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How and why to follow best practices for testing mediation models with missing data.

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Summary
This summary is machine-generated.

Researchers often overlook best practices for missing data in mediation models. This study demonstrates how full information maximum likelihood (FIMLM) and multiple imputation (MI) reduce bias and improve generalizability, using R and JASP.

Keywords:
LongitudinalMediationMissing dataR software

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Area of Science:

  • Psychology
  • Statistics

Background:

  • Mediation models are crucial for understanding psychological change processes.
  • Researchers frequently fail to use best practices for missing data in mediation analyses.
  • Listwise deletion is common, despite known limitations.

Purpose of the Study:

  • To review and demonstrate best practice methods for handling missing data in mediation models.
  • To highlight the benefits of full information maximum likelihood (FIML) and multiple imputation (MI) over listwise deletion.
  • To provide practical guidance using free, open-source software (R and JASP).

Main Methods:

  • The tutorial reviews the implementation of FIML and MI for testing indirect effects in mediation.
  • Demonstrations are provided using R and JASP statistical software.
  • Two example mediation models (cross-sectional and longitudinal) are used for illustration.

Main Results:

  • FIML and MI reduce parameter estimate bias compared to listwise deletion.
  • These methods preserve sample size, maximizing statistical power and generalizability.
  • Practical implementation guides and supplemental materials are offered.

Conclusions:

  • Adopting FIML and MI is essential for robust mediation analysis.
  • Accessible tools like R and JASP facilitate the use of these advanced methods.
  • Improved missing data handling enhances the reliability and applicability of psychological research findings.