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

Machelle D. Wilson and Kerstin Lueck

Journal of Applied Mathematics, 2014, vol. 2014, issue 1

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.

Date: 2014
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https://doi.org/10.1155/2014/368791

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jnljam:v:2014:y:2014:i:1:n:368791

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