Also, the relationship may be due to a – controlling both of the observed variables.

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Why do control variables matter?
Control variables in experiments
Control variables in non-experimental research
How do you control a variable?
Random assignment
Standardised procedures
Statistical controls
Control variable vs control group
Frequently asked questions about control variables
What is it called when a psychologist simply records the relationship between two variables without manipulating them?
Which type of variable confuses the effect of the independent variable on the dependent variable?
Which of the following would be the most useful for showing a correlation between two variables?
Which research method is most appropriate if you are looking for a causal relationship?

Similar to an independent variable, a covariate is complementary to the dependent, or response, variable. A variable is a covariate if it is related to the dependent variable. According to this definition, any variable that is measurable and considered to have a statistical relationship with the dependent variable would qualify as a potential covariate. A covariate is thus a possible predictive or explanatory variable of the dependent variable. This may be the reason that in regression analyses, independent variables (i.e., the regressors) are sometimes called covariates. Used in this context, covariates are of primary interest. In most other circumstances, however, covariates are of no primary interest compared with the independent variables. They arise because the experimental or observational units are heterogeneous. When this occurs, their ...

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Published on 4 May 2022 by Pritha Bhandari. Revised on 17 October 2022.

A control variable is anything that is held constant or limited in a research study. It’s a variable that is not of interest to the study’s aims but is controlled because it could influence the outcomes.

Variables may be controlled directly by holding them constant throughout a study (e.g., by controlling the room temperature in an experiment), or they may be controlled indirectly through methods like randomisation or statistical control (e.g., to account for participant characteristics like age in statistical tests).

Examples of control variables

Research question	Control variables
Does soil quality affect plant growth?	Temperature Amount of light Amount of water
Does caffeine improve memory recall?	Participant age Noise in the environment Type of memory test
Do people with a fear of spiders perceive spider images faster than other people?	Computer screen brightness Room lighting Visual stimuli sizes

Why do control variables matter?

Control variables enhance the internal validity of a study by limiting the influence of confounding and other extraneous variables. This helps you establish a correlational or causal relationship between your variables of interest.

Aside from the independent and dependent variables, all variables that can impact the results should be controlled. If you don’t control relevant variables, you may not be able to demonstrate that they didn’t influence your results. Uncontrolled variables are alternative explanations for your results.

Control variables in experiments

In an experiment, a researcher is interested in understanding the effect of an independent variable on a dependent variable. Control variables help you ensure that your results are solely caused by your experimental manipulation.

Example: ExperimentYou want to study the effectiveness of vitamin D supplements on improving alertness. You design an experiment with a control group that receives a placebo pill, and an experimental group that receives the supplement.

The independent variable is whether the vitamin D supplement is added to a diet, and the dependent variable is the level of alertness.

To make sure any change in alertness is caused by the vitamin D supplement and not by other factors, you control these variables that might affect alertness:

Diet
Timing of meals
Caffeine intake
Screen time

Control variables in non-experimental research

In an observational study or other types of non-experimental research, a researcher can’t manipulate the independent variable (often due to practical or ethical considerations). Instead, control variables are measured and taken into account to infer relationships between the main variables of interest.

Example: Non-experimental designYou want to investigate whether there’s a relationship between the variables of income and happiness. You hypothesise that income level predicts happiness, but it’s not practically possible to manipulate the variable of income. Instead, you use a survey to collect data about income and happiness.

To account for other factors that are likely to influence the results, you also measure these control variables:

Age
Marital status
Health

How do you control a variable?

There are several ways to control extraneous variables in experimental designs, and some of these can also be used in observational or quasi-experimental designs.

Random assignment

In experimental studies with multiple groups, participants should be randomly assigned to the different conditions. Random assignment helps you balance the characteristics of groups so that there are no systematic differences between them.

This method of assignment controls participant variables that might otherwise differ between groups and skew your results.

Example: Random assignmentIn your experiment, you recruit volunteers through social media ads, word of mouth, and flyers on campus. About 40% of participants sign up through Facebook ads, while more than 50% hear about the study through campus flyers.

It’s possible that the participants who found the study through Facebook have more screen time during the day, and this might influence how alert they are in your study.

To make sure that participant characteristics have no effect on the study, participants are randomly assigned to one of two groups: a control group or an experimental group.

Standardised procedures

It’s important to use the same procedures across all groups in an experiment. The groups should only differ in the independent variable manipulation so that you can isolate its effect on the dependent variable (the results).

To control variables, you can hold them constant at a fixed level using a protocol that you design and use for all participant sessions. For example, the instructions and time spent on an experimental task should be the same for all participants in a laboratory setting.

Example: Standardised proceduresAll participants receive the same information about the study, including instructions for participation and debriefing materials.

To control for diet, fresh and frozen meals are delivered to participants three times a day.
To control meal timings, participants are instructed to eat breakfast at 9:30, lunch at 13:00, and dinner at 18:30.
To control caffeine intake, participants are asked to consume a maximum of one cup of coffee a day.

For the experimental manipulation, the control group is given a placebo, while the experimental group receives a vitamin D supplement. The condition they are in is unknown to participants, and they are all asked to take these pills daily after lunch.

Statistical controls

You can measure and control for extraneous variables statistically to remove their effects on other variables.

“Controlling for a variable” means modelling control variable data along with independent and dependent variable data in regression analyses and ANCOVAs. That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Example: Statistical controlYou collect data on your main variables of interest, income and happiness, and on your control variables of age, marital status, and health.

In a multiple linear regression analysis, you add all control variables along with the independent variable as predictors. The results tell you how much happiness can be predicted by income, while holding age, marital status, and health fixed.

Control variable vs control group

A control variable isn’t the same as a control group. Control variables are held constant or measured throughout a study for both control and experimental groups, while an independent variable varies between control and experimental groups.

A control group doesn’t undergo the experimental treatment of interest, and its outcomes are compared with those of the experimental group. A control group usually has either no treatment, a standard treatment that’s already widely used, or a placebo (a fake treatment).

Aside from the experimental treatment, everything else in an experimental procedure should be the same between an experimental and control group.

Frequently asked questions about control variables

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What is it called when a psychologist simply records the relationship between two variables without manipulating them?

Correlational research involves measuring two variables and assessing the relationship between them, with no manipulation of an independent variable.

Which type of variable confuses the effect of the independent variable on the dependent variable?

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause, while the dependent variable is the supposed effect. A confounding variable is a third variable that influences both the independent and dependent variables.

Which of the following would be the most useful for showing a correlation between two variables?

The most useful graph for displaying the relationship between two quantitative variables is a scatterplot.

Which research method is most appropriate if you are looking for a causal relationship?

Experimental research allows the identification of causal relationships between entities or events. Successful experimental research depends on well-defined research hypotheses that specify the dependent variables to be observed and the independent variables to be controlled.