Repeated measures design

Definition

A repeated measures design is a research strategy where the same subjects are used for each treatment condition, allowing for the measurement of changes over time or across different conditions. This design helps control for individual differences since each participant serves as their own control, making it easier to detect effects of the treatment or intervention being studied.

5 Must Know Facts For Your Next Test

1. Repeated measures designs are beneficial because they reduce the variability caused by individual differences, enhancing statistical power.
2. One of the key advantages is that fewer participants are required compared to between-subjects designs, making it more efficient.
3. However, researchers must be cautious about potential carryover effects, where earlier treatments affect responses to later treatments.
4. Statistical analysis for repeated measures typically involves methods like ANOVA for repeated measures, which accounts for the correlated nature of the data.
5. This design is particularly useful in longitudinal studies, where changes over time within the same subjects are of interest.

Review Questions

• How does a repeated measures design help in controlling for individual differences in research studies?
  • A repeated measures design allows each participant to serve as their own control by undergoing all treatment conditions. This means that individual differences are minimized since the same subjects experience different treatments, leading to a clearer understanding of how those treatments affect outcomes. It effectively reduces variability in results that could arise from having different subjects in each group.
• What statistical considerations must researchers take into account when analyzing data from a repeated measures design?
  • Researchers must consider the correlated nature of the data in a repeated measures design, as observations are not independent. This often requires the use of specialized statistical methods, such as ANOVA for repeated measures, which can handle these correlations and accurately assess treatment effects. It is also essential to account for potential carryover effects that may arise from the sequence of treatments.
• Evaluate the strengths and weaknesses of using a repeated measures design compared to a between-subjects design in psychological research.
  • Repeated measures designs have several strengths, including increased statistical power due to reduced variability and efficiency through fewer participants needed. However, they also come with weaknesses like potential carryover effects and increased complexity in analysis. In contrast, between-subjects designs can avoid carryover issues but may require larger sample sizes and may introduce greater variability due to individual differences among participants. The choice between these designs depends on the research question and specific context.

Related terms

Within-subjects design: A research design in which each participant is exposed to all levels of the independent variable, similar to repeated measures.

Carryover effect: A potential issue in repeated measures design where the effects of a previous condition influence responses in a subsequent condition.

ANOVA for repeated measures: A statistical method used to analyze data from repeated measures designs, allowing researchers to compare means across multiple conditions.