Control variables explained

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 randomization 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: Experiment
You 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 non-experimental research, a researcher can’t manipulate the independent variable (often due to ethical or practical considerations). Instead, control variables are measured and taken into account to infer relationships between the main variables of interest.

Example: Non-experimental design
You want to investigate whether there’s a relationship between the variables of income and happiness. You hypothesize 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 assignment
In 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 use 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.

Standardized 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: Standardized procedures
All 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 control
You 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.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

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

What is a control variable?

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Why are control variables important?

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity.

If you don’t control relevant extraneous variables, they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable.

What is internal validity?

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

What does “controlling for a variable” mean?

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model 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.

Is this article helpful?
Pritha Bhandari

Pritha has an academic background in English, psychology and cognitive neuroscience. As an interdisciplinary researcher, she enjoys writing articles explaining tricky research concepts for students and academics.