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. © 2014 machelle d. wilson and kerstin lueck.