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| Autoren | Aßmann, Christian; Würbach, Ariane; Goßmann, Solange; Geissler, Ferdinand; Biedermann, Anika |
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| Institution | Leibniz-Institut für Bildungsverläufe; Nationales Bildungspanel |
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| Titel | A nonparametric multiple imputation approach for multilevel filtered questionnaires. |
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| URL | https://www.neps-data.de/Portals/0/Working Papers/WP_XXXVI.pdf |
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| Erscheinungsjahr | 2014 |
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| Seitenzahl | 27 S. |
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| Verlag | Bamberg: Leibniz Institute for Educational Trajectories (LIfBi) |
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| Reihe | NEPS working paper. Band 36 |
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| Dokumenttyp | Monographie; Graue Literatur; online |
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| Beigaben | Literaturangaben, Abbildungen, Tabellen, Anhang |
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| Sprache | englisch |
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| Forschungsschwerpunkt | Bildungspanel (NEPS) |
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| Schlagwörter | Datenanalyse; Variable; Filterverfahren; Fragebogen; Regressionsanalyse; Einkommen; Erwachsener |
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| Abstract | Despite high efforts in field work and questionnaire design, low rates of missing values inevitably occur. The principles of multiple imputation allow for addressing this issue enhancing the analytical potential of the surveyed data. Large scale surveys provide rich data structures characterized by manifold discrete variables in combination with multilevel filtering in questionnaires. This requires multiple imputation techniques to preserve possible nonlinear relationships among the surveyed variables and full conditional distributions incorporating the information from multilevel filtering rules on an individual basis. To meet these requirements, a tree-based sequential regression approach is adapted addressing both the issues of possibly nonlinear relationships between categorical variables and complex multilevel filtering. Handling of filters within imputation is thereby adapted in a way to ensure 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. (Orig.). |
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| Förderkennzeichen | 01GJ0888 |
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