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

Simon Grund, Oliver Lüdtke and Alexander Robitzsch

Journal of Educational and Behavioral Statistics, 2018, vol. 43, issue 3, 316-353

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.

Keywords: multiple imputation; missing data; multilevel; Level 2 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.3102/1076998617738087 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:43:y:2018:i:3:p:316-353

DOI: 10.3102/1076998617738087

Access Statistics for this article

More articles in Journal of Educational and Behavioral Statistics
Bibliographic data for series maintained by SAGE Publications ().