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Latently Mediating: A Bayesian Take on Causal Mediation Analysis with Structured Survey Data.

Alessandro Varacca1

  • 1Department of Economics and Social Sciences (DiSes), Università Cattolica del Sacro Cuore, Piacenza (PC), Italy.

Multivariate Behavioral Research
|November 18, 2024
PubMed
Summary

This study introduces a Bayesian causal mediation method for Likert-scaled survey data. The approach accurately estimates causal effects by modeling measurement error and imputing counterfactuals, enhancing mediation analysis reliability.

Keywords:
Bayesian methodsCausal mediationg-computationitem response theorylatent variablesmeasurement error

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Area of Science:

  • Statistics
  • Psychometrics
  • Behavioral Economics

Background:

  • Analyzing experimental data with Likert-scaled measures presents challenges due to measurement error.
  • Traditional mediation analysis may be insufficient when both mediator and outcome are latent constructs.

Purpose of the Study:

  • To propose a Bayesian causal mediation approach for Likert-scaled experimental data.
  • To address measurement error in mediators and outcomes using Item Response Theory.
  • To enhance the accuracy and robustness of causal mediation analysis.

Main Methods:

  • Bayesian causal mediation framework.
  • Item Response Theory for modeling Likert-scaled data and latent variables.
  • G-computation algorithm for counterfactual imputation.
  • Sensitivity analysis for mediator's conditional ignorability.

Main Results:

  • The proposed method effectively models surrogate measures and their latent counterparts.
  • Causal parameters are estimated by imputing counterfactuals.
  • Sensitivity analysis confirms robustness to ignorability assumptions.

Conclusions:

  • The Bayesian approach offers a robust method for causal mediation analysis with Likert-scaled data.
  • Item Response Theory integration improves the handling of measurement error.
  • The methodology is applicable to various experimental settings, including behavioral studies.