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Journal of Applied Mathematics
Volume 2014, Article ID 368791, 9 pages
Research Article

Working with Missing Data: Imputation of Nonresponse Items in Categorical Survey Data with a Non-Monotone Missing Pattern

1Department of Public Health Sciences, Division of Biostatistics, University of California, Davis, Davis, CA 95616, USA
2Social Psychology, The University of Adelaide, Adelaide, SA 5005, Australia
3Department of Integration and Conflict, Max Planck Institute, 06017 Halle, Germany

Received 9 June 2014; Accepted 16 October 2014; Published 7 December 2014

Academic Editor: Jin Liang

Copyright © 2014 Machelle D. Wilson and Kerstin Lueck. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The imputation of missing data is often a crucial step in the analysis of survey data. This study reviews typical problems with missing data and discusses a method for the imputation of missing survey data with a large number of categorical variables which do not have a monotone missing pattern. We develop a method for constructing a monotone missing pattern that allows for imputation of categorical data in data sets with a large number of variables using a model-based MCMC approach. We report the results of imputing the missing data from a case study, using educational, sociopsychological, and socioeconomic data from the National Latino and Asian American Study (NLAAS). We report the results of multiply imputed data on a substantive logistic regression analysis predicting socioeconomic success from several educational, sociopsychological, and familial variables. We compare the results of conducting inference using a single imputed data set to those using a combined test over several imputations. Findings indicate that, for all variables in the model, all of the single tests were consistent with the combined test.