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Monte Carlo confidence intervals for the indirect effect with missing data.

Ivan Jacob Agaloos Pesigan1, Shu Fai Cheung2

  • 1Department of Psychology, Faculty of Social Sciences, University of Macau, Avenida da Universidade, Taipa, Macao SAR, China. i.j.a.pesigan@connect.um.edu.mo.

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|August 7, 2023
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Summary
This summary is machine-generated.

This study introduces a fast and accurate two-step Monte Carlo method for constructing confidence intervals for indirect effects in mediation analysis with missing data. This approach enhances statistical rigor when data is incomplete.

Keywords:
Full-information maximum likelihoodIndirect effectMediationMissing at randomMissing completely at randomMonte Carlo methodMultiple imputationNonparametric bootstrap

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Area of Science:

  • Statistics
  • Quantitative Psychology
  • Econometrics

Background:

  • Missing data is prevalent in mediation analysis, complicating the accurate estimation of indirect effects.
  • Existing methods for confidence intervals often assume complete data, limiting their applicability.
  • Nonparametric bootstrap and Monte Carlo methods are established for complete data, but require adaptation for missing data scenarios.

Purpose of the Study:

  • To propose a novel, efficient, and precise two-step method for confidence intervals of indirect effects in mediation analysis with missing data.
  • To adapt the Monte Carlo method to effectively handle missing data in mediation models.
  • To evaluate the performance of the proposed method through a simulation study.

Main Methods:

  • A two-step approach is presented for generating confidence intervals for the indirect effect.
  • Step 1 involves parameter estimation and variance-covariance matrix calculation using methods like Full-Information Maximum Likelihood (FIML) or Multiple Imputation (MI).
  • Step 2 simulates the sampling distribution of the indirect effect using the estimates from Step 1, followed by confidence interval construction.

Main Results:

  • The proposed two-step Monte Carlo method demonstrates simplicity, speed, and accuracy in generating confidence intervals for indirect effects with missing data.
  • Simulation studies confirm the method's viability across various conditions.
  • The approach provides a reliable alternative to traditional methods when dealing with incomplete datasets in mediation analysis.

Conclusions:

  • The developed two-step Monte Carlo method offers a practical solution for constructing confidence intervals for indirect effects in the presence of missing data.
  • This method enhances the robustness and applicability of mediation analysis in real-world research scenarios.
  • Findings have significant implications for applied researchers seeking to accurately interpret indirect effects with incomplete data.