Working with Missing Data: Imputation of Nonresponse Items in Categorical Survey Data with a Non-Monotone Missing Pattern. (7th December 2014)

• Record Type: Journal Article
• Authors: Wilson, Machelle D.; Lueck, Kerstin
• Other Names: Liang Jin Academic Editor.
• Is Part Of: Journal of applied mathematics. Volume 2014(2014)
• Publication Date: 2014-12-07
• Subjects: Mathematics -- Periodicals; 519.05
• DOI: 10.1155/2014/368791
• Languages: English
• ISSNs: 1110-757X

Abstract

Abstract : 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.