Journal
PSYCHOLOGICAL METHODS
Volume 23, Issue 2, Pages 298-317Publisher
AMER PSYCHOLOGICAL ASSOC
DOI: 10.1037/met0000148
Keywords
missing data; multiple imputation; multilevel models; imputation software
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Funding
- Institute of Educational Sciences [R305D150056]
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Specialized imputation routines for multilevel data are widely available in software packages, but these methods are generally not equipped to handle a wide range of complexities that are typical of behavioral science data. In particular, existing imputation schemes differ in their ability to handle random slopes, categorical variables, differential relations at Level-1 and Level-2, and incomplete Level-2 variables. Given the limitations of existing imputation tools, the purpose of this manuscript is to describe a flexible imputation approach that can accommodate a diverse set of 2-level analysis problems that includes any of the aforementioned features. The procedure employs a fully conditional specification (also known as chained equations) approach with a latent variable formulation for handling incomplete categorical variables. Computer simulations suggest that the proposed procedure works quite well, with trivial biases in most cases. We provide a software program that implements the imputation strategy, and we use an artificial data set to illustrate its use.
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