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Multiple-group analysis approach to testing group difference in indirect effects.

Ehri Ryu1

  • 1Department of Psychology, Boston College, Chestnut Hill, MA, 02467, USA, ehri.ryu.1@bc.edu.

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Summary
This summary is machine-generated.

This study presents five methods for comparing indirect effects across multiple groups. The likelihood ratio test and percentile bootstrap confidence intervals are recommended for testing group differences in indirect effects.

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Area of Science:

  • Psychometrics
  • Statistical modeling
  • Quantitative psychology

Background:

  • Indirect effects are crucial for understanding complex relationships in various fields.
  • Existing frameworks for moderated indirect effects do not directly test for equality of indirect effects between groups.
  • A need exists for methods that specifically assess group differences in indirect effects.

Purpose of the Study:

  • To introduce and evaluate five novel methods for testing group differences in indirect effects.
  • To provide direct statistical tests for the equality of indirect effects across multiple groups.
  • To compare the performance of these methods using simulation.

Main Methods:

  • Multiple-group analysis approach.
  • Simulation study to assess type I error rate, statistical power, and confidence interval coverage.
  • Evaluation of five distinct methods for indirect effect comparison.

Main Results:

  • The likelihood ratio test demonstrated favorable performance.
  • Percentile bootstrap confidence intervals showed good coverage properties.
  • Both methods are recommended for practical application in analyzing group differences in indirect effects.

Conclusions:

  • The introduced methods offer direct tests for equality of indirect effects between groups.
  • The likelihood ratio test and percentile bootstrap confidence intervals are statistically sound and recommended.
  • These methods are valuable for researchers investigating group-level variations in indirect relationships.