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Methods for Mediation Analysis with Missing Data

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  • Zhiyong Zhang
  • Lijuan Wang

Abstract

Despite wide applications of both mediation models and missing data techniques, formal discussion of mediation analysis with missing data is still rare. We introduce and compare four approaches to dealing with missing data in mediation analysis including listwise deletion, pairwise deletion, multiple imputation (MI), and a two-stage maximum likelihood (TS-ML) method. An R package bmem is developed to implement the four methods for mediation analysis with missing data in the structural equation modeling framework, and two real examples are used to illustrate the application of the four methods. The four methods are evaluated and compared under MCAR, MAR, and MNAR missing data mechanisms through simulation studies. Both MI and TS-ML perform well for MCAR and MAR data regardless of the inclusion of auxiliary variables and for AV-MNAR data with auxiliary variables. Although listwise deletion and pairwise deletion have low power and large parameter estimation bias in many studied conditions, they may provide useful information for exploring missing mechanisms. Copyright The Psychometric Society 2013

Suggested Citation

  • Zhiyong Zhang & Lijuan Wang, 2013. "Methods for Mediation Analysis with Missing Data," Psychometrika, Springer;The Psychometric Society, vol. 78(1), pages 154-184, January.
    • Handle: RePEc:spr:psycho:v:78:y:2013:i:1:p:154-184
    DOI: 10.1007/s11336-012-9301-5
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    References listed on IDEAS

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    1. Ke-Hai Yuan, 2009. "Identifying Variables Responsible for Data not Missing at Random," Psychometrika, Springer;The Psychometric Society, vol. 74(2), pages 233-256, June.
    2. P. Bentler & David Weeks, 1980. "Linear structural equations with latent variables," Psychometrika, Springer;The Psychometric Society, vol. 45(3), pages 289-308, September.
    3. N. G. Best & D. J. Spiegelhalter & A. Thomas & C. E. G. Brayne, 1996. "Bayesian Analysis of Realistically Complex Models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 159(2), pages 323-342, March.
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    1. D. Gunzler & W. Tang & N. Lu & P. Wu & X. Tu, 2014. "A Class of Distribution-Free Models for Longitudinal Mediation Analysis," Psychometrika, Springer;The Psychometric Society, vol. 79(4), pages 543-568, October.
    2. Zhou, Wubiao, 2017. "Institutional environment, public-private hybrid forms, and entrepreneurial reinvestment in a transition economy," Journal of Business Venturing, Elsevier, vol. 32(2), pages 197-214.
    3. Antonio Calcagnì & Luigi Lombardi & Lorenzo Avanzi & Eduardo Pascali, 2020. "Multiple mediation analysis for interval-valued data," Statistical Papers, Springer, vol. 61(1), pages 347-369, February.

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