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

Author

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  • Machelle D. Wilson
  • Kerstin Lueck

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.

Suggested Citation

  • Machelle D. Wilson & Kerstin Lueck, 2014. "Working with Missing Data: Imputation of Nonresponse Items in Categorical Survey Data with a Non‐Monotone Missing Pattern," Journal of Applied Mathematics, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnljam:v:2014:y:2014:i:1:n:368791
    DOI: 10.1155/2014/368791
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    References listed on IDEAS

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    1. Horton, Nicholas J. & Kleinman, Ken P., 2007. "Much Ado About Nothing: A Comparison of Missing Data Methods and Software to Fit Incomplete Data Regression Models," The American Statistician, American Statistical Association, vol. 61, pages 79-90, February.
    2. Liu, C. H., 1993. "Bartlett's Decomposition of the Posterior Distribution of the Covariance for Normal Monotone Ignorable Missing Data," Journal of Multivariate Analysis, Elsevier, vol. 46(2), pages 198-206, August.
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