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Two-condition within-participant statistical mediation analysis: A path-analytic framework.

Amanda K Montoya1, Andrew F Hayes1

  • 1Department of Psychology, The Ohio State University.

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Summary
This summary is machine-generated.

This study introduces a path-analytic framework for within-participant mediation analysis, simplifying mediation testing by focusing on the indirect effect. The approach allows for robust statistical inference, including bootstrapping, for mediation models.

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Area of Science:

  • Psychology
  • Statistics
  • Behavioral Science

Background:

  • Traditional mediation analysis often uses separate hypothesis tests for model components.
  • The Judd et al. (2001) approach for within-participant mediation relies on these discrete tests.
  • This method does not directly estimate or infer the indirect effect.

Purpose of the Study:

  • To recast the Judd et al. approach within a path-analytic framework for within-participant mediation.
  • To enable direct estimation and inference of the indirect effect in within-participant mediation.
  • To generalize existing inference methods for indirect effects to within-participant designs.

Main Methods:

  • Recasting the Judd et al. mediation approach into a path-analytic framework.
  • Estimating the indirect effect as the product of path influences.
  • Generalizing between-participant inference methods (bootstrap, Monte Carlo CIs) to within-participant mediation.

Main Results:

  • The path-analytic approach allows for direct estimation and inference of the indirect effect.
  • This eliminates the need for multiple discrete hypothesis tests.
  • The method is extended to models with multiple parallel and serial mediators.

Conclusions:

  • A path-analytic framework provides a unified approach to within-participant mediation analysis.
  • Direct inference on the indirect effect simplifies mediation testing.
  • The approach is applicable to complex mediation models and offers computational tools.