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Learning Objectives
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After studying Chapter 8, you should know and understand the following key points:

Why Researchers Use Repeated Measures Designs
Researchers choose to use a repeated measures design to conduct an experiment when few participants are available, or to conduct the experiment more efficiently, or to increase the sensitivity of the experiment, or to study changes in participants' behavior over time.

The Role of Practice Effects in Repeated Measures Designs

Repeated measures designs cannot be confounded by individual differences variables because the same individuals participate in each condition (level) of the independent variable.

Participants' performance in repeated measures designs may change across conditions simply because of repeated testing (not because of the independent variable); these changes are called practice effects.

Practice effects may threaten the internal validity of a repeated measures experiment when the different conditions of the independent variable are presented in the same order to all participants.

There are two types of repeated measures designs (complete and incomplete) that differ in the specific ways in which they control for practice effects.

Balancing Practice Effects in the Complete Design
Practice effects are balanced in complete designs within each participant using block randomization or ABBA counterbalancing.

In block randomization, the conditions of the experiment (a block) are randomly ordered each time they are presented.

In ABBA counterbalancing, a random sequence of all conditions is presented, followed by the opposite of the sequence.

Block randomization is preferred over ABBA counterbalancing when practice effects are not linear, or when participants' performance can be affected by anticipation effects.

Balancing Practice Effects in the Incomplete Design

Practice effects are balanced across subjects in the incomplete design rather than within subjects, as in the complete design.

The rule for balancing practice effects in the incomplete design is that each condition of the experiment must be presented in each ordinal position (first, second, etc.) equally often.

The best method for balancing practice effects in the incomplete design with four or fewer conditions is to use all possible orders of the conditions.

Two methods for selecting specific orders to use in an incomplete design are the Latin Square and random starting order with rotation.

Whether using all possible orders or selected orders, participants should be randomly assigned to the different sequences.
Data Analysis of Repeated Measures Designs
Describing the Results
Data analysis for a complete design begins with computing a summary score (e.g., mean, median) for each participant.

Descriptive statistics are used to summarize performance across all participants for each condition of the independent variable.
Confirming What the Results Reveal
The general procedures and logic for null hypothesis testing for repeated measures designs are similar to those used for random groups designs.
The Problem of Differential Transfer
Differential transfer occurs when the effects of one condition persist and influence performance in subsequent conditions.

Variables that may lead to differential transfer should be tested using a random groups design because differential transfer threatens the internal validity of repeated measures designs.

Differential transfer can be identified by comparing the results for the same independent variable when tested in a repeated measures design and in a random groups design.

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