control experiment definition psychology

What Is a Controlled Experiment?

Definition and Example

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• Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
• B.A., Physics and Mathematics, Hastings College

A controlled experiment is one in which everything is held constant except for one variable . Usually, a set of data is taken to be a control group , which is commonly the normal or usual state, and one or more other groups are examined where all conditions are identical to the control group and to each other except for one variable.

Sometimes it's necessary to change more than one variable, but all of the other experimental conditions will be controlled so that only the variables being examined change. And what is measured is the variables' amount or the way in which they change.

Controlled Experiment

• A controlled experiment is simply an experiment in which all factors are held constant except for one: the independent variable.
• A common type of controlled experiment compares a control group against an experimental group. All variables are identical between the two groups except for the factor being tested.
• The advantage of a controlled experiment is that it is easier to eliminate uncertainty about the significance of the results.

Example of a Controlled Experiment

Let's say you want to know if the type of soil affects how long it takes a seed to germinate, and you decide to set up a controlled experiment to answer the question. You might take five identical pots, fill each with a different type of soil, plant identical bean seeds in each pot, place the pots in a sunny window, water them equally, and measure how long it takes for the seeds in each pot to sprout.

This is a controlled experiment because your goal is to keep every variable constant except the type of soil you use. You control these features.

Why Controlled Experiments Are Important

The big advantage of a controlled experiment is that you can eliminate much of the uncertainty about your results. If you couldn't control each variable, you might end up with a confusing outcome.

For example, if you planted different types of seeds in each of the pots, trying to determine if soil type affected germination, you might find some types of seeds germinate faster than others. You wouldn't be able to say, with any degree of certainty, that the rate of germination was due to the type of soil. It might as well have been due to the type of seeds.

Or, if you had placed some pots in a sunny window and some in the shade or watered some pots more than others, you could get mixed results. The value of a controlled experiment is that it yields a high degree of confidence in the outcome. You know which variable caused or did not cause a change.

Are All Experiments Controlled?

No, they are not. It's still possible to obtain useful data from uncontrolled experiments, but it's harder to draw conclusions based on the data.

An example of an area where controlled experiments are difficult is human testing. Say you want to know if a new diet pill helps with weight loss. You can collect a sample of people, give each of them the pill, and measure their weight. You can try to control as many variables as possible, such as how much exercise they get or how many calories they eat.

However, you will have several uncontrolled variables, which may include age, gender, genetic predisposition toward a high or low metabolism, how overweight they were before starting the test, whether they inadvertently eat something that interacts with the drug, etc.

Scientists try to record as much data as possible when conducting uncontrolled experiments, so they can see additional factors that may be affecting their results. Although it is harder to draw conclusions from uncontrolled experiments, new patterns often emerge that would not have been observable in a controlled experiment.

For example, you may notice the diet drug seems to work for female subjects, but not for male subjects, and this may lead to further experimentation and a possible breakthrough. If you had only been able to perform a controlled experiment, perhaps on male clones alone, you would have missed this connection.

• Box, George E. P., et al.  Statistics for Experimenters: Design, Innovation, and Discovery . Wiley-Interscience, a John Wiley & Soncs, Inc., Publication, 2005. 
• Creswell, John W.  Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research . Pearson/Merrill Prentice Hall, 2008.
• Pronzato, L. "Optimal experimental design and some related control problems". Automatica . 2008.
• Robbins, H. "Some Aspects of the Sequential Design of Experiments". Bulletin of the American Mathematical Society . 1952.
• Understanding Simple vs Controlled Experiments
• What Is the Difference Between a Control Variable and Control Group?
• The Role of a Controlled Variable in an Experiment
• Scientific Variable
• DRY MIX Experiment Variables Acronym
• Six Steps of the Scientific Method
• Scientific Method Vocabulary Terms
• What Are the Elements of a Good Hypothesis?
• Scientific Method Flow Chart
• What Are Examples of a Hypothesis?
• What Is an Experimental Constant?
• Scientific Hypothesis Examples
• What Is a Hypothesis? (Science)
• Null Hypothesis Examples
• What Is a Testable Hypothesis?
• Random Error vs. Systematic Error

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6.2 Experimental Design

Learning objectives.

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

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

Between-Subjects Experiments

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

Random Assignment

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

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

One problem with coin flipping and other strict procedures for random assignment is that they are likely to result in unequal sample sizes in the different conditions. Unequal sample sizes are generally not a serious problem, and you should never throw away data you have already collected to achieve equal sample sizes. However, for a fixed number of participants, it is statistically most efficient to divide them into equal-sized groups. It is standard practice, therefore, to use a kind of modified random assignment that keeps the number of participants in each group as similar as possible. One approach is block randomization . In block randomization, all the conditions occur once in the sequence before any of them is repeated. Then they all occur again before any of them is repeated again. Within each of these “blocks,” the conditions occur in a random order. Again, the sequence of conditions is usually generated before any participants are tested, and each new participant is assigned to the next condition in the sequence. Table 6.2 “Block Randomization Sequence for Assigning Nine Participants to Three Conditions” shows such a sequence for assigning nine participants to three conditions. The Research Randomizer website ( http://www.randomizer.org ) will generate block randomization sequences for any number of participants and conditions. Again, when the procedure is computerized, the computer program often handles the block randomization.

Table 6.2 Block Randomization Sequence for Assigning Nine Participants to Three Conditions

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

Treatment and Control Conditions

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

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

Placebo effects are interesting in their own right (see Note 6.28 “The Powerful Placebo” ), but they also pose a serious problem for researchers who want to determine whether a treatment works. Figure 6.2 “Hypothetical Results From a Study Including Treatment, No-Treatment, and Placebo Conditions” shows some hypothetical results in which participants in a treatment condition improved more on average than participants in a no-treatment control condition. If these conditions (the two leftmost bars in Figure 6.2 “Hypothetical Results From a Study Including Treatment, No-Treatment, and Placebo Conditions” ) were the only conditions in this experiment, however, one could not conclude that the treatment worked. It could be instead that participants in the treatment group improved more because they expected to improve, while those in the no-treatment control condition did not.

Figure 6.2 Hypothetical Results From a Study Including Treatment, No-Treatment, and Placebo Conditions

Fortunately, there are several solutions to this problem. One is to include a placebo control condition , in which participants receive a placebo that looks much like the treatment but lacks the active ingredient or element thought to be responsible for the treatment’s effectiveness. When participants in a treatment condition take a pill, for example, then those in a placebo control condition would take an identical-looking pill that lacks the active ingredient in the treatment (a “sugar pill”). In research on psychotherapy effectiveness, the placebo might involve going to a psychotherapist and talking in an unstructured way about one’s problems. The idea is that if participants in both the treatment and the placebo control groups expect to improve, then any improvement in the treatment group over and above that in the placebo control group must have been caused by the treatment and not by participants’ expectations. This is what is shown by a comparison of the two outer bars in Figure 6.2 “Hypothetical Results From a Study Including Treatment, No-Treatment, and Placebo Conditions” .

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

The Powerful Placebo

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

Medical researcher J. Bruce Moseley and his colleagues conducted a study on the effectiveness of two arthroscopic surgery procedures for osteoarthritis of the knee (Moseley et al., 2002). The control participants in this study were prepped for surgery, received a tranquilizer, and even received three small incisions in their knees. But they did not receive the actual arthroscopic surgical procedure. The surprising result was that all participants improved in terms of both knee pain and function, and the sham surgery group improved just as much as the treatment groups. According to the researchers, “This study provides strong evidence that arthroscopic lavage with or without débridement [the surgical procedures used] is not better than and appears to be equivalent to a placebo procedure in improving knee pain and self-reported function” (p. 85).

Research has shown that patients with osteoarthritis of the knee who receive a “sham surgery” experience reductions in pain and improvement in knee function similar to those of patients who receive a real surgery.

Army Medicine – Surgery – CC BY 2.0.

Within-Subjects Experiments

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

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

Carryover Effects and Counterbalancing

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

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

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

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

When 9 Is “Larger” Than 221

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

Simultaneous Within-Subjects Designs

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

Between-Subjects or Within-Subjects?

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

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

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

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

Key Takeaways

• Experiments can be conducted using either between-subjects or within-subjects designs. Deciding which to use in a particular situation requires careful consideration of the pros and cons of each approach.
• Random assignment to conditions in between-subjects experiments or to orders of conditions in within-subjects experiments is a fundamental element of experimental research. Its purpose is to control extraneous variables so that they do not become confounding variables.
• Experimental research on the effectiveness of a treatment requires both a treatment condition and a control condition, which can be a no-treatment control condition, a placebo control condition, or a waitlist control condition. Experimental treatments can also be compared with the best available alternative.

Discussion: For each of the following topics, list the pros and cons of a between-subjects and within-subjects design and decide which would be better.

• You want to test the relative effectiveness of two training programs for running a marathon.
• Using photographs of people as stimuli, you want to see if smiling people are perceived as more intelligent than people who are not smiling.
• In a field experiment, you want to see if the way a panhandler is dressed (neatly vs. sloppily) affects whether or not passersby give him any money.
• You want to see if concrete nouns (e.g., dog ) are recalled better than abstract nouns (e.g., truth ).
• Discussion: Imagine that an experiment shows that participants who receive psychodynamic therapy for a dog phobia improve more than participants in a no-treatment control group. Explain a fundamental problem with this research design and at least two ways that it might be corrected.

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

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

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

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

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.

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• Controlled Experiments | Methods & Examples of Control

Controlled Experiments | Methods & Examples of Control

Published on 19 April 2022 by Pritha Bhandari . Revised on 10 October 2022.

In experiments , researchers manipulate independent variables to test their effects on dependent variables. In a controlled experiment , all variables other than the independent variable are controlled or held constant so they don’t influence the dependent variable.

Controlling variables can involve:

• Holding variables at a constant or restricted level (e.g., keeping room temperature fixed)
• Measuring variables to statistically control for them in your analyses
• Balancing variables across your experiment through randomisation (e.g., using a random order of tasks)

Table of contents

Why does control matter in experiments, methods of control, problems with controlled experiments, frequently asked questions about controlled experiments.

Control in experiments is critical for internal validity , which allows you to establish a cause-and-effect relationship between variables.

• Your independent variable is the colour used in advertising.
• Your dependent variable is the price that participants are willing to pay for a standard fast food meal.

Extraneous variables are factors that you’re not interested in studying, but that can still influence the dependent variable. For strong internal validity, you need to remove their effects from your experiment.

• Design and description of the meal
• Study environment (e.g., temperature or lighting)
• Participant’s frequency of buying fast food
• Participant’s familiarity with the specific fast food brand
• Participant’s socioeconomic status

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You can control some variables by standardising your data collection procedures. All participants should be tested in the same environment with identical materials. Only the independent variable (e.g., advert colour) should be systematically changed between groups.

Other extraneous variables can be controlled through your sampling procedures . Ideally, you’ll select a sample that’s representative of your target population by using relevant inclusion and exclusion criteria (e.g., including participants from a specific income bracket, and not including participants with colour blindness).

By measuring extraneous participant variables (e.g., age or gender) that may affect your experimental results, you can also include them in later analyses.

After gathering your participants, you’ll need to place them into groups to test different independent variable treatments. The types of groups and method of assigning participants to groups will help you implement control in your experiment.

Control groups

Controlled experiments require control groups . Control groups allow you to test a comparable treatment, no treatment, or a fake treatment, and compare the outcome with your experimental treatment.

You can assess whether it’s your treatment specifically that caused the outcomes, or whether time or any other treatment might have resulted in the same effects.

• A control group that’s presented with red advertisements for a fast food meal
• An experimental group that’s presented with green advertisements for the same fast food meal

Random assignment

To avoid systematic differences between the participants in your control and treatment groups, you should use random assignment .

This helps ensure that any extraneous participant variables are evenly distributed, allowing for a valid comparison between groups .

Random assignment is a hallmark of a ‘true experiment’ – it differentiates true experiments from quasi-experiments .

Masking (blinding)

Masking in experiments means hiding condition assignment from participants or researchers – or, in a double-blind study , from both. It’s often used in clinical studies that test new treatments or drugs.

Sometimes, researchers may unintentionally encourage participants to behave in ways that support their hypotheses. In other cases, cues in the study environment may signal the goal of the experiment to participants and influence their responses.

Using masking means that participants don’t know whether they’re in the control group or the experimental group. This helps you control biases from participants or researchers that could influence your study results.

Although controlled experiments are the strongest way to test causal relationships, they also involve some challenges.

Difficult to control all variables

Especially in research with human participants, it’s impossible to hold all extraneous variables constant, because every individual has different experiences that may influence their perception, attitudes, or behaviors.

But measuring or restricting extraneous variables allows you to limit their influence or statistically control for them in your study.

Risk of low external validity

Controlled experiments have disadvantages when it comes to external validity – the extent to which your results can be generalised to broad populations and settings.

The more controlled your experiment is, the less it resembles real world contexts. That makes it harder to apply your findings outside of a controlled setting.

There’s always a tradeoff between internal and external validity . It’s important to consider your research aims when deciding whether to prioritise control or generalisability in your experiment.

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

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• Control Groups and Treatment Groups | Uses & Examples

Control Groups and Treatment Groups | Uses & Examples

Published on July 3, 2020 by Lauren Thomas . Revised on June 22, 2023.

In a scientific study, a control group is used to establish causality by isolating the effect of an independent variable .

Here, researchers change the independent variable in the treatment group and keep it constant in the control group. Then they compare the results of these groups.

Using a control group means that any change in the dependent variable can be attributed to the independent variable. This helps avoid extraneous variables or confounding variables from impacting your work, as well as a few types of research bias , like omitted variable bias .

Table of contents

Control groups in experiments, control groups in non-experimental research, importance of control groups, other interesting articles, frequently asked questions about control groups.

Control groups are essential to experimental design . When researchers are interested in the impact of a new treatment, they randomly divide their study participants into at least two groups:

• The treatment group (also called the experimental group ) receives the treatment whose effect the researcher is interested in.
• The control group receives either no treatment, a standard treatment whose effect is already known, or a placebo (a fake treatment to control for placebo effect ).

The treatment is any independent variable manipulated by the experimenters, and its exact form depends on the type of research being performed. In a medical trial, it might be a new drug or therapy. In public policy studies, it could be a new social policy that some receive and not others.

In a well-designed experiment, all variables apart from the treatment should be kept constant between the two groups. This means researchers can correctly measure the entire effect of the treatment without interference from confounding variables .

• You pay the students in the treatment group for achieving high grades.
• Students in the control group do not receive any money.

Studies can also include more than one treatment or control group. Researchers might want to examine the impact of multiple treatments at once, or compare a new treatment to several alternatives currently available.

• The treatment group gets the new pill.
• Control group 1 gets an identical-looking sugar pill (a placebo)
• Control group 2 gets a pill already approved to treat high blood pressure

Since the only variable that differs between the three groups is the type of pill, any differences in average blood pressure between the three groups can be credited to the type of pill they received.

• The difference between the treatment group and control group 1 demonstrates the effectiveness of the pill as compared to no treatment.
• The difference between the treatment group and control group 2 shows whether the new pill improves on treatments already available on the market.

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Although control groups are more common in experimental research, they can be used in other types of research too. Researchers generally rely on non-experimental control groups in two cases: quasi-experimental or matching design.

Control groups in quasi-experimental design

While true experiments rely on random assignment to the treatment or control groups, quasi-experimental design uses some criterion other than randomization to assign people.

Often, these assignments are not controlled by researchers, but are pre-existing groups that have received different treatments. For example, researchers could study the effects of a new teaching method that was applied in some classes in a school but not others, or study the impact of a new policy that is implemented in one state but not in the neighboring state.

In these cases, the classes that did not use the new teaching method, or the state that did not implement the new policy, is the control group.

Control groups in matching design

In correlational research , matching represents a potential alternate option when you cannot use either true or quasi-experimental designs.

In matching designs, the researcher matches individuals who received the “treatment”, or independent variable under study, to others who did not–the control group.

Each member of the treatment group thus has a counterpart in the control group identical in every way possible outside of the treatment. This ensures that the treatment is the only source of potential differences in outcomes between the two groups.

Control groups help ensure the internal validity of your research. You might see a difference over time in your dependent variable in your treatment group. However, without a control group, it is difficult to know whether the change has arisen from the treatment. It is possible that the change is due to some other variables.

If you use a control group that is identical in every other way to the treatment group, you know that the treatment–the only difference between the two groups–must be what has caused the change.

For example, people often recover from illnesses or injuries over time regardless of whether they’ve received effective treatment or not. Thus, without a control group, it’s difficult to determine whether improvements in medical conditions come from a treatment or just the natural progression of time.

Risks from invalid control groups

If your control group differs from the treatment group in ways that you haven’t accounted for, your results may reflect the interference of confounding variables instead of your independent variable.

Minimizing this risk

A few methods can aid you in minimizing the risk from invalid control groups.

• Ensure that all potential confounding variables are accounted for , preferably through an experimental design if possible, since it is difficult to control for all the possible confounders outside of an experimental environment.
• Use double-blinding . This will prevent the members of each group from modifying their behavior based on whether they were placed in the treatment or control group, which could then lead to biased outcomes.
• Randomly assign your subjects into control and treatment groups. This method will allow you to not only minimize the differences between the two groups on confounding variables that you can directly observe, but also those you cannot.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

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How the Experimental Method Works in Psychology

sturti/Getty Images

The Experimental Process

Types of experiments, potential pitfalls of the experimental method.

The experimental method is a type of research procedure that involves manipulating variables to determine if there is a cause-and-effect relationship. The results obtained through the experimental method are useful but do not prove with 100% certainty that a singular cause always creates a specific effect. Instead, they show the probability that a cause will or will not lead to a particular effect.

At a Glance

While there are many different research techniques available, the experimental method allows researchers to look at cause-and-effect relationships. Using the experimental method, researchers randomly assign participants to a control or experimental group and manipulate levels of an independent variable. If changes in the independent variable lead to changes in the dependent variable, it indicates there is likely a causal relationship between them.

What Is the Experimental Method in Psychology?

The experimental method involves manipulating one variable to determine if this causes changes in another variable. This method relies on controlled research methods and random assignment of study subjects to test a hypothesis.

For example, researchers may want to learn how different visual patterns may impact our perception. Or they might wonder whether certain actions can improve memory . Experiments are conducted on many behavioral topics, including:

The scientific method forms the basis of the experimental method. This is a process used to determine the relationship between two variables—in this case, to explain human behavior .

Positivism is also important in the experimental method. It refers to factual knowledge that is obtained through observation, which is considered to be trustworthy.

When using the experimental method, researchers first identify and define key variables. Then they formulate a hypothesis, manipulate the variables, and collect data on the results. Unrelated or irrelevant variables are carefully controlled to minimize the potential impact on the experiment outcome.

History of the Experimental Method

The idea of using experiments to better understand human psychology began toward the end of the nineteenth century. Wilhelm Wundt established the first formal laboratory in 1879.

Wundt is often called the father of experimental psychology. He believed that experiments could help explain how psychology works, and used this approach to study consciousness .

Wundt coined the term "physiological psychology." This is a hybrid of physiology and psychology, or how the body affects the brain.

Other early contributors to the development and evolution of experimental psychology as we know it today include:

• Gustav Fechner (1801-1887), who helped develop procedures for measuring sensations according to the size of the stimulus
• Hermann von Helmholtz (1821-1894), who analyzed philosophical assumptions through research in an attempt to arrive at scientific conclusions
• Franz Brentano (1838-1917), who called for a combination of first-person and third-person research methods when studying psychology
• Georg Elias Müller (1850-1934), who performed an early experiment on attitude which involved the sensory discrimination of weights and revealed how anticipation can affect this discrimination

Key Terms to Know

To understand how the experimental method works, it is important to know some key terms.

Dependent Variable

The dependent variable is the effect that the experimenter is measuring. If a researcher was investigating how sleep influences test scores, for example, the test scores would be the dependent variable.

Independent Variable

The independent variable is the variable that the experimenter manipulates. In the previous example, the amount of sleep an individual gets would be the independent variable.

A hypothesis is a tentative statement or a guess about the possible relationship between two or more variables. In looking at how sleep influences test scores, the researcher might hypothesize that people who get more sleep will perform better on a math test the following day. The purpose of the experiment, then, is to either support or reject this hypothesis.

Operational definitions are necessary when performing an experiment. When we say that something is an independent or dependent variable, we must have a very clear and specific definition of the meaning and scope of that variable.

Extraneous Variables

Extraneous variables are other variables that may also affect the outcome of an experiment. Types of extraneous variables include participant variables, situational variables, demand characteristics, and experimenter effects. In some cases, researchers can take steps to control for extraneous variables.

Demand Characteristics

Demand characteristics are subtle hints that indicate what an experimenter is hoping to find in a psychology experiment. This can sometimes cause participants to alter their behavior, which can affect the results of the experiment.

Intervening Variables

Intervening variables are factors that can affect the relationship between two other variables. 

Confounding Variables

Confounding variables are variables that can affect the dependent variable, but that experimenters cannot control for. Confounding variables can make it difficult to determine if the effect was due to changes in the independent variable or if the confounding variable may have played a role.

Psychologists, like other scientists, use the scientific method when conducting an experiment. The scientific method is a set of procedures and principles that guide how scientists develop research questions, collect data, and come to conclusions.

The five basic steps of the experimental process are:

• Identifying a problem to study
• Devising the research protocol
• Conducting the experiment
• Analyzing the data collected
• Sharing the findings (usually in writing or via presentation)

Most psychology students are expected to use the experimental method at some point in their academic careers. Learning how to conduct an experiment is important to understanding how psychologists prove and disprove theories in this field.

There are a few different types of experiments that researchers might use when studying psychology. Each has pros and cons depending on the participants being studied, the hypothesis, and the resources available to conduct the research.

Lab Experiments

Lab experiments are common in psychology because they allow experimenters more control over the variables. These experiments can also be easier for other researchers to replicate. The drawback of this research type is that what takes place in a lab is not always what takes place in the real world.

Field Experiments

Sometimes researchers opt to conduct their experiments in the field. For example, a social psychologist interested in researching prosocial behavior might have a person pretend to faint and observe how long it takes onlookers to respond.

This type of experiment can be a great way to see behavioral responses in realistic settings. But it is more difficult for researchers to control the many variables existing in these settings that could potentially influence the experiment's results.

Quasi-Experiments

While lab experiments are known as true experiments, researchers can also utilize a quasi-experiment. Quasi-experiments are often referred to as natural experiments because the researchers do not have true control over the independent variable.

A researcher looking at personality differences and birth order, for example, is not able to manipulate the independent variable in the situation (personality traits). Participants also cannot be randomly assigned because they naturally fall into pre-existing groups based on their birth order.

So why would a researcher use a quasi-experiment? This is a good choice in situations where scientists are interested in studying phenomena in natural, real-world settings. It's also beneficial if there are limits on research funds or time.

Field experiments can be either quasi-experiments or true experiments.

Examples of the Experimental Method in Use

The experimental method can provide insight into human thoughts and behaviors, Researchers use experiments to study many aspects of psychology.

A 2019 study investigated whether splitting attention between electronic devices and classroom lectures had an effect on college students' learning abilities. It found that dividing attention between these two mediums did not affect lecture comprehension. However, it did impact long-term retention of the lecture information, which affected students' exam performance.

An experiment used participants' eye movements and electroencephalogram (EEG) data to better understand cognitive processing differences between experts and novices. It found that experts had higher power in their theta brain waves than novices, suggesting that they also had a higher cognitive load.

A study looked at whether chatting online with a computer via a chatbot changed the positive effects of emotional disclosure often received when talking with an actual human. It found that the effects were the same in both cases.

One experimental study evaluated whether exercise timing impacts information recall. It found that engaging in exercise prior to performing a memory task helped improve participants' short-term memory abilities.

Sometimes researchers use the experimental method to get a bigger-picture view of psychological behaviors and impacts. For example, one 2018 study examined several lab experiments to learn more about the impact of various environmental factors on building occupant perceptions.

A 2020 study set out to determine the role that sensation-seeking plays in political violence. This research found that sensation-seeking individuals have a higher propensity for engaging in political violence. It also found that providing access to a more peaceful, yet still exciting political group helps reduce this effect.

While the experimental method can be a valuable tool for learning more about psychology and its impacts, it also comes with a few pitfalls.

Experiments may produce artificial results, which are difficult to apply to real-world situations. Similarly, researcher bias can impact the data collected. Results may not be able to be reproduced, meaning the results have low reliability .

Since humans are unpredictable and their behavior can be subjective, it can be hard to measure responses in an experiment. In addition, political pressure may alter the results. The subjects may not be a good representation of the population, or groups used may not be comparable.

And finally, since researchers are human too, results may be degraded due to human error.

What This Means For You

Every psychological research method has its pros and cons. The experimental method can help establish cause and effect, and it's also beneficial when research funds are limited or time is of the essence.

At the same time, it's essential to be aware of this method's pitfalls, such as how biases can affect the results or the potential for low reliability. Keeping these in mind can help you review and assess research studies more accurately, giving you a better idea of whether the results can be trusted or have limitations.

Colorado State University. Experimental and quasi-experimental research .

American Psychological Association. Experimental psychology studies human and animals .

Mayrhofer R, Kuhbandner C, Lindner C. The practice of experimental psychology: An inevitably postmodern endeavor . Front Psychol . 2021;11:612805. doi:10.3389/fpsyg.2020.612805

Mandler G. A History of Modern Experimental Psychology .

Stanford University. Wilhelm Maximilian Wundt . Stanford Encyclopedia of Philosophy.

Britannica. Gustav Fechner .

Britannica. Hermann von Helmholtz .

Meyer A, Hackert B, Weger U. Franz Brentano and the beginning of experimental psychology: implications for the study of psychological phenomena today . Psychol Res . 2018;82:245-254. doi:10.1007/s00426-016-0825-7

Britannica. Georg Elias Müller .

McCambridge J, de Bruin M, Witton J.  The effects of demand characteristics on research participant behaviours in non-laboratory settings: A systematic review .  PLoS ONE . 2012;7(6):e39116. doi:10.1371/journal.pone.0039116

Laboratory experiments . In: The Sage Encyclopedia of Communication Research Methods. Allen M, ed. SAGE Publications, Inc. doi:10.4135/9781483381411.n287

Schweizer M, Braun B, Milstone A. Research methods in healthcare epidemiology and antimicrobial stewardship — quasi-experimental designs . Infect Control Hosp Epidemiol . 2016;37(10):1135-1140. doi:10.1017/ice.2016.117

Glass A, Kang M. Dividing attention in the classroom reduces exam performance . Educ Psychol . 2019;39(3):395-408. doi:10.1080/01443410.2018.1489046

Keskin M, Ooms K, Dogru AO, De Maeyer P. Exploring the cognitive load of expert and novice map users using EEG and eye tracking . ISPRS Int J Geo-Inf . 2020;9(7):429. doi:10.3390.ijgi9070429

Ho A, Hancock J, Miner A. Psychological, relational, and emotional effects of self-disclosure after conversations with a chatbot . J Commun . 2018;68(4):712-733. doi:10.1093/joc/jqy026

Haynes IV J, Frith E, Sng E, Loprinzi P. Experimental effects of acute exercise on episodic memory function: Considerations for the timing of exercise . Psychol Rep . 2018;122(5):1744-1754. doi:10.1177/0033294118786688

Torresin S, Pernigotto G, Cappelletti F, Gasparella A. Combined effects of environmental factors on human perception and objective performance: A review of experimental laboratory works . Indoor Air . 2018;28(4):525-538. doi:10.1111/ina.12457

Schumpe BM, Belanger JJ, Moyano M, Nisa CF. The role of sensation seeking in political violence: An extension of the significance quest theory . J Personal Social Psychol . 2020;118(4):743-761. doi:10.1037/pspp0000223

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

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Why control an experiment?

John s torday.

1 Department of Pediatrics, Harbor‐UCLA Medical Center, Torrance, CA, USA

František Baluška

2 IZMB, University of Bonn, Bonn, Germany

Empirical research is based on observation and experimentation. Yet, experimental controls are essential for overcoming our sensory limits and generating reliable, unbiased and objective results.

We made a deliberate decision to become scientists and not philosophers, because science offers the opportunity to test ideas using the scientific method. And once we began our formal training as scientists, the greatest challenge beyond formulating a testable or refutable hypothesis was designing appropriate controls for an experiment. In theory, this seems trivial, but in practice, it is often difficult. But where and when did this concept of controlling an experiment start? It is largely attributed to Roger Bacon, who emphasized the use of artificial experiments to provide additional evidence for observations in his Novum Organum Scientiarum in 1620. Other philosophers took up the concept of empirical research: in 1877, Charles Peirce redefined the scientific method in The Fixation of Belief as the most efficient and reliable way to prove a hypothesis. In the 1930s, Karl Popper emphasized the necessity of refuting hypotheses in The Logic of Scientific Discoveries . While these influential works do not explicitly discuss controls as an integral part of experiments, their importance for generating solid and reliable results is nonetheless implicit.

… once we began our formal training as scientists, the greatest challenge beyond formulating a testable or refutable hypothesis was designing appropriate controls for an experiment.

But the scientific method based on experimentation and observation has come under criticism of late in light of the ever more complex problems faced in physics and biology. Chris Anderson, the editor of Wired Magazine, proposed that we should turn to statistical analysis, machine learning, and pattern recognition instead of creating and testing hypotheses, based on the Informatics credo that if you cannot answer the question, you need more data. However, this attitude subsumes that we already have enough data and that we just cannot make sense of it. This assumption is in direct conflict with David Bohm's thesis that there are two “Orders”, the Explicate and Implicate 1 . The Explicate Order is the way in which our subjective sensory systems perceive the world 2 . In contrast, Bohm's Implicate Order would represent the objective reality beyond our perception. This view—that we have only a subjective understanding of reality—dates back to Galileo Galilei who, in 1623, criticized the Aristotelian concept of absolute and objective qualities of our sensory perceptions 3 and to Plato's cave allegory that reality is only what our senses allow us to see.

The only way for systematically overcoming the limits of our sensory apparatus and to get a glimpse of the Implicate Order is through the scientific method, through hypothesis‐testing, controlled experimentation. Beyond the methodology, controlling an experiment is critically important to ensure that the observed results are not just random events; they help scientists to distinguish between the “signal” and the background “noise” that are inherent in natural and living systems. For example, the detection method for the recent discovery of gravitational waves used four‐dimensional reference points to factor out the background noise of the Cosmos. Controls also help to account for errors and variability in the experimental setup and measuring tools: The negative control of an enzyme assay, for instance, tests for any unrelated background signals from the assay or measurement. In short, controls are essential for the unbiased, objective observation and measurement of the dependent variable in response to the experimental setup.

The only way for systematically overcoming the limits of our sensory apparatus […] is through the Scientific Method, through hypothesis‐testing, controlled experimentation.

Nominally, both positive and negative controls are material and procedural; that is, they control for variability of the experimental materials and the procedure itself. But beyond the practical issues to avoid procedural and material artifacts, there is an underlying philosophical question. The need for experimental controls is a subliminal recognition of the relative and subjective nature of the Explicate Order. It requires controls as “reference points” in order to transcend it, and to approximate the Implicate Order.

This is similar to Peter Rowlands’ 4 dictum that everything in the Universe adds up to zero, the universal attractor in mathematics. Prior to the introduction of zero, mathematics lacked an absolute reference point similar to a negative or positive control in an experiment. The same is true of biology, where the cell is the reference point owing to its negative entropy: It appears as an attractor for the energy of its environment. Hence, there is a need for careful controls in biology: The homeostatic balance that is inherent to life varies during the course of an experiment and therefore must be precisely controlled to distinguish noise from signal and approximate the Implicate Order of life.

P  < 0.05 tacitly acknowledges the explicate order

Another example of the “subjectivity” of our perception is the level of accuracy we accept for differences between groups. For example, when we use statistical methods to determine if an observed difference between control and experimental groups is a random occurrence or a specific effect, we conventionally consider a p value of less than or equal to 5% as statistically significant; that is, there is a less than 0.05 probability that the effect is random. The efficacy of this arbitrary convention has been debated for decades; suffice to say that despite questioning the validity of that convention, a P value of < 0.05 reflects our acceptance of the subjectivity of our perception of reality.

… controls are essential for the unbiased, objective observation and measurement of the dependent variable in response to the experimental setup.

Thus, if we do away with hypothesis‐testing science in favor of informatics based on data and statistics—referring to Anderson's suggestion—it reflects our acceptance of the noise in the system. However, mere data analysis without any underlying hypothesis is tantamount to “garbage in‐garbage out”, in contrast to well‐controlled imaginative experiments to separate the wheat from the chaff. Albert Einstein was quoted as saying that imagination was more important than knowledge.

The ultimate purpose of the scientific method is to understand ourselves and our place in Nature. Conventionally, we subscribe to the Anthropic Principle, that we are “in” this Universe, whereas the Endosymbiosis Theory, advocated by Lynn Margulis, stipulates that we are “of” this Universe as a result of the assimilation of the physical environment. According to this theory, the organism endogenizes external factors to make them physiologically “useful”, such as iron as the core of the hemoglobin molecule, or ancient bacteria as mitochondria.

… there is a fundamental difference between knowing via believing and knowing based on empirical research.

By applying the developmental mechanism of cell–cell communication to phylogeny, we have revealed the interrelationships between cells and explained evolution from its origin as the unicellular state to multicellularity via cell–cell communication. The ultimate outcome of this research is that consciousness is the product of cellular processes and cell–cell communication in order to react to the environment and better anticipate future events 5 , 6 . Consciousness is an essential prerequisite for transcending the Explicate Order toward the Implicate Order via cellular sensory and cognitive systems that feed an ever‐expanding organismal knowledge about both the environment and itself.

It is here where the empirical approach to understanding nature comes in with its emphasis that knowledge comes only from sensual experience rather than innate ideas or traditions. In the context of the cell or higher systems, knowledge about the environment can only be gained by sensing and analyzing the environment. Empiricism is similar to an equation in which the variables and terms form a product, or a chemical reaction, or a biological process where the substrates, aka sensory data, form products, that is, knowledge. However, it requires another step—imagination, according to Albert Einstein—to transcend the Explicate Order in order to gain insight into the Implicate Order. Take for instance, Dmitri Ivanovich Mendeleev's Periodic Table of Elements: his brilliant insight was not just to use Atomic Number to organize it, but also to consider the chemical reactivities of the Elements by sorting them into columns. By introducing chemical reactivity to the Periodic Table, Mendeleev provided something like the “fourth wall” in Drama, which gives the audience an omniscient, god‐like perspective on what is happening on stage.

The capacity to transcend the subjective Explicate Order to approximate the objective Implicate Order is not unlike Eastern philosophies like Buddhism or Taoism, which were practiced long before the scientific method. An Indian philosopher once pointed out that the Hindus have known for 30,000 years that the Earth revolves around the sun, while the Europeans only realized this a few hundred years ago based on the work of Copernicus, Brahe, and Galileo. However, there is a fundamental difference between knowing via believing and knowing based on empirical research. A similar example is Aristotle's refusal to test whether a large stone would fall faster than a small one, as he knew the answer already 7 . Galileo eventually performed the experiment from the Leaning Tower in Pisa to demonstrate that the fall time of two objects is independent of their mass—which disproved Aristotle's theory of gravity that stipulated that objects fall at a speed proportional to their mass. Again, it demonstrates the power of empiricism and experimentation as formulated by Francis Bacon, John Locke, and others, over intuition and rationalizing.

Even if our scientific instruments provide us with objective data, we still need to apply our consciousness to evaluate and interpret such data.

Following the evolution from the unicellular state to multicellular organisms—and reverse‐engineering it to a minimal‐cell state—reveals that biologic diversity is an artifact of the Explicate Order. Indeed, the unicell seems to be the primary level of selection in the Implicate Order, as it remains proximate to the First Principles of Physiology, namely negative entropy (negentropy), chemiosmosis, and homeostasis. The first two principles are necessary for growth and proliferation, whereas the last reflects Newton's Third Law of Motion that every action has an equal and opposite reaction so as to maintain homeostasis.

All organisms interact with their surroundings and assimilate their experience as epigenetic marks. Such marks extend to the DNA of germ cells and thus change the phenotypic expression of the offspring. The offspring, in turn, interacts with the environment in response to such epigenetic modifications, giving rise to the concept of the phenotype as an agent that actively and purposefully interacts with its environment in order to adapt and survive. This concept of phenotype based on agency linked to the Explicate Order fundamentally differs from its conventional description as a mere set of biologic characteristics. Organisms’ capacities to anticipate future stress situations from past memories are obvious in simple animals such as nematodes, as well as in plants and bacteria 8 , suggesting that the subjective Explicate Order controls both organismal behavior and trans‐generational evolution.

That perspective offers insight to the nature of consciousness: not as a “mind” that is separate from a “body”, but as an endogenization of physical matter, which complies with the Laws of Nature. In other words, consciousness is the physiologic manifestation of endogenized physical surroundings, compartmentalized, and made essential for all organisms by forming the basis for their physiology. Endocytosis and endocytic/synaptic vesicles contribute to endogenization of cellular surroundings, allowing eukaryotic organisms to gain knowledge about the environment. This is true not only for neurons in brains, but also for all eukaryotic cells 5 .

Such a view of consciousness offers insight to our awareness of our physical surroundings as the basis for self‐referential self‐organization. But this is predicated on our capacity to “experiment” with our environment. The burgeoning idea that we are entering the Anthropocene, a man‐made world founded on subjective senses instead of Natural Laws, is a dangerous step away from our innate evolutionary arc. Relying on just our senses and emotions, without experimentation and controls to understand the Implicate Order behind reality, is not just an abandonment of the principles of the Enlightenment, but also endangers the planet and its diversity of life.

Further reading

Anderson C (2008) The End of Theory: the data deluge makes the scientific method obsolete. Wired (December 23, 2008)

Bacon F (1620, 2011) Novum Organum Scientiarum. Nabu Press

Baluška F, Gagliano M, Witzany G (2018) Memory and Learning in Plants. Springer Nature

Charlesworth AG, Seroussi U, Claycomb JM (2019) Next‐Gen learning: the C. elegans approach. Cell 177: 1674–1676

Eliezer Y, Deshe N, Hoch L, Iwanir S, Pritz CO, Zaslaver A (2019) A memory circuit for coping with impending adversity. Curr Biol 29: 1573–1583

Gagliano M, Renton M, Depczynski M, Mancuso S (2014) Experience teaches plants to learn faster and forget slower in environments where it matters. Oecologia 175: 63–72

Gagliano M, Vyazovskiy VV, Borbély AA, Grimonprez M, Depczynski M (2016) Learning by association in plants. Sci Rep 6: 38427

Katz M, Shaham S (2019) Learning and memory: mind over matter in C. elegans . Curr Biol 29: R365‐R367

Kováč L (2007) Information and knowledge in biology – time for reappraisal. Plant Signal Behav 2: 65–73

Kováč L (2008) Bioenergetics – a key to brain and mind. Commun Integr Biol 1: 114–122

Koshland DE Jr (1980) Bacterial chemotaxis in relation to neurobiology. Annu Rev Neurosci 3: 43–75

Lyon P (2015) The cognitive cell: bacterial behavior reconsidered. Front Microbiol 6: 264

Margulis L (2001) The conscious cell. Ann NY Acad Sci 929: 55–70

Maximillian N (2018) The Metaphysics of Science and Aim‐Oriented Empiricism. Springer: New York

Mazzocchi F (2015) Could Big Data be the end of theory in science? EMBO Rep 16: 1250–1255

Moore RS, Kaletsky R, Murphy CT (2019) Piwi/PRG‐1 argonaute and TGF‐β mediate transgenerational learned pathogenic avoidance. Cell 177: 1827–1841

Peirce CS (1877) The Fixation of Belief. Popular Science Monthly 12: 1–15

Pigliucci M (2009) The end of theory in science? EMBO Rep 10: 534

Popper K (1959) The Logic of Scientific Discovery. Routledge: London

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Ladislav Kováč discussed the advantages and drawbacks of the inductive method for science and the logic of scientific discoveries 9 . Obviously, technological advances have enabled scientists to expand the borders of knowledge, and informatics allows us to objectively analyze ever larger data‐sets. It was the telescope that enabled Tycho Brahe, Johannes Kepler, and Galileo Galilei to make accurate observations and infer the motion of the planets. The microscope provided Robert Koch and Louis Pasteur insights into the microbial world and determines the nature of infectious diseases. Particle colliders now give us a glimpse into the birth of the Universe, while DNA sequencing and bioinformatics have enormously advanced biology's goal to understand the molecular basis of life.

However, Kováč also reminds us that Bayesian inferences and reasoning have serious drawbacks, as documented in the instructive example of Bertrand Russell's “inductivist turkey”, which collected large amounts of reproducible data each morning about feeding time. Based on these observations, the turkey correctly predicted the feeding time for the next morning—until Christmas Eve when the turkey's throat was cut 9 . In order to avoid the fate of the “inductivist turkey”, mankind should also rely on Popperian deductive science, namely formulating theories, concepts, and hypotheses, which are either confirmed or refuted via stringent experimentation and proper controls. Even if our scientific instruments provide us with objective data, we still need to apply our consciousness to evaluate and interpret such data. Moreover, before we start using our scientific instruments, we need to pose scientific questions. Therefore, as suggested by Albert Szent‐Györgyi, we need both Dionysian and Apollonian types of scientists 10 . Unfortunately, as was the case in Szent‐Györgyi's times, the Dionysians are still struggling to get proper support.

There have been pleas for reconciling philosophy and science, which parted ways owing to the rise of empiricism. This essay recognizes the centrality experiments and their controls for the advancement of scientific thought, and the attendant advance in philosophy needed to cope with many extant and emerging issues in science and society. We need a common “will” to do so. The rationale is provided herein, if only.

Acknowledgements

John Torday has been a recipient of NIH Grant HL055268. František Baluška is thankful to numerous colleagues for very stimulating discussions on topics analyzed in this article.

EMBO Reports (2019) 20 : e49110 [ PMC free article ] [ PubMed ] [ Google Scholar ]

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John S Torday, Email: ude.alcu@yadrotj .

František Baluška, Email: ed.nnob-inu@aksulab .

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What Is A Controlled Experiment? Aren’t All Experiments Controlled?

Why should you experiment, how should you experiment, key parameters of a controlled experiment, is there such a thing as an uncontrolled experiment.

A procedure that helps you understand the influence of various factors that affect a result and the extent of their effect in a controlled environment.

Have you ever done science experiments that have numerous parameters you need to take care of to get an accurate result?

If so, I know exactly how that feels!

Most of the time, you won’t get a perfect value, but rather a value that is nearly correct. It can be so frustrating at times, as you need to take care of the amount of catalyst, the temperature, pressure and a million other things!

I wonder who found out that you need precisely ‘this’ thing in exactly ‘this’ amount to get ‘that’ thing! Well, over time, I’ve realized just how much important these parameters are. These values help us set up a controlled environment where the experiment can occur.

And while many people loathe doing lengthy experiments, scientists have performed these exact same experiments a million times to find the perfect mix of parameters that give a predictable result! Now that’s perseverance!!

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There was a time when scientists speculated about plants being alive in the same way as humans. Jagdish Chandra Bose was the scientist who was able to prove that plants are indeed living things by noting their response to different stimuli. He used an experiment wherein the roots of a plant’s stem were dipped in a solution of Bromine Chloride, a poison . He observed the pulse of the plant as a white spot on the crescograph, a device that could magnify the motion of plant tissues up to 10,000 times.

This experiment may have been groundbreaking at that time, but his result was derived because of the three steps that every scientist follows to arrive at a conclusion.

• Scientists observe a certain phenomenon that interests them or sparks their curiosity.
• They form a hypothesis, i.e., they try to establish a ‘cause-effect’ relationship for the phenomenon. There are multiple hypotheses for a single occurrence that may or may not be correct.

         Example: the atomic model was proposed by many scientists before the most recent Quantum model was accepted. Simply put, a hypothesis is the possible cause of the effect that one wishes to study.

• Now, the hypothesis is often based on mathematical calculations or general observations, but until they are disproved, the theory is not accepted.
• This is where experiments come into the picture. Various experiments are done that can support the hypothesis. If a particular theory is supported by experimental backing, the hypothesis becomes a “scientific theory/discovery”.

Also Read: What Is Endogeneity? What Is An Exogenous Variable?

To reach effective results, you need to test your hypothesis by performing an experiment, but it’s not as if any random experiment can give you results. A controlled experiment allows you to isolate and study the clear result that will eventually allow you to draw conclusions.

A single phenomenon is the result of multiple factors, but how do you know the independent effect of each factor? A controlled experiment basically limits the scope of the result because only one or two factors affecting the result are allowed to vary. All the other factors are kept constant.

Also Read: What Is An Independent Variable?

Now, when you perform an experiment, you’re basically looking for two things

• The factors that affect the final result.
• The extent to which each factor contributes to the result.

We can identify the elements that affect the result by keeping all the other elements constant. These variables/factors that are constant are known as control variables/constant variables .

If we want to test the effect of a certain (factor) fertilizer on plants, we take two plants, both identical in all respects, such that all the other factors affecting its growth remain constant. Now, to one plant we add the fertilizer, and to the other, we add no fertilizer. Thus, after the allotted time period, if the fertilizer was actually useful, you will see that the growth in one plant is greater than the other. Here, the plant that got the fertilizer is the experimental group and the one without the fertilizer is the control group .

If you’re wondering what the use of the control group is, it basically provides you with a minimal value to start with. It allows you to compare the effect of the fertilizer with respect to the normal growth factor and the extent to which the fertilizer enhanced the growth of the plant. A controlled experiment tries to form a link between the cause and the effect. If we are to study the effect of fertilizers on plant growth, the cause will be the ‘fertilizer’ and its effect would be the ‘growth of the plant’. In other words:

• The fertilizer would be the independent variable — a variable that is changed and modified to study its effect.
• The growth of the plant will be the dependent variable— a variable that is being tested and whose value depends on the independent variable.

Well, after reading all of this, it’s pretty obvious that controlled experiments are often set up that way and don’t occur naturally. They also give results that are reliable and spot on!

Clearly, experiments that don’t have any control variables are uncontrolled in every way. In fact, the entire natural phenomenon that gave rise to a scientist’s hypothesis is an uncontrolled experiment. This implies that, without control, you can still get results, but those results are unclear. You can draw conclusions from uncontrolled experiments, but it’s a lot harder to determine the true influence of individual factors when all of them are acting at the same time.

Some experiments, however, are impossible to control! Experiments that require testing on humans are influenced by genetic makeup, metabolism and psychology, among other factors, all of which are beyond human control. Thus, there is often a result that is simply averaged and used because no particular result can reflect the whole effect.

Uncontrolled experiments may not give perfect results, but they often help scientists observe patterns. A task that was performed better by more females than males helps to identify that there is possibly an element of female psychology, a hormone or temperament that influenced the result.

Controlled experimentation is the most widely preferred method used to study and prove a hypothesis. Nature is an intelligent experimenter and designs phenomena that are intricate and detailed, and we humans are still trying to understand those details, so we need to break things into parts before we can understand the whole picture. This is where controlled experimentation helps us. All in all, controlled experimentation aids us in understanding things at a pace we are comfortable with, while giving us time to explore the depths to which we want to study a given occurrence.

• Controlled experiments (article) | Khan Academy. Khan Academy
• How Acharya Jagadish Chandra Bose proved plants have .... India Today
• What are Independent and Dependent Variables?-NCES Kids .... The National Center for Education Statistics

Zankhana has completed her Bachelors in Electronics and Telecommunications Engineering. She is an avid reader of works of mythology and history. She is trained in Hindustani Classical Singing and Kathak. She likes to travel and trusts her artsy heart and scientific mind to take her to places that she has dreamt of.

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What Is a Control Variable? Definition and Examples

A control variable is any factor that is controlled or held constant during an experiment . For this reason, it’s also known as a controlled variable or a constant variable. A single experiment may contain many control variables . Unlike the independent and dependent variables , control variables aren’t a part of the experiment, but they are important because they could affect the outcome. Take a look at the difference between a control variable and control group and see examples of control variables.

Importance of Control Variables

Remember, the independent variable is the one you change, the dependent variable is the one you measure in response to this change, and the control variables are any other factors you control or hold constant so that they can’t influence the experiment. Control variables are important because:

• They make it easier to reproduce the experiment.
• The increase confidence in the outcome of the experiment.

For example, if you conducted an experiment examining the effect of the color of light on plant growth, but you didn’t control temperature, it might affect the outcome. One light source might be hotter than the other, affecting plant growth. This could lead you to incorrectly accept or reject your hypothesis. As another example, say you did control the temperature. If you did not report this temperature in your “methods” section, another researcher might have trouble reproducing your results. What if you conducted your experiment at 15 °C. Would you expect the same results at 5 °C or 35 5 °C? Sometimes the potential effect of a control variable can lead to a new experiment!

Sometimes you think you have controlled everything except the independent variable, but still get strange results. This could be due to what is called a “ confounding variable .” Examples of confounding variables could be humidity, magnetism, and vibration. Sometimes you can identify a confounding variable and turn it into a control variable. Other times, confounding variables cannot be detected or controlled.

Control Variable vs Control Group

A control group is different from a control variable. You expose a control group to all the same conditions as the experimental group, except you change the independent variable in the experimental group. Both the control group and experimental group should have the same control variables.

Control Variable Examples

Anything you can measure or control that is not the independent variable or dependent variable has potential to be a control variable. Examples of common control variables include:

• Duration of the experiment
• Size and composition of containers
• Temperature
• Sample volume
• Experimental technique
• Chemical purity or manufacturer
• Species (in biological experiments)

For example, consider an experiment testing whether a certain supplement affects cattle weight gain. The independent variable is the supplement, while the dependent variable is cattle weight. A typical control group would consist of cattle not given the supplement, while the cattle in the experimental group would receive the supplement. Examples of control variables in this experiment could include the age of the cattle, their breed, whether they are male or female, the amount of supplement, the way the supplement is administered, how often the supplement is administered, the type of feed given to the cattle, the temperature, the water supply, the time of year, and the method used to record weight. There may be other control variables, too. Sometimes you can’t actually control a control variable, but conditions should be the same for both the control and experimental groups. For example, if the cattle are free-range, weather might change from day to day, but both groups have the same experience. When you take data, be sure to record control variables along with the independent and dependent variable.

• Box, George E.P.; Hunter, William G.; Hunter, J. Stuart (1978). Statistics for Experimenters : An Introduction to Design, Data Analysis, and Model Building . New York: Wiley. ISBN 978-0-471-09315-2.
• Giri, Narayan C.; Das, M. N. (1979). Design and Analysis of Experiments . New York, N.Y: Wiley. ISBN 9780852269145.
• Stigler, Stephen M. (November 1992). “A Historical View of Statistical Concepts in Psychology and Educational Research”. American Journal of Education . 101 (1): 60–70. doi: 10.1086/444032

Related Posts

What Do Psychologists Mean When They Say "Experiment"?

Control groups and control conditions allow for vital comparisons..

Posted August 29, 2021 | Reviewed by Devon Frye

• Control is one of the key features of an experiment.
• This means using control groups or control conditions for comparison.
• The quality of comparison matters—we can't just compare doing something with doing nothing.

What makes an experiment, an experiment?

In the last, first post of this blog, I mentioned that much of research methods is trying to make sure we draw the right conclusions, while also trying very hard not to draw the wrong conclusions. The type of study particularly suited for this is the experiment . Contrary to popular belief, not every study is an experiment—in fact, in psychological research, the term "experiment" is narrowly defined as a study involving both randomisation and control. In this blog post, I want to explain what we mean by control and why it is such an important part of research.

Let’s assume a group of researchers wants to find out how to improve childrens’ working memory in the long term. In fact, we don't need to assume because that's precisely what researchers Henry, Messer, and Nash wanted to find out in their 2013 study . In particular, they want to test whether adaptive "executive-loaded exercises" are effective in training children working memory. "Executive-loaded" means these are exercises that put a cognitive load on the executive function , i.e. the part of the brain that allocates cognitive resources and attention ; adaptive means they adjust to the children's skill and get easier or more difficult as the children progress.

Surely the easiest way for Henry et al. would have been to test the childrens’ working memory to establish a baseline for comparison, then train them with this set of exercises, then test their working memory again, right?

Simple Comparisons Don't Work

Let's assume for a moment that's what they did. In principle, there are three(ish) possible outcomes in such a situation: the second set of tests show that children perform better in the same working memory tests; they perform equally well; or they perform worse. Luckily for the researchers, the tests show an improvement. Would that allow Henry and colleagues to conclude that these exercises helped children to improve their working memory?

Sadly no. As is the case with most of us,1 we continue to learn and improve our skills —and of course, this is particularly true for children. In other words, there is a distinct chance that the children in the study would have improved over time anyway, and the researchers would not be able to say with confidence that any improvement they might have seen is due to their training method.

What the researchers need, therefore, is a way of finding out whether the improvement is (only) due to the passage of time or, at least partially, to them training the children in this method. They need another group of children who don’t get trained in executive-loaded exercises. This helps establish whether any improvement they observe would have happened anyway, or whether it’s a consequence of the intervention (i.e., the training method). If both groups show roughly equivalent 1 improvement, it’s unlikely 2 that our method made any difference; however, if the comparison group does not improve, and ours does, we are slightly more justified in concluding that method X might have some merit.

We Need Control

In psychological research, such a comparison group is also called a control group as it’s essentially controlling for the passage of time and the change of skills, abilities, opinions, and experiences that go with it. We also refer to the two groups as conditions , as in “Group 1 experiences condition X, group 2 experiences condition Y.”

But simply having a control group isn’t enough. It’s also important how that control group is selected, and what the control group experiences. In the study by Henry et al., the intervention consisted of repeated in-person meetings with an experimenter. But it could also have consisted of children coming into the psychology department with their parents and spending some time with the researcher during training. Or perhaps the researcher(s) paid house visits to the children and their families.

In any of these cases, the children in our intervention group did receive some more attention and interaction from their parents and/or the researcher(s) than they usually would have—and more than the control group, if control just means doing nothing! You may have heard this referred to as the Hawthorne effect , after research at the eponymous production plant which found that workers’ productivity in a factory improved regardless of the actual intervention (e.g., more light, less light) and eventually concluded that it was the existence of the intervention and the resulting increase in attention that improved workers’ productivity.

… But Not Just Any Kind of Control

Whether the original study really showed such an effect is fiercely debated in today’s literature, but that the presence of an intervention alone can have an effect is fairly well established. That’s the reason why we tend to use what’s called “active controls,” that is, control groups or conditions that also get a comparable intervention or experience. And that's exactly what Henry, Messer, and Nash did: In their study, participants were allocated to either the intervention or an “active control”—a different memory training that was similar in time-commitment and involvement by the children.

Still, in some contexts, the Hawthorne effect may be very difficult to mitigate. In their article in the British Medical Journal , Sedgwick and Greenwood (2015) describe a study testing comparing patient-controlled vs. nurse-controlled administration of pain medication to patients with pain from traumatic injuries.

Which patients fare better: Those that are allowed to control dosage and administration of their pain medication, or those that have medication dosages set and administered by nurses? The answer may surprise you! … Or it probably won’t. Patients who have control over their own medication report better pain management and satisfaction.

But is this because pain management was objectively better and more effective, or because participants had a higher degree of control (and autonomy) over their treatment? They conclude that it’s likely both patients and nurses involved in the study may have been affected by the Hawthorne effect, and that even the “gold standard” of empirical research (double-blinding—more on that in an upcoming post) would likely not have made much of a difference.

Control Alone Isn't Enough

Even active control, however, is not enough for a study to be called an experiment. While control groups or control conditions allow us to exclude some potential influences and reasons for our observations, there are still too many potential distractions and disturbances that we need to account for. And, counterintuitively, one of those requirements relies on randomness. But that's a topic for the next post.

1 The question of what constitutes „roughly equivalent” is in itself a complex question and is linked to concepts such as statistical significance – yet another topic for another post.

2 Note that while unlikely, it‘s not impossible, and is also related to concepts such as significance.

Henry, L., Messer, D. J. and Nash, G. (2013). Testing for Near and Far Transfer Effects with a Short, Face-to-Face Adaptive Working Memory Training Intervention in Typical Children. Infant and Child Development, 23(1), pp. 84-103. doi: 10.1002/icd.1816

Sedgwick, P., & Greenwood, N. (2015). Understanding the Hawthorne effect. British Medical Journal, 351.

Daniela Aidley, Ph.D., is a professor in business psychology at the West Coast Applied University, Heide, Germany, where she's teaching psychology, diversity management, and research methods.

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IResearchNet

Control Condition

Control condition definition.

Control Condition Evidence and Implications

The control condition is designed to be equivalent to the experimental condition except for the independent variable, which is absent or held constant under its normal circumstances. Thus, the control condition provides a basis for comparison. The researcher assesses the influence of the independent variable by comparing the outcomes under the experimental and control conditions. For example, if researchers were to design an experimental study to test the effect of loud music on test performance, students who did not listen to loud music would be in the control group. The researchers could compare the test score of the students who did listen to loud music with the students in the control group to determine whether loud music had an impact on test scores.

Not all experimental designs have a control condition. However, it is useful to include a control condition to determine the effect of the procedure outside the effect of the independent variable. Consider the design of an experiment in which researchers are testing the effectiveness of two different types of medicine on headache relief. Participants with headaches would be divided into two groups, with each group getting one type of medicine. After an hour, researcher would ask participants to rate the effectiveness of the headache medicine. From this design, researchers could determine if one of the medicines was more effective that the other. They could not determine, however, if either of these medicines was more effective than no medicine at all. It is possible that simply believing you are taking headache medicine can lessen the pain. If the researchers included a control condition in this experimental design, they could make this comparison. Participants could be divided into three groups, with two groups receiving different headache medicines and one group receiving a placebo.

Then, researchers could compare the effectiveness ratings of the two real headache medicines with the ratings from the control group. If the effectiveness ratings provided by participants receiving actual medicine were greater than those provided by participants in the control group, researchers could conclude that taking a headache medicine was more effective than taking no medicine. Thus, including a control condition allows researchers to compare the way things are in the presence of an independent variable with the way things would have been in the absence of an independent variable.

• Pelham, B. W., & Blanton, H. (2002). Conducting experiments in psychology: Measuring the weight of smoke (2nd ed.). Belmont, CA: Wadsworth.
• Social Psychology Research Methods

Locus of Control Theory In Psychology: Definition & Examples

Gabriel Lopez-Garrido

Undergraduate at Harvard University

Political Science and Psychology

Gabriel Lopez-Garrido is currently in his final year at Harvard University. He is pursuing a Bachelor's degree with a primary focus on Political Science (Government) and a minor in Psychology.

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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.

On This Page:

Take-home Messages

• The term ‘Locus of control’ refers to how much control a person feels they have in their own behavior. A person can either have an internal or external locus of control (Rotter, 1954).
• People with a high internal locus of control perceive themselves as having much personal control over their behavior and are, therefore, more likely to take responsibility for their behavior. For example, I did well on the exams because I revised extremely hard.
• In contrast, a person with a high external locus of control perceives their behaviors as a result of external influences or luck – e.g., I did well on the test because it was easy.
• Research has shown that people with an internal locus of control tend to be less conforming and obedient (i.e., more independent). Rotter proposes that people with an internal locus of control are better at resisting social pressure to conform or obey, perhaps because they feel responsible for their actions.
• Locus of control is an important term to know in almost every branch of the psychology community. This is mainly because it can be applied in many aspects of daily life; whether the locus is external or internal, it will – by definition – affect your mind, body, and even actions.
• Fields like educational psychology, clinical psychology, and even health psychology have all made strides in researching the phenomenon to understand more about how one can control or improve one’s locus of control.
• Experts in the field of psychology often disagree with each other regarding whether differences should be attributed based on cultural differences or whether a more worldwide measure of locus of control will be more helpful when it comes to practical application.

Internal vs. External Locus of Control

Locus of control is how much individuals perceive that they themselves have control over their own actions as opposed to events in life occurring instead because of external forces. It is measured along a dimension of “high internal” to “high external”.

The concept was created by Julian B. Rotter in 1954, and it quickly became a central concept in the field of personality psychology.

An individual’s “locus” (plural “loci”) is conceptualized as internal (a conviction that one can handle one’s own life) or external (a conviction that life is constrained by outside factors which the individual can’t impact or that possibility or destiny controls their lives). There is a continuum, with most people lying in between.

A high internal perceive themselves as having a great deal of personal control and therefore are more inclined to take personal responsibility for their behavior, which they see are being a product of their own effect. High external perceive their behavior as being caused more by external forces or luck.

It is also worth mentioning that the term locus of control is not to be confused with attributional style . Locus of control refers to an idea connected with anticipations about the future, while attributional style is a concept that is instead concerned with finding explanations for past outcomes.

People with an internal locus of control accept occasions in their day-to-day existence as controllable. To be more specific, this means that they can recognize instances where destiny is controllable: for instance, an individual is taking a test for a driver’s license.

A person with an internal locus of control will attribute whether they pass or fail the exam due to their own capabilities. This individual would praise their own abilities if they passed the test and would also recognize the need to improve their own driving if they had instead failed the exam.

An individual with an external locus of control would perceive the same event differently. This individual would be more likely to blame other factors such as the weather, their current condition, or even the exam itself as an excuse rather than accept that the exam went the way it did because of personal decisions.

Rather than accept that part of the blame rests on them, the event is instead attributed to occur because of uncontrollable forces (destiny/fate/etc.).

Locus of control is one of the four elements of center self-assessments – one’s principal examination of oneself – alongside neuroticism , self-viability, and self-esteem.

The idea of center self-assessments was first inspected by Judge, Locke, and Durham (1997), and since has demonstrated to foresee a few work results, explicitly, work fulfillment and occupation performance.

In a subsequent report, Judge et al. (2002) contended that locus of control, neuroticism, self-viability, and confidence elements might all influence each other.

How it Works

The first recorded trace of the term Locus of Control comes from Julian B. Rotter’s work (1954) based on the social learning theory of personality. It is a great example of a generalized expectancy related to problem-solving, a strategy that applies to a wide variety of situations.

In 1966 Rotter distributed an article in Psychological Monographs that summed up around a decade of extensive research (by Rotter and his understudies), with most of this work actually never being published beforehand.

It is speculated that Locus of Control may have come beforehand as a term coined by a psychologist by the name of Alfred Adler . The evidence for this is lacking, however, so the main bulk of the credit for the concept lies in Rotter and his understudies” early works.

One of these understudies was William H. James. This psychologist would later go on to produce his own work in the field, but while he was under the tutelage of Rotter, he wanted to study what he denoted as “expectancy shifts.”

These “expectancy shifts” can be classified as follows:

Typical Expectancy Shifts

Typical expectancy shifts derive from the belief that success (or failure) will be the determining notion for whatever activity/action is preceded next (that is to say, if one succeeds at something, then the expectancy is that they will succeed again).

Let’s say – for example – that during a basketball game, a player shoots a basketball and scores a point. After attempting this three times and scoring all three, the player might come to believe that (due to the fact the player has been continuously successful) if they continue to shoot, they will continue to score.

Atypical Expectancy Shifts

Atypical expectancy shifts, which derive from the belief that success (or a failure) will not have any determining notion for whichever activity/action that follows it (that is to say, if one succeeds at an activity, then the expectancy for the subsequent one is independent of this result; one could fail or succeed).

To give an example of this, picture someone who is at a casino. This individual places a bet on the ball, landing on a red number in the roulette wheel.

After three spins of the wheel, the ball has landed once in a red number, once in a black number, and finally once in a green number. The individual will (hopefully) most likely come to the conclusion that the result of the spin is independent of the last result, with each individual spin being a stand-alone event.

Additional research supported the hypothesis that typical expectancy shifts were much more common amongst individuals who had confidence in their own abilities, whilst those who didn’t really believe in their capabilities tended to attribute their expectancies toward fate rather than skill.

In other words, the distinction lies in whether the cause is internal or external; those who have faith in their own abilities will look towards an internal cause and adapt a typical expectancy shift, while those who attribute their results to external causes will most likely exhibit an atypical expectancy shift.

Rotter has made strides in this area of his research, covering this phenomenon in multiple works (1975). He has talked about issues and confusion in others’ utilization of the interior versus outer build, explaining how misconceptions and miscommunication have led people to mistake Locus of Control for other psychological terms.

Measurement

There are multiple ways to measure locus of control, but by far, the most widely used questionnaire is the 13-item (plus six filler items) forced-choice scale of Rotter (1966). This questionnaire first came into the scene in 1966 and is arguably still the best way to determine the locus of control in the present day. This does not mean that this is the only popular questionnaire.

Another example is Bialer’s (1961) 23-item scale for children, which actually even predates Rotter’s work. Other examples would be the Crandall Intellectual Ascription of Responsibility Scale (Crandall, 1965) and the Nowicki-Strickland Scale (Nowicki & Strickland, 1973), though again, most of these are not used in favor of Rotter’s 1966 questionnaire.

One of his understudies (again, William H. James) was actually responsible for developing one of the earliest psychometric scales to assess locus of control for his unpublished doctoral dissertation, supervised by Rotter at Ohio State University. As just mentioned, however, the work remains unpublished yet it is an example of just how much influence Rotter and his students have over the origins of the term.

Many measures of locus of control have appeared since Rotter’s scale. These vary from the original that predate Rotter’s own original designs to the locus of controls designed specifically for groups – like children (such as the Stanford Preschool Internal-External Scale for three- to six-year-olds).

According to the data analyzed by Furnham and Steele (1993), they suggest that the most reliable, valid questionnaire for adults is The Duttweiler (1984) Internal Control Index (ICI), which might be the better scale. Right off the bat, an advantage these scales have is that they address perceived problems with the Rotter scales.

These issues include adjusting the forced-choice format, removing the susceptibility to social desirability and heterogeneity (as indicated by factor analysis), and the natural improvements that come from developing something almost 30 years after the Rotter scales.

One important thing to note is that while other scales existed in 1984 besides the Duttweiler scales to measure locus of control, they all appear to fall victim to the same problems that the Rotter scales never originally addressed.

The primary difference lies in the removal of the forced-choice format used in Rotter’s scale. Previously, individuals had to affirm whether the assertion presented by the scale was true or false.

However, with Duttweiler’s 28-item ICI, which utilizes a Likert-type scale, individuals must specify whether they would behave as described in each of the 28 statements rarely, occasionally, sometimes, frequently, or usually.

This approach makes the scale much more adaptable to human nature’s nuances than the original Rotter scales.

The ICI gives individuals much more choice by assessing variables pertinent to internal locus. These include but are not limited to cognitive processing, resistance to social influence, self-reliance, autonomy, and delay of gratification. Small validation studies have indicated that the scale had good internal consistency reliability (a Cronbach’s alpha of 0.85)

Applications

The field most associated with locus of control is health psychology, mainly because the original scales to measure locus of control originated in the health domain of psychology.

These first scales were originally reviewed and approved by Furnham and Steele in 1993; they have since remained an essential part of health and other branches of psychology.

Out of the reviewed scales, The best-known in the field of health psychology are the Health Locus of Control Scale and the Multidimensional Health Locus of Control Scale, or MHLC (Wallston & Wallston, 2004).

The idea that health psychology and Locus of control go together is based on the concept that health may be attributed to three sources: internal factors (such as self-determination of a healthy lifestyle), powerful external factors (the words of a doctor or a loved one) or luck/destiny/coincidence.

Those that belong to the last group are almost impossible to deal with, given that they have a firm belief that nothing they will do can either change or avert what is going to happen either way.

The scales reviewed by Furnham and Steele (1993) have directly contributed to multiple areas of health psychology. Take, for example, Saltzer’s (1982) Weight Locus of Control Scale or Stotland and Zuroff’s (1990) Dieting Beliefs Scale.

Both these scales tackled the issue of obesity and shed light on how it affects different types of individuals. These scales do not limit themselves only to the physical aspects of individuals; take, for example, Wood and Letak’s (1982) Mental health locus of control scale.

These scales try to measure the stages of health and depression that an individual is currently in; there’s even a scale meant for measuring cancer and cancer-like symptoms (the Cancer Locus of Control Scale of Pruyn et al., 1990).

Perhaps the most important link that locus of control has to health psychology is Claire Bradley’s work, which links locus of control to the management of diabetes mellitus. This empirical data was reviewed by Norman and Bennet (1997) and they note that the data collected on whether certain health-related behaviors are related to internal health locus of control is, at best ambiguous.

For example, they point out that according to certain studies, locus of control was found to be linked with increased exercise, but also note how other studies have mentioned that the impact that exercise has on locus of control is either minimal or non-existent.

Activities such as jogging or running have long since been dismissed as lone factors for influencing any sort of command in one’s locus of control.

This ambiguity goes on in the study, with data on the relationship between internal health locus of control and other health-related behaviors also being suspicious.

These health-related behaviors include breast self-examination, weight control, and preventive-health behavior and in the study, it is said that alcohol consumption has a direct relationship with one’s internal locus of control.

Again with alcoholism as a factor, the same problems occur; the facts from the study begin to contradict themselves. During their analysis of the validity of the study, Norman and Bennett (1998) realized that some of the studies concluded by suggesting that a link existed between alcoholism and having an increased externality for health locus of control.

This goes against what is known right now, which is that – according to multiple other studies – alcoholism is related instead to increased internality in regards to an individual’s locus of control. The perceived notion is that alcoholism is directly related to the strength of the locus, not to what type of locus exists.

That is to say, it does not matter whether an individual has an internal or external locus of control; alcohol consumption is only related to the actual strength of that respective locus of control.

What is internal locus of control?

An internal locus of control refers to the belief that one can control their own life and the outcomes of events. Individuals with a high internal locus of control perceive their actions as directly influencing the results they experience.

What is external locus of control?

An external locus of control refers to the belief that external factors, such as fate, luck, or other people, are responsible for the outcomes of events in one’s life rather than one’s own actions.

Who proposed the locus of control concept?

The concept of locus of control was proposed by psychologist Julian B. Rotter in 1954.

Bennett, P., Norman, P., Murphy, S., Moore, L., & Tudor-Smith, C. (1998). Beliefs about alcohol, health locus of control, value for health and reported consumption in a representative population sample. Health Education Research, 13 (1), 25-32.

Bialer, I. (1961). Conceptualization of success and failure in mentally retarded and normal children. Journal of personality .

CRANDALL, V. C., KATKOVSKY, W., & CRANDALL, V. J. (1965) Children’s beliefs in their own control of reinforcements in intellectual-academic achievement situations. Child Development , 36, 91-109.

Duttweiler, P. C. (1984). The internal control index: A newly developed measure of locus of control. Educational and Psychological Measurement, 44 (2), 209-221.

Furnham, A., & Steele, H. (1993). Measuring locus of control: A critique of general, children’s, health‐and work‐related locus of control questionnaires. British Journal of Psychology, 84 (4), 443-479.

Nowicki, S., & Strickland, B. R. (1973). A locus of control scale for children. Journal of Consulting and Clinical Psychology, 40 (1), 148.

Norman, P., Bennett, P., Smith, C., & Murphy, S. (1997). Health locus of control and leisure-time exercise. Personality and Individual Differences, 23 (5), 769-774.

Norman, P., Bennett, P., Smith, C., & Murphy, S. (1998). Health locus of control and health behavior. Journal of Health Psychology, 3 (2), 171-180.

Rotter, J. B. (1954). Social learning and clinical psychology .

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Rotter, J. B. (1975). Some problems and misconceptions related to the construct of internal versus external control of reinforcement. Journal of Consulting and Clinical Psychology, 43 (1), 56.

Saltzer, E. B. (1982). The weight locus of control (WLOC) scale: a specific measure for obesity research. Journal of Personality Assessment, 46 (6), 620-628.

Stotland, S., & Zuroff, D. C. (1990). A new measure of weight locus of control: The Dieting Beliefs Scale. Journal of personality assessment, 54 (1-2), 191-203.

Wallston, K. A., Strudler Wallston, B., & DeVellis, R. (1978). Development of the multidimensional health locus of control (MHLC) scales. Health education monographs, 6 (1), 160-170.

Wallston, K. A., & Wallston, B. S. (2004). Multidimensional health locus of control scale. Encyclopedia of health psychology , 171, 172.

Watson, M., Greer, S., Pruyn, J., & Van Den Borne, B. (1990). Locus of control and adjustment to cancer. Psychological Reports, 66 (1), 39-48.

Wood, W. D., & Letak, J. K. (1982). A mental-health locus of control scale. Personality and Individual Differences, 3 (1), 84-87.

Keep Learning

• Rotter, J. B. (1966). Generalized expectancies for internal versus external control of reinforcement. Psychological monographs: General and applied, 80(1), 1.
• Rotter, J. B. (1990). Internal versus external control of reinforcement: A case history of a variable. American psychologist, 45(4), 489.
• Stotland, S., & Zuroff, D. C. (1990). A new measure of weight locus of control: The Dieting Beliefs Scale. Journal of personality assessment, 54(1-2), 191-203.
• Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior