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Accommodating a Latent XM Interaction in Statistical Mediation Analysis.

Oscar Gonzalez1, Matthew J Valente2

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This summary is machine-generated.

This study presents eight methods for estimating treatment-by-mediator interactions when the mediator is latent in statistical mediation analysis. These approaches, using structural models or mediator scoring, showed low bias and good interval coverage in simulations.

Keywords:
Statistical mediationlatent interactionslatent variable scoresstructural models

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Area of Science:

  • Social Sciences
  • Public Health
  • Psychometrics
  • Statistics

Background:

  • Statistical mediation analysis identifies mechanisms linking treatments to outcomes.
  • Estimating treatment-by-mediator (XM) interactions is crucial for understanding mediation effects in linear and potential outcomes frameworks.
  • Limited guidance exists for estimating XM interactions with latent mediators.

Purpose of the Study:

  • To present and evaluate methods for estimating treatment-by-mediator interactions when the mediator is latent.
  • To provide practical guidance for researchers conducting mediation analysis with latent variables.
  • To compare the performance of different estimation approaches through simulations.

Main Methods:

  • Eight methods are discussed, categorized into structural equation modeling approaches and mediator scoring techniques.
  • Structural model methods include latent moderated structural equations, Bayesian mediation, unconstrained product indicator, and multiple-group models.
  • Mediator scoring methods involve summed scores and factor scores, with and without attenuation correction.

Main Results:

  • Simulation results indicate low finite-sample bias across all methods.
  • Type 1 error rates and coverage of confidence/credible intervals were close to nominal levels.
  • Statistical power was comparable among the evaluated approaches.

Conclusions:

  • The discussed methods offer viable solutions for estimating latent XM interactions in mediation analysis.
  • Researchers can confidently apply these techniques, supported by simulation evidence of good performance.
  • Practical implementation guidance and syntax are provided for applied researchers.