What is the fundamental difference between Independent Groups and Repeated Measures designs?

In Independent Groups, different participants are used for each condition of the IV, whereas in Repeated Measures, the same participants complete all conditions. This means Independent Groups compares different people, while Repeated Measures compares the same person to themselves.

How does Matched Pairs design differ from Independent Groups design?

While both use different people in each condition, Matched Pairs ensures those people are similar on key variables (like IQ or age) before the experiment starts. Independent Groups relies on random allocation to balance differences, whereas Matched Pairs actively pairs similar individuals to reduce participant variables.

When should a researcher choose Repeated Measures over Independent Groups?

Repeated Measures is preferred when the participant sample is small or when individual differences (like natural ability) are likely to significantly skew the results. It is also useful when the researcher wants to see how an individual's behavior changes directly across different levels of a treatment.

What is the 'Practice Effect' and which design does it threaten?

The Practice Effect occurs when participants perform better in a second condition because they have already done the task once and are now familiar with it. This is a major threat to Repeated Measures designs, as it can make the second condition appear more effective than it actually is.

Why is 'Random Allocation' critical in Independent Groups designs?

Without random allocation, a researcher might accidentally put all the 'high-performing' participants in one group, creating a bias. Randomly assigning participants ensures that individual differences are spread evenly across conditions, making the groups comparable at the start.

What error occurs if a researcher fails to use counterbalancing in a Repeated Measures study?

The researcher risks 'Order Effects' becoming a confounding variable, meaning they won't know if the results were caused by the IV or simply by the order in which the tasks were performed. This significantly lowers the internal validity of the experiment.

Define 'Order Effects'.

Order effects are changes in participants' performance resulting from the sequence in which they experience experimental conditions. Common types include practice effects (improvement) and fatigue or boredom effects (decline).

What is 'Counterbalancing'?

Counterbalancing is an experimental technique used to overcome order effects in a repeated measures design. It involves ensuring that each condition is tested first and second an equal number of times (e.g., Group 1 does A then B; Group 2 does B then A).

Define 'Participant Variables'.

Participant variables are the individual characteristics of research subjects—such as age, intelligence, or personality—that could influence the outcome of an experiment. They are a primary concern in independent groups designs.

Why does Repeated Measures design have higher statistical power than Independent Groups?

Because it uses the same participants, it eliminates the 'noise' created by individual differences between groups. This makes it easier for researchers to detect a statistically significant effect caused by the independent variable itself.

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Experiments: Experimental Design

Summary

Experimental design refers to the specific framework used to organize participants across the different conditions of an experiment. It determines whether participants experience one or all levels of the independent variable, directly impacting the internal validity of the study by managing participant variables and potential biases.

1. Definition & Core Concepts

Experimental Design is the strategy a researcher uses to allocate participants to the various conditions of the Independent Variable (IV). It is a critical planning stage that occurs before data collection begins.

The primary goal of choosing a specific design is to ensure that any observed changes in the Dependent Variable (DV) are caused by the IV rather than by differences between the participants or the order in which tasks are completed.

A well-chosen design minimizes extraneous variables, which are factors other than the IV that could potentially influence the results if not controlled.

Diagram comparing Independent Groups (different people in each condition) and Repeated Measures (same people in both conditions).

2. Independent Groups Design

Methodology: In this design, participants are divided into separate groups, with each group experiencing only one condition of the independent variable. Their results are then compared against each other.
Random Allocation: To prevent researcher bias and ensure groups are as similar as possible, participants should be assigned to conditions using a random process (e.g., drawing names from a hat).
Strengths: This design avoids order effects because participants only perform one task. It also reduces demand characteristics, as participants are less likely to guess the study's aim since they only see one part of the experiment.
Weaknesses: The main drawback is participant variables (individual differences like IQ or personality) which may act as confounding variables. It also requires a larger sample size compared to other designs.

3. Repeated Measures Design

4. Matched Pairs Design

5. Key Distinctions

6. Exam Strategy & Tips