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Impact of Inconsistent Imputation Models in Mediation Analysis with Clustered Data

In: Dependent Data in Social Sciences Research

Author

Listed:
  • Bo Ye

    (School of Public Health, State University of New York, Department of Epidemiology and Biostatistics)

  • Recai Yucel

    (College of Public Health, Temple University, Department of Epidemiology and Biostatistics)

  • Donna L. Coffman

    (University of South Carolina, Department of Psychology)

Abstract

The choice of imputation model is often driven by the focus of the post-imputation analyses. However, this can be impractical especially when multiple imputation (MI) inference is used in large-scale surveys or in applications with complex data structures. This work investigates the impact of the imputation model on mediation analysis with clustered data. Specifically, we consider joint and variable-by-variable imputation models prior to a 1-1-1 multilevel mediation analysis. We provide theoretical and analytical assessment of the bias under each imputation method. A comprehensive simulation study is conducted to understand the performance of imputation methods in a repetitive sampling framework. The choice of the selected imputation model does not have an influence on the multilevel mediation analysis.

Suggested Citation

  • Bo Ye & Recai Yucel & Donna L. Coffman, 2024. "Impact of Inconsistent Imputation Models in Mediation Analysis with Clustered Data," Springer Books, in: Mark Stemmler & Wolfgang Wiedermann & Francis L. Huang (ed.), Dependent Data in Social Sciences Research, edition 2, chapter 0, pages 617-656, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-56318-8_25
    DOI: 10.1007/978-3-031-56318-8_25
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