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

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...

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Veröffentlicht in:	Journal of educational and behavioral statistics 2018-06, Vol.43 (3), p.316-353
Hauptverfasser:	Grund, Simon, Lüdtke, Oliver, Robitzsch, Alexander
Format:	Artikel
Sprache:	eng
Schlagworte:	Achievement Tests Bayesian analysis Comparative Analysis Computation Data Foreign Countries Hierarchical Linear Modeling International Assessment Maximum likelihood method Missing data Multivariate Analysis Secondary School Students Statistical Analysis
Online-Zugang:	Volltext
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