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2014 Working with Missing Data: Imputation of Nonresponse Items in Categorical Survey Data with a Non-Monotone Missing Pattern
Machelle D. Wilson, Kerstin Lueck
J. Appl. Math. 2014: 1-9 (2014). DOI: 10.1155/2014/368791

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.

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Machelle D. Wilson. Kerstin Lueck. "Working with Missing Data: Imputation of Nonresponse Items in Categorical Survey Data with a Non-Monotone Missing Pattern." J. Appl. Math. 2014 1 - 9, 2014. https://doi.org/10.1155/2014/368791

Information

Published: 2014
First available in Project Euclid: 2 March 2015

zbMATH: 07131553
Digital Object Identifier: 10.1155/2014/368791

Rights: Copyright © 2014 Hindawi

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