Nonparametric multiple imputation for questionnaires with individual skip patterns and constraints : The case of income imputation in the National Educational Panel Study

  • Large-scale surveys typically exhibit data structures characterized by rich mutual dependencies between surveyed variables and individual-specific skip patterns. Despite high efforts in fieldwork and questionnaire design, missing values inevitably occur. One approach for handling missing values is to provide multiply imputed data sets, thus enhancing the analytical potential of the surveyed data. To preserve possible nonlinear relationships among variables and incorporate skip patterns that make the full conditional distributions individual specific, we adapt a full conditional multiple imputation approach based on sequential classification and regression trees. Individual-specific skip patterns and constraints are handled within imputation in a way ensuring the consistency of the sequence of full conditional distributions. The suggested approach is illustrated in the context of income imputation in the adult cohort of the National Educational Panel Study.

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Institutes:Fakultät Sozial- und Wirtschaftswissenschaften / Lehrstuhl für Statistik und Ökonometrie in den Sozial- und Wirtschaftswissenschaften
Fakultät Sozial- und Wirtschaftswissenschaften / Lehrstuhl für Volkswirtschaftslehre, insbesondere Empirische Mikroökonomik
Zentrale Einrichtungen und Institute / Leibniz-Institut für Bildungsverläufe e.V. (LIfBi) an der Universität Bamberg
Author:Christian Aßmann, Ariane Würbach, Solange Goßmann, Ferdinand Geißler, Anika Bela
Title of the journal / compilation (English):Sociological Methods and Research
Place of publication:Thousand Oaks [u.a.]
Publisher:Sage Publ.
Year of publication:2015
Pages / Size:34 S.
Keywords:CART; missing income values; nonparametric multiple imputation; skip patterns
Document Type:Article in a journal
Release Date:2016/01/21