Controlled Experiments | Methods & Examples of Control

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)

Why does control matter in experiments?

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

Example: Experiment
You’re studying the effects of colours in advertising. You want to test whether using green for advertising fast food chains increases the value of their products.

  • 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.

There are many factors that could influence the value of a meal. A controlled experiment is the strongest way to test whether advertising colour really changes how much customers are willing to pay.

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.

Example: Extraneous variables
In your experiment about advertising colour and meal value, extraneous variables include:

  • 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

If left uncontrolled, any of these variables could affect how much a participant is willing to spend on a meal, making it difficult to determine the true impact of advertising colour on the meal’s value.

Methods of control

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.

Example: Control group
In your experiment on the effects of colours in advertising, all participants are invited to come to a lab individually, where environmental conditions are kept the same throughout the study. To test the effect of colours in advertising, each participant is placed in one of two groups:

  • 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

Only the colour of the advert is different between groups, and all other aspects of the design are the same.

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.

Example: Random assignment
To divide your sample into groups, you assign a unique number to each participant. You use a computer program to randomly place each number into either a control group or an experimental group.Because of random assignment, the two groups have comparable participant characteristics of age, gender, socioeconomic status, etc. That makes it possible to directly compare the results between groups.

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.

Example: Masking (blinding)
To apply double blinding, another researcher holds onto information about condition assignment until data collection is complete.You use an online survey form to present the advertisements to participants, and you leave the room while each participant completes the survey on the computer so that you can’t tell which condition each participant was in.

You also hide the research aim from participants by using filler tasks to prevent them from guessing the purpose of the experiment.

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Problems with controlled experiments

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.

Frequently asked questions about controlled experiments

What is the definition of an experimental design?

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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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.

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