Journal of Applied Mathematics
- J. Appl. Math.
- Volume 2014 (2014), Article ID 368791, 9 pages.
Working with Missing Data: Imputation of Nonresponse Items in Categorical Survey Data with a Non-Monotone Missing Pattern
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.
J. Appl. Math., Volume 2014 (2014), Article ID 368791, 9 pages.
First available in Project Euclid: 2 March 2015
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Wilson, Machelle D.; Lueck, Kerstin. Working with Missing Data: Imputation of Nonresponse Items in Categorical Survey Data with a Non-Monotone Missing Pattern. J. Appl. Math. 2014 (2014), Article ID 368791, 9 pages. doi:10.1155/2014/368791. https://projecteuclid.org/euclid.jam/1425306086