Imputation of missing data from split questionnaire designs in social surveys

Axenfeld, Julian B.

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URN:	urn:nbn:de:bsz:180-madoc-651182
Dokumenttyp:	Dissertation
Erscheinungsjahr:	2023
Ort der Veröffentlichung:	Mannheim
Hochschule:	Universität Mannheim
Gutachter:	Keusch, Florian
Datum der mündl. Prüfung:	24 Juli 2023
Sprache der Veröffentlichung:	Englisch
Einrichtung:	Fakultät für Sozialwissenschaften > Data Science (Blom 2017-2022)
Lizenz:	Creative Commons Namensnennung 4.0 International (CC BY 4.0)
Fachgebiet:	300 Sozialwissenschaften, Soziologie, Anthropologie
Normierte Schlagwörter (SWD):	Umfrage , Imputationstechnik , fehlende Daten
Freie Schlagwörter (Englisch):	imputation , planned missing data , split questionnaire designs , social survey research , survey methodology
Abstract:	Amidst the challenges of declining response rates and escalating costs in survey research, the adoption of innovative new data collection designs such as planned missingness and split questionnaire designs is becoming increasingly prevalent. This dissertation addresses the imputation of social survey data from split questionnaire designs and the methodological decisions associated with implementing such surveys to facilitate imputation. Through a series of Monte Carlo simulations, drawing on real social survey data from the German Internet Panel and the European Social Survey, this research assesses the accuracy of estimates across various scenarios, encompassing the implementation of both the split questionnaire design and the subsequent imputation. It delves into the impacts of different split questionnaire module construction strategies, varying imputation techniques, the interplay between planned missingness and conventional item nonresponse, and the implications of general-purpose versus analysis-specific imputation on the accuracy of estimates for a multivariate model. The insights gleaned from these simulations offer valuable guidance and recommendations for the implementation of split questionnaire designs in social surveys.