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.
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.
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.
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.
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.
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.
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