ACSPRI Conferences, RC33 Eighth International Conference on Social Science Methodology

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Multiple Imputation of Incomplete Multilevel Count Data

Kristian Kleinke, Jost Reinecke

Building: Law Building
Room: Breakout 7 - Law Building, Room 028
Date: 2012-07-12 01:30 PM – 03:00 PM
Last modified: 2011-12-20


Over the last couple of years multiple imputation has become a popular and widely accepted technique to handle missing data properly. Although various multiple imputation procedures have been implemented in all major statistical packages, currently available software is still highly limited regarding the imputation of incomplete count data. As count data analysis typically makes it necessary to fit statistical models that are suited for count data like Poisson or negative binomial models, also imputation procedures should be specially tailored to the statistical specialities of count data. We present flexible and easy to use software to create multiple imputations of incomplete ordinary and overdispersed multilevel count data, based on a generalized linear mixed model with multivariate normal random effects, using penalized quasi-likelihood. Our procedure works as an add-on for the popular and powerful MICE software (van Buuren & Groothuis-Oudshoorn, 2011). The advantage is that users simply work in MICE and call this functions directly from MICE and do not have to familiarize themselves with yet another statistical software.