Handling Missing Data in Cross-Classified Multilevel Analyses: An Evaluation of Different Multiple Imputation Approaches

Simon Grund, Oliver Lüdtke and Alexander Robitzsch
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Simon Grund: IPN - Leibniz Institute for Science and Mathematics Education Centre for International Student Assessment University of Hamburg
Alexander Robitzsch: IPN - Leibniz Institute for Science and Mathematics Education Centre for International Student Assessment

Journal of Educational and Behavioral Statistics, 2023, vol. 48, issue 4, 454-489

Abstract: Multiple imputation (MI) is a popular method for handling missing data. In education research, it can be challenging to use MI because the data often have a clustered structure that need to be accommodated during MI. Although much research has considered applications of MI in hierarchical data, little is known about its use in cross-classified data, in which observations are clustered in multiple higher-level units simultaneously (e.g., schools and neighborhoods, transitions from primary to secondary schools). In this article, we consider several approaches to MI for cross-classified data (CC-MI), including a novel fully conditional specification approach, a joint modeling approach, and other approaches that are based on single- and two-level MI. In this context, we clarify the conditions that CC-MI methods need to fulfill to provide a suitable treatment of missing data, and we compare the approaches both from a theoretical perspective and in a simulation study. Finally, we illustrate the use of CC-MI in real data and discuss the implications of our findings for research practice.

Keywords: missing data; multiple imputation; cross-classification; clustered data; random-effects models; multilevel analysis (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:48:y:2023:i:4:p:454-489

DOI: 10.3102/10769986231151224

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