.

Experimental Psychology Uses

While experimental psychology is sometimes thought of as a separate branch or subfield of psychology, experimental methods are widely used throughout all areas of psychology.

• Developmental psychologists use experimental methods to study how people grow through childhood and over the course of a lifetime.
• Social psychologists use experimental techniques to study how people are influenced by groups.
• Health psychologists rely on experimentation and research to better understand the factors that contribute to wellness and disease.

A Word From Verywell

The experimental method in psychology helps us learn more about how people think and why they behave the way they do. Experimental psychologists can research a variety of topics using many different experimental methods. Each one contributes to what we know about the mind and human behavior.

Shaughnessy JJ, Zechmeister EB, Zechmeister JS. Research Methods in Psychology . McGraw-Hill.

Heale R, Twycross A. What is a case study? . Evid Based Nurs. 2018;21(1):7-8. doi:10.1136/eb-2017-102845

Chiang IA, Jhangiani RS, Price PC.  Correlational research . In: Research Methods in Psychology, 2nd Canadian edition. BCcampus Open Education.

Pierce T.  Naturalistic observation . Radford University.

Kantowitz BH, Roediger HL, Elmes DG. Experimental Psychology . Cengage Learning.

Weiner IB, Healy AF, Proctor RW. Handbook of Psychology: Volume 4, Experimental Psychology . John Wiley & Sons.

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

Chapter 6: Experimental Research

Experiment basics, learning objectives.

• Explain what an experiment is and recognize examples of studies that are experiments and studies that are not experiments.
• Explain what internal validity is and why experiments are considered to be high in internal validity.
• Explain what external validity is and evaluate studies in terms of their external validity.
• Distinguish between the manipulation of the independent variable and control of extraneous variables and explain the importance of each.
• Recognize examples of confounding variables and explain how they affect the internal validity of a study.

What Is an Experiment?

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

Four Big Validities

When we read about psychology experiments with a critical view, one question to ask is “is this study valid?” However, that question is not as straightforward as it seems because in psychology, there are many different kinds of validities. Researchers have focused on four validities to help assess whether an experiment is sound (Judd & Kenny, 1981; Morling, 2014) [1] [2] : internal validity, external validity, construct validity, and statistical validity. We will explore each validity in depth.

Internal Validity

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

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

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

External Validity

At the same time, the way that experiments are conducted sometimes leads to a different kind of criticism. Specifically, the need to manipulate the independent variable and control extraneous variables means that experiments are often conducted under conditions that seem artificial (Bauman, McGraw, Bartels, & Warren, 2014) [3] . In many psychology experiments, the participants are all undergraduate students and come to a classroom or laboratory to fill out a series of paper-and-pencil questionnaires or to perform a carefully designed computerized task. Consider, for example, an experiment in which researcher Barbara Fredrickson and her colleagues had undergraduate students come to a laboratory on campus and complete a math test while wearing a swimsuit (Fredrickson, Roberts, Noll, Quinn, & Twenge, 1998) [4] . At first, this manipulation might seem silly. When will undergraduate students ever have to complete math tests in their swimsuits outside of this experiment?

The issue we are confronting is that of external validity . An empirical study is high in external validity if the way it was conducted supports generalizing the results to people and situations beyond those actually studied. As a general rule, studies are higher in external validity when the participants and the situation studied are similar to those that the researchers want to generalize to and participants encounter everyday, often described as mundane realism . Imagine, for example, that a group of researchers is interested in how shoppers in large grocery stores are affected by whether breakfast cereal is packaged in yellow or purple boxes. Their study would be high in external validity and have high mundane realism if they studied the decisions of ordinary people doing their weekly shopping in a real grocery store. If the shoppers bought much more cereal in purple boxes, the researchers would be fairly confident that this increase would be true for other shoppers in other stores. Their study would be relatively low in external validity, however, if they studied a sample of undergraduate students in a laboratory at a selective university who merely judged the appeal of various colours presented on a computer screen; however, this study would have high psychological realism where the same mental process is used in both the laboratory and in the real world.  If the students judged purple to be more appealing than yellow, the researchers would not be very confident that this preference is relevant to grocery shoppers’ cereal-buying decisions because of low external validity but they could be confident that the visual processing of colours has high psychological realism.

We should be careful, however, not to draw the blanket conclusion that experiments are low in external validity. One reason is that experiments need not seem artificial. Consider that Darley and Latané’s experiment provided a reasonably good simulation of a real emergency situation. Or consider field experiments  that are conducted entirely outside the laboratory. In one such experiment, Robert Cialdini and his colleagues studied whether hotel guests choose to reuse their towels for a second day as opposed to having them washed as a way of conserving water and energy (Cialdini, 2005) [5] . These researchers manipulated the message on a card left in a large sample of hotel rooms. One version of the message emphasized showing respect for the environment, another emphasized that the hotel would donate a portion of their savings to an environmental cause, and a third emphasized that most hotel guests choose to reuse their towels. The result was that guests who received the message that most hotel guests choose to reuse their towels reused their own towels substantially more often than guests receiving either of the other two messages. Given the way they conducted their study, it seems very likely that their result would hold true for other guests in other hotels.

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

Construct Validity

In addition to the generalizability of the results of an experiment, another element to scrutinize in a study is the quality of the experiment’s manipulations, or the construct validity . The research question that Darley and Latané started with is “does helping behaviour become diffused?” They hypothesized that participants in a lab would be less likely to help when they believed there were more potential helpers besides themselves. This conversion from research question to experiment design is called operationalization (see Chapter 2 for more information about the operational definition). Darley and Latané operationalized the independent variable of diffusion of responsibility by increasing the number of potential helpers. In evaluating this design, we would say that the construct validity was very high because the experiment’s manipulations very clearly speak to the research question; there was a crisis, a way for the participant to help, and increasing the number of other students involved in the discussion, they provided a way to test diffusion.

What if the number of conditions in Darley and Latané’s study changed? Consider if there were only two conditions: one student involved in the discussion or two. Even though we may see a decrease in helping by adding another person, it may not be a clear demonstration of diffusion of responsibility, just merely the presence of others. We might think it was a form of Bandura’s social inhibition  (discussed in Chapter 4 ). The construct validity would be lower. However, had there been five conditions, perhaps we would see the decrease continue with more people in the discussion or perhaps it would plateau after a certain number of people. In that situation, we may not necessarily be learning more about diffusion of responsibility or it may become a different phenomenon. By adding more conditions, the construct validity may not get higher. When designing your own experiment, consider how well the research question is operationalized your study.

Statistical Validity

A common critique of experiments is that a study did not have enough participants. The main reason for this criticism is that it is difficult to generalize about a population from a small sample. At the outset, it seems as though this critique is about external validity but there are studies where small sample sizes are not a problem ( Chapter 10 will discuss how small samples, even of only 1 person, are still very illuminating for psychology research). Therefore, small sample sizes are actually a critique of statistical validity . The statistical validity speaks to whether the statistics conducted in the study support the conclusions that are made.

Proper statistical analysis should be conducted on the data to determine whether the difference or relationship that was predicted was found. The number of conditions and the number of total participants will determine the overall size of the effect. With this information, a power analysis can be conducted to ascertain whether you are likely to find a real difference. When designing a study, it is best to think about the power analysis so that the appropriate number of participants can be recruited and tested (more on effect sizes in Chapter 12 ). To design a statistically valid experiment, thinking about the statistical tests at the beginning of the design will help ensure the results can be believed.

Prioritizing Validities

These four big validities–internal, external, construct, and statistical–are useful to keep in mind when both reading about other experiments and designing your own. However, researchers must prioritize and often it is not possible to have high validity in all four areas. In Cialdini’s study on towel usage in hotels, the external validity was high but the statistical validity was more modest. This discrepancy does not invalidate the study but it shows where there may be room for improvement for future follow-up studies (Goldstein, Cialdini, & Griskevicius, 2008) [6] . Morling (2014) points out that most psychology studies have high internal and construct validity but sometimes sacrifice external validity.

Manipulation of the Independent Variable

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

Notice that the manipulation of an independent variable must involve the active intervention of the researcher. Comparing groups of people who differ on the independent variable before the study begins is not the same as manipulating that variable. For example, a researcher who compares the health of people who already keep a journal with the health of people who do not keep a journal has not manipulated this variable and therefore 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 the third-variable problem.

Of course, there are many situations in which the independent variable cannot be manipulated for practical or ethical reasons and therefore an experiment is not possible. For example, whether or not people have a significant early illness experience cannot be manipulated, making it impossible to 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.

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

Control of Extraneous Variables

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 shoe size. 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 6.1 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 6.1 . 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 6.1 , which makes the effect of the independent variable easier to detect (although real data never look quite  that  good).

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

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

Extraneous Variables as Confounding Variables

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

To confound means to confuse , and this effect is exactly why confounding variables are undesirable. Because they differ across conditions—just like the independent variable—they provide an alternative explanation for any observed difference in the dependent variable.  Figure 6.1  shows the results of a hypothetical study, in which participants in a positive mood condition scored higher on a memory task than participants in a negative mood condition. But if IQ is a confounding variable—with participants in the positive mood condition having higher IQs on average than participants in the negative mood condition—then it is unclear whether it was the positive moods or the higher IQs that caused participants in the first condition to score higher. One way to avoid confounding variables is by holding extraneous variables constant. For example, one could prevent IQ from becoming a confounding variable by limiting participants only to those with IQs of exactly 100. But this approach is not always desirable for reasons we have already discussed. A second and much more general approach—random assignment to conditions—will be discussed in detail shortly.

Figure 6.1 Hypothetical Results From a Study on the Effect of Mood on Memory. Because IQ also differs across conditions, it is a confounding variable.

Key Takeaways

• An experiment is a type of empirical study that features the manipulation of an independent variable, the measurement of a dependent variable, and control of extraneous variables.
• Studies are high in internal validity to the extent that the way they are conducted supports the conclusion that the independent variable caused any observed differences in the dependent variable. Experiments are generally high in internal validity because of the manipulation of the independent variable and control of extraneous variables.
• Studies are high in external validity to the extent that the result can be generalized to people and situations beyond those actually studied. Although experiments can seem “artificial”—and low in external validity—it is important to consider whether the psychological processes under study are likely to operate in other people and situations.
• Practice: List five variables that can be manipulated by the researcher in an experiment. List five variables that cannot be manipulated by the researcher in an experiment.
• Effect of parietal lobe damage on people’s ability to do basic arithmetic.
• Effect of being clinically depressed on the number of close friendships people have.
• Effect of group training on the social skills of teenagers with Asperger’s syndrome.
• Effect of paying people to take an IQ test on their performance on that test.
• Judd, C.M. & Kenny, D.A. (1981). Estimating the effects of social interventions . Cambridge, MA: Cambridge University Press. ↵
• Morling, B. (2014, April). Teach your students to be better consumers. APS Observer . Retrieved from http://www.psychologicalscience.org/index.php/publications/observer/2014/april-14/teach-your-students-to-be-better-consumers.html ↵
• Bauman, C.W., McGraw, A.P., Bartels, D.M., & Warren, C. (2014). Revisiting external validity: Concerns about trolley problems and other sacrificial dilemmas in moral psychology. Social and Personality Psychology Compass, 8/9 , 536-554. ↵
• Fredrickson, B. L., Roberts, T.-A., Noll, S. M., Quinn, D. M., & Twenge, J. M. (1998). The swimsuit becomes you: Sex differences in self-objectification, restrained eating, and math performance. Journal of Personality and Social Psychology, 75 , 269–284. ↵
• Cialdini, R. (2005, April). Don’t throw in the towel: Use social influence research. APS Observer . Retrieved from http://www.psychologicalscience.org/index.php/publications/observer/2005/april-05/dont-throw-in-the-towel-use-social-influence-research.html ↵
• Goldstein, N. J., Cialdini, R. B., & Griskevicius, V. (2008). A room with a viewpoint: Using social norms to motivate environmental conservation in hotels. Journal of Consumer Research, 35 , 472–482. ↵
• Research Methods in Psychology. Authored by : Paul C. Price, Rajiv S. Jhangiani, and I-Chant A. Chiang. Provided by : BCCampus. Located at : https://opentextbc.ca/researchmethods/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

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Experimental Research

25 Experimentation and Validity

Learning objectives.

• Explain what internal validity is and why experiments are considered to be high in internal validity.
• Explain what external validity is and evaluate studies in terms of their external validity.
• Explain the concepts of construct and statistical validity.

Four Big Validities

When we read about psychology experiments with a critical view, one question to ask is “is this study valid (accurate)?” However, that question is not as straightforward as it seems because, in psychology, there are many different kinds of validities. Researchers have focused on four validities to help assess whether an experiment is sound (Judd & Kenny, 1981; Morling, 2014) [1] [2] : internal validity, external validity, construct validity, and statistical validity. We will explore each validity in depth.

Internal Validity

Two variables being statistically related does not necessarily mean that one causes the other. In your psychology education, you have probably heard the term, “Correlation does not imply causation.” For example, if it were the case that people who exercise regularly are happier than people who do not exercise regularly, this implication would not necessarily mean that exercising increases people’s happiness. It could mean instead that greater happiness causes people to exercise or that something like better physical health causes people to exercise   and  be happier.

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

An empirical study is said to be high in  internal validity if the way it was conducted supports the conclusion that the independent variable caused any observed differences in the dependent variable. Thus experiments are high in internal validity because the way they are conducted—with the manipulation of the independent variable and the control of extraneous variables (such as through the use of random assignment to minimize confounds)—provides strong support for causal conclusions. In contrast, non-experimental research designs (e.g., correlational designs), in which variables are measured but are not manipulated by an experimenter, are low in internal validity.

External Validity

At the same time, the way that experiments are conducted sometimes leads to a different kind of criticism. Specifically, the need to manipulate the independent variable and control extraneous variables means that experiments are often conducted under conditions that seem artificial (Bauman, McGraw, Bartels, & Warren, 2014) [3] . In many psychology experiments, the participants are all undergraduate students and come to a classroom or laboratory to fill out a series of paper-and-pencil questionnaires or to perform a carefully designed computerized task. Consider, for example, an experiment in which researcher Barbara Fredrickson and her colleagues had undergraduate students come to a laboratory on campus and complete a math test while wearing a swimsuit (Fredrickson, Roberts, Noll, Quinn, & Twenge, 1998) [4] . At first, this manipulation might seem silly. When will undergraduate students ever have to complete math tests in their swimsuits outside of this experiment?

The issue we are confronting is that of external validity . An empirical study is high in external validity if the way it was conducted supports generalizing the results to people and situations beyond those actually studied. As a general rule, studies are higher in external validity when the participants and the situation studied are similar to those that the researchers want to generalize to and participants encounter every day, often described as mundane realism . Imagine, for example, that a group of researchers is interested in how shoppers in large grocery stores are affected by whether breakfast cereal is packaged in yellow or purple boxes. Their study would be high in external validity and have high mundane realism if they studied the decisions of ordinary people doing their weekly shopping in a real grocery store. If the shoppers bought much more cereal in purple boxes, the researchers would be fairly confident that this increase would be true for other shoppers in other stores. Their study would be relatively low in external validity, however, if they studied a sample of undergraduate students in a laboratory at a selective university who merely judged the appeal of various colors presented on a computer screen; however, this study would have high psychological realism where the same mental process is used in both the laboratory and in the real world.  If the students judged purple to be more appealing than yellow, the researchers would not be very confident that this preference is relevant to grocery shoppers’ cereal-buying decisions because of low external validity but they could be confident that the visual processing of colors has high psychological realism.

We should be careful, however, not to draw the blanket conclusion that experiments are low in external validity. One reason is that experiments need not seem artificial. Consider that Darley and Latané’s experiment provided a reasonably good simulation of a real emergency situation. Or consider field experiments  that are conducted entirely outside the laboratory. In one such experiment, Robert Cialdini and his colleagues studied whether hotel guests choose to reuse their towels for a second day as opposed to having them washed as a way of conserving water and energy (Cialdini, 2005) [5] . These researchers manipulated the message on a card left in a large sample of hotel rooms. One version of the message emphasized showing respect for the environment, another emphasized that the hotel would donate a portion of their savings to an environmental cause, and a third emphasized that most hotel guests choose to reuse their towels. The result was that guests who received the message that most hotel guests choose to reuse their towels, reused their own towels substantially more often than guests receiving either of the other two messages. Given the way they conducted their study, it seems very likely that their result would hold true for other guests in other hotels.

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

Construct Validity

In addition to the generalizability of the results of an experiment, another element to scrutinize in a study is the quality of the experiment’s manipulations or the construct validity . The research question that Darley and Latané started with is “does helping behavior become diffused?” They hypothesized that participants in a lab would be less likely to help when they believed there were more potential helpers besides themselves. This conversion from research question to experiment design is called operationalization (see Chapter 4 for more information about the operational definition). Darley and Latané operationalized the independent variable of diffusion of responsibility by increasing the number of potential helpers. In evaluating this design, we would say that the construct validity was very high because the experiment’s manipulations very clearly speak to the research question; there was a crisis, a way for the participant to help, and increasing the number of other students involved in the discussion, they provided a way to test diffusion.

What if the number of conditions in Darley and Latané’s study changed? Consider if there were only two conditions: one student involved in the discussion or two. Even though we may see a decrease in helping by adding another person, it may not be a clear demonstration of diffusion of responsibility, just merely the presence of others. We might think it was a form of Bandura’s concept of social inhibition. The construct validity would be lower. However, had there been five conditions, perhaps we would see the decrease continue with more people in the discussion or perhaps it would plateau after a certain number of people. In that situation, we may develop a more nuanced understanding of the phenomenon. But by adding still more conditions, the construct validity may not get higher. When designing your own experiment, consider how well the research question is operationalized your study.

Statistical Validity

Statistical validity concerns the proper statistical treatment of data and the soundness of the researchers’ statistical conclusions. There are many different types of inferential statistics tests (e.g.,  t- tests, ANOVA, regression, correlation) and statistical validity concerns the use of the proper type of test to analyze the data. When considering the proper type of test, researchers must consider the scale of measure their dependent variable was measured on and the design of their study. Further, many inferential statistics tests carry certain assumptions (e.g., the data are normally distributed) and statistical validity is threatened when these assumptions are not met but the statistics are used nonetheless.

One common critique of experiments is that a study did not have enough participants. The main reason for this criticism is that it is difficult to generalize about a population from a small sample. At the outset, it seems as though this critique is about external validity but there are studies where small sample sizes are not a problem (subsequent chapters will discuss how small samples, even of only one person, are still very illuminating for psychological research). Therefore, small sample sizes are actually a critique of statistical validity . The statistical validity speaks to whether the statistics conducted in the study are sound and support the conclusions that are made.

The proper statistical analysis should be conducted on the data to determine whether the difference or relationship that was predicted was indeed found. Interestingly, the likelihood of detecting an effect of the independent variable on the dependent variable depends on not just whether a relationship really exists between these variables, but also the number of conditions and the size of the sample. This is why it is important to conduct a power analysis when designing a study, which is a calculation that informs you of the number of participants you need to recruit to detect an effect of a specific size.

Prioritizing Validities

These four big validities–internal, external, construct, and statistical–are useful to keep in mind when both reading about other experiments and designing your own. However, researchers must prioritize and often it is not possible to have high validity in all four areas. In Cialdini’s study on towel usage in hotels, the external validity was high but the statistical validity was more modest. This discrepancy does not invalidate the study but it shows where there may be room for improvement for future follow-up studies (Goldstein, Cialdini, & Griskevicius, 2008) [6] . Morling (2014) points out that many psychology studies have high internal and construct validity but sometimes sacrifice external validity.

• Judd, C.M. & Kenny, D.A. (1981). Estimating the effects of social interventions . Cambridge, MA: Cambridge University Press. ↵
• Morling, B. (2014, April). Teach your students to be better consumers. APS Observer . Retrieved from http://www.psychologicalscience.org/index.php/publications/observer/2014/april-14/teach-your-students-to-be-better-consumers.html ↵
• Bauman, C.W., McGraw, A.P., Bartels, D.M., & Warren, C. (2014). Revisiting external validity: Concerns about trolley problems and other sacrificial dilemmas in moral psychology. Social and Personality Psychology Compass, 8/9 , 536-554. ↵
• Fredrickson, B. L., Roberts, T.-A., Noll, S. M., Quinn, D. M., & Twenge, J. M. (1998). The swimsuit becomes you: Sex differences in self-objectification, restrained eating, and math performance. Journal of Personality and Social Psychology, 75 , 269–284. ↵
• Cialdini, R. (2005, April). Don’t throw in the towel: Use social influence research. APS Observer . Retrieved from http://www.psychologicalscience.org/index.php/publications/observer/2005/april-05/dont-throw-in-the-towel-use-social-influence-research.html ↵
• Goldstein, N. J., Cialdini, R. B., & Griskevicius, V. (2008). A room with a viewpoint: Using social norms to motivate environmental conservation in hotels. Journal of Consumer Research, 35 , 472–482. ↵

Refers to the degree to which we can confidently infer a causal relationship between variables.

Refers to the degree to which we can generalize the findings to other circumstances or settings, like the real-world environment.

When the participants and the situation studied are similar to those that the researchers want to generalize to and participants encounter every day.

Where the same mental process is used in both the laboratory and in the real world.

One of the "big four" validities, whereby the research question is clearly operationalized by the study's methods.

The specification of exactly how the research question will be studied in the experiment design.

Concerns the proper statistical treatment of data and the soundness of the researchers’ statistical conclusions.

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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• Published: 10 July 2023

Misinformation

Linking lab and field research

• Michael Geers   ORCID: orcid.org/0000-0003-0602-5893 1 , 2  

Nature Reviews Psychology volume  2 ,  page 458 ( 2023 ) Cite this article

• Behavioural methods
• Human behaviour

Upon starting my PhD in Psychology, I was convinced I’d be running nothing but experimental studies in the laboratory. This perception changed when I came across a 2019 paper by Guess and colleagues, whose pioneering approach to studying the spread of misinformation online bridged the gap between lab and field research.

Researchers increasingly turn to social media data to study human behaviour within ecologically valid settings, reflecting the prevalence of online interactions in contemporary society. Nevertheless, these data are not tailored for research and present a set of inherent challenges. Usually, identifying the characteristics of individual social media users is difficult, in part because people often don’t disclose this information. This lack of information limits social media researchers to drawing conclusions about accounts or posts rather than the people behind them. For instance, with only the account information, it can be unclear whether misinformation was shared by a human user or a bot.

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Original article

Guess, A., Nagler, J. & Tucker, J. Less than you think: prevalence and predictors of fake news dissemination on Facebook. Sci. Adv. 5 , eaau4586 (2019)

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Mosleh, M., Pennycook, G. & Rand, D. G. Field experiments on social media. Curr. Dir. Psychol. Sci. 31 , 69–75 (2022)

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Parry, D. A. et al. A systematic review and meta-analysis of discrepancies between logged and self-reported digital media use. Nat. Hum. Behav. 5 , 1535–1547 (2021)

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