Impact of Inconsistent Imputation Models in Mediation Analysis with Clustered Data

Bo Ye, Recai Yucel () and Donna L. Coffman
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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

Chapter Chapter 25 in Dependent Data in Social Sciences Research, 2024, pp 617-656 from Springer

Abstract: 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.

Keywords: Multilevel mediation analysis; Missing data; Multiple imputation; Compatible imputation (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-56318-8_25

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DOI: 10.1007/978-3-031-56318-8_25

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