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Multiple Imputation of Missing Data at Level 2: A Comparison of Fully Conditional and Joint Modeling in Multilevel Designs

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  • Simon Grund
  • Oliver Lüdtke
  • Alexander Robitzsch

Abstract

Multiple imputation (MI) can be used to address missing data at Level 2 in multilevel research. In this article, we compare joint modeling (JM) and the fully conditional specification (FCS) of MI as well as different strategies for including auxiliary variables at Level 1 using either their manifest or their latent cluster means. We show with theoretical arguments and computer simulations that (a) an FCS approach that uses latent cluster means is comparable to JM and (b) using manifest cluster means provides similar results except in relatively extreme cases with unbalanced data. We outline a computational procedure for including latent cluster means in an FCS approach using plausible values and provide an example using data from the Programme for International Student Assessment 2012 study.

Suggested Citation

  • Simon Grund & Oliver Lüdtke & Alexander Robitzsch, 2018. "Multiple Imputation of Missing Data at Level 2: A Comparison of Fully Conditional and Joint Modeling in Multilevel Designs," Journal of Educational and Behavioral Statistics, , vol. 43(3), pages 316-353, June.
    • Handle: RePEc:sae:jedbes:v:43:y:2018:i:3:p:316-353
    DOI: 10.3102/1076998617738087
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