Researchers often manipulate independent variables to understand the effect on the dependent variables when conducting research. In the case of a controlled experiment, all other variables except the independent variable are controlled or held constant to find the exact correlation between the dependent and independent variables.

The methods of controlling variables can include the following,

• Restricting effects of the variables or holding them constant like the ambient temperature.
• Controlling the variables statistically for your analysis.
• Using randomization to balance the variables, like using a random order of tasks.

What is the importance of controlled experiments?

Controlling variables in research is important as it is a major factor for internal validity, which helps establish a cause and effect relationship between the variables.

Example
You are analyzing the impact of colours on marketing strategies. You want to test if using the colour green will help to increase the value of the products. In this case, the independent and dependent variables are as follows,

• Independent variable: The colour used for advertising and marketing purposes.
• Dependent variable: The price that the customer is willing to pay for the products.

Several factors could influence the value of the product. Using a controlled experiment would be sure to determine if the colour impacts advertising and makes customers pay more for the product.

Several unrelated factors might influence the validity of your study if they are not accounted for. These are known as extraneous variables.

Extraneous variable example
In the above example, the extraneous variables can include

• The product description and the design.
• The ambient environment of the study, like lighting and temperature.
• The customer’s buying habits.
• The customer’s familiarity with the product.
• The socioeconomic background of the customer.

When such variables are left uncontrolled, these will severely influence the customers’ price for your product. Furthermore, it makes determining the impact of colours on advertising very difficult.

How to control variables?

When conducting a controlled experiment, one of the most common control methods is the standardization of data collection. It means that all the study participants would be tested in the same environment using the same resources or materials. In addition, only the independent variable should be manipulated between the study groups.

Tweaking your sampling procedures will help you to control extraneous variables when conducting controlled experiments. For best results, the sample should be chosen from the target population using specific criteria for inclusion and exclusion. For example, choosing a specific income group to include participants from, excluding participants that suffer from colour blindness.

You can also include the effects of the extraneous variables in later studies that may affect the study results.

Once the participants are selected for the study, they must be sorted into groups to test the effects of the independent variable manipulations. Assigning participants to groups will help in implementing control while conducting the research.

Control groups: By creating control groups, researchers can test the comparable effects of manipulating the independent variable by subjecting the groups to treatment, no treatment or fake treatment.

It allows assessing whether the outcomes result from treatment or some other factors that might have influenced the outcome.

Control group example
To conduct your controlled experiment, you invite the participants to the study environment, which is kept identical for each participant.

To test the impact of colours, the participants are divided into two groups. These include,

• Treatment group: The group is exposed to red coloured advertising for the product.
• Control group: The group is presented with green advertising items for the same item.

Ensure that only the colour should be different while every other aspect should remain identical.

Random assignment: Random assignment is a great way to eliminate systematic differences between the participant of the control and treatment groups.

It also ensures that most of the extraneous participant variables are distributed evenly among the experiment groups, allowing for a valid comparison.

A true experiment always employs random assignment, and this differentiates it from a quasi-experiment.

Random assignment example
Every participant chosen for the study should be given a number, and a randomizer algorithm should be used to sort participants randomly into the groups.

It will evenly distribute the extraneous variables like gender, age, etc., between the control and treatment groups, thus eliminating their influence on the outcome.

Blinding/Masking: This type of control in controlled experiments hides the assignment from the participants and the researcher. This type of control is very common in clinical studies to test out drugs or new treatments.

In some cases, the researcher might unintentionally influence the behaviour of the participants in a certain way which can diminish the validity of the study. Also, the environment might influence the participants to behave in a certain way or influence their responses.

By masking, both the researcher and the participants are not aware of the experimental group. It helps to eliminate biases from the study that could otherwise have influenced the outcome of the study.

Masking example
In the case of double-blinding, the researcher is not aware of the assignment conditions of the participants until the collection of data study is complete.

In the above scenario, researchers can provide the participants with survey forms to record their responses. They are gathered in a room and given the form to record their responses. The data is digitized on a computer so that the conditions of each of the participants are hidden.

The research aim is also hidden from the participants by introducing unrelated filler tasks so that participants cannot guess the experiment’s aim.

The limitations of controlled experiments

Though a controlled experiment is a standard method of testing causal relations, some limitations need to be accounted for.

Not all variables can be controlled: It is true, especially when human participants are involved in the study. Moreover, it can be very difficult, almost impossible, to account for all the extraneous variables like perception, attitude, etc., that might influence the study’s outcome.

However, it is essential to control these variables to limit their effect on the study’s outcome.

Risk of low external validation: Controlled experiments have a significant disadvantage with external validity. It means the outcome of the study cannot be broadly implemented to a generalized population and environment.

The more the variables are controlled, the less it reflects the real-world scenario making it harder to apply the results outside a controlled setting.

One must always balance the outcome with both external and internal validities; therefore, it is essential to understand the aim of the research to decide which side needs more attention.

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