Control groups in scientific research

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

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.

Control groups in experiments

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).

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.

Example of a control group
You are interested in whether college students perform better in school if they are paid for their performance. To test this, you divide several students into control and treatment groups.

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

By comparing the average change in their grades over the year, you can find out whether monetary incentives improve school performance.

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.

Example of multiple control groups
You have developed a new pill to treat high blood pressure. To test its effectiveness, you run an experiment with a treatment and two control groups.

  • 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.

Control groups in non-experimental research

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.

Example of a matched control group
You are interested in whether smoking electronic cigarettes can cause lung cancer. Here, the “treatment” is whether or not someone has smoked e-cigarettes. You cannot simply compare the cancer rates of those who smoked e-cigarettes against those who have not–the two groups most likely differ in ways that might affect their rates of cancer.

Instead, you can create a control group by matching individuals who do not smoke with those who do (the treatment group) on age, gender, diet, level of exercise, and so on, ensuring that the only difference between the two groups–and thus the only variable that could cause differences in their rates of lung cancer–is their use of e-cigarettes.

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Importance of control 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.

Example of an invalid control group
While analyzing your research on e-cigarettes, you realize that you forgot to control for a family history of smoking, which likely differs between your control and treatment groups, as people whose parents smoke are more likely to take it up themselves.

Since those who come from a family of smokers are more likely to be exposed to secondhand smoke, a known cause of cancer, higher rates may occur among individuals in your treatment group, but you can’t know for sure if this difference is due to the use of e-cigarettes.

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.
  • Randomize 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.

Frequently asked questions about control groups

What is the difference between a control group and an experimental group?

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.

Do experiments always need a control group?

Yes. In an experiment, you need to include a control group that is identical to the treatment group in every way except that it does not receive the experimental treatment.

Without a control group, you can’t know whether it was the treatment or some other variable that caused the outcome of the experiment. By including a control group, you can eliminate the possible impact of all other variables.

What is a confounding variable?

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.

How do I prevent confounding variables from interfering with my research?

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.

What is experimental design?

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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Lauren Thomas

Lauren has a bachelor's degree in Economics and Political Science and is currently finishing up a master's in Economics. She is always on the move, having lived in five cities in both the US and France, and is happy to have a job that will follow her wherever she goes.

1 comment

Lauren Thomas
Lauren Thomas (Scribbr-team)
July 3, 2020 at 3:49 PM

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