What is the fundamental difference between an Independent Variable (IV) and a Dependent Variable (DV)?

The IV is the factor that the researcher actively manipulates to create different experimental conditions, whereas the DV is the outcome factor that is measured to see if the manipulation had an effect.

How does an extraneous variable differ from a confounding variable?

An extraneous variable is any 'extra' factor that could potentially affect the results, while a confounding variable is an extraneous variable that was not controlled and actually varied along with the IV, providing a false explanation for the results.

What is the difference between a 'control group' and 'controlled variables'?

A control group is a set of participants who do not receive the experimental treatment (used for comparison), while controlled variables are specific environmental or participant factors kept constant across all groups to ensure fairness.

What is the 'direction of causality' error in variable manipulation?

This error occurs when a researcher assumes the IV caused a change in the DV, but in reality, the DV might have caused the change in the IV, or a third uncontrolled variable influenced both.

Why is it a mistake to use a qualitative measure for a Dependent Variable in a formal experiment?

Experiments aim for objective, statistical analysis to determine if a change is significant. Qualitative data (words/descriptions) is difficult to compare mathematically across conditions compared to quantitative data (numbers).

What happens to the internal validity of a study if a researcher fails to control for situational variables?

Internal validity decreases because the researcher cannot be certain if the change in the DV was caused by the IV or by the inconsistent environmental factors (like noise or lighting).

Define 'Operationalization' in the context of experimental variables.

Operationalization is the process of defining a variable in terms of the specific procedures or measurements used to determine its presence or quantity in an experiment.

What are 'Participant Variables'?

These are individual differences between participants, such as age, intelligence, or prior experience, that could act as extraneous variables if not controlled through random allocation.

What is a 'Standardized Procedure'?

A set of identical instructions and environmental conditions provided to every participant to ensure that situational variables do not interfere with the experimental results.

Why is manipulation of the IV necessary to establish a cause-and-effect relationship?

By actively changing the IV and observing the DV while holding everything else constant, the researcher demonstrates that the IV is the specific trigger for the observed change.

Manipulation and Control of Variables | AQA A-Level Psychology

Manipulation and Control of Variables

Summary

In experimental research, the validity of a causal claim depends on the researcher's ability to isolate the effect of one specific factor. This is achieved through the systematic manipulation of the independent variable while strictly controlling all other potential influences, ensuring that any observed changes in the dependent variable are truly attributable to the experimental treatment.

1. Definition & Core Concepts

Independent Variable (IV): This is the factor that the researcher deliberately changes or manipulates to observe its effect. It represents the 'cause' in a cause-and-effect relationship.
Dependent Variable (DV): This is the factor that is measured by the researcher. Its value is expected to 'depend' on the changes made to the independent variable.
Operationalization: The process of defining variables in precise, measurable terms. For example, instead of measuring 'intelligence,' a researcher might operationalize it as a specific score on a standardized test.
Extraneous Variables: Any variables other than the IV that have the potential to influence the DV. If left unchecked, these can introduce 'noise' into the data.

Conceptual diagram showing the Independent Variable (IV) influencing the Dependent Variable (DV), with Controlled Variables acting as a stabilizing force to ensure the relationship is clear.

2. Underlying Principles

Causal Isolation: The primary goal of control is to ensure that the IV is the only factor varying systematically between experimental conditions. This allows researchers to conclude that the IV caused the change in the DV.
Internal Validity: This refers to the degree to which an experiment is free from flaws in its internal structure. High internal validity means the researcher can be confident in the cause-and-effect relationship discovered.
The Law of Parsimony: In experimental design, researchers aim for the simplest explanation. By controlling variables, they eliminate complex alternative explanations for their findings.

3. Methods & Techniques of Control

4. Key Distinctions

5. Exam Strategy & Tips

6. Common Pitfalls & Misconceptions

Confusing Correlation with Causation: Just because two variables change together does not mean one manipulated the other. Without strict control, you only have a correlation.
Over-controlling: While control is necessary for internal validity, controlling too many factors can make the experiment so artificial that it no longer applies to the real world (low external validity).
Natural Variables as IVs: Remember that factors like age, gender, or personality cannot be 'manipulated' by the researcher; studies using these are 'quasi-experiments' rather than true experiments.