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The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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Selecting a Within- or Between-Subject Design for Mediation: Validity, Causality, and Statistical Power.

Amanda K Montoya1

  • 1Department of Psychology, University of California, Los Angeles.

Multivariate Behavioral Research
|June 9, 2022
PubMed
Summary

Choosing between within-subject and between-subject designs for mediation analysis requires balancing validity, causality, and statistical power. Within-subject designs offer higher statistical power but may introduce threats to validity and causality.

Keywords:
Mediation analysisMonte Carlo simulationcausal inferenceindirect effectpowerpower analysistype I errorvaliditywithin-subject design

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Area of Science:

  • Psychology
  • Statistics
  • Research Methodology

Background:

  • Mediation analysis is crucial for understanding indirect effects in research.
  • Researchers face a critical choice between within-subject and between-subject designs for mediation studies.
  • Design selection impacts validity, causal inference, and statistical power.

Purpose of the Study:

  • To critically evaluate the trade-offs between within-subject and between-subject designs in mediation analysis.
  • To identify key factors—validity, causality, and statistical power—that should guide design choice.
  • To provide empirical evidence and tools for researchers conducting mediation analyses.

Main Methods:

  • A Monte Carlo simulation was employed to compare within-subject and between-subject designs.
  • The simulation examined various sample sizes, effect sizes, and correlations among repeated measures.
  • An empirical example and R script were developed for power analysis in within-subject mediation.

Main Results:

  • Within-subject designs generally require approximately half the sample size of between-subject designs to detect similar indirect effects.
  • The power advantage of within-subject designs can be influenced by population parameters.
  • Causal inference assumptions differ, with between-subject designs needing stricter no-confounder assumptions, while within-subject designs assume no carry-over effects.

Conclusions:

  • Design choice in mediation analysis should not solely prioritize statistical power.
  • Validity and causality considerations are paramount and may favor between-subject designs in certain contexts.
  • Researchers must carefully weigh the advantages and disadvantages of each design for robust mediation findings.