Multiple Imputation for Systematically Missing Partner Variables in Survey Data

Haensch, Anna-Carolina und Schunck, Reinhard (2024): Multiple Imputation for Systematically Missing Partner Variables in Survey Data. In: Sociological Methodology [Forthcoming]

Abstract

Systematically missing information on secondary respondents is a frequent problem in multiactor surveys. Budget and time constraints often prevent all variables collected for primary respondents (e.g., anchors) from being collected for secondary respondents (e.g., partners). Thus, a subset of variables are systematically missing for secondary respondents. This can severely limit the analysis potential of multiactor data, ruling out all research questions that would require (the same) information on primary and secondary respondents. The problem of systematically missing data is also present in other settings, for example, after changes in measurement instruments in repeated surveys or in ex post survey harmonization if one or more surveys did not include a specific variable. In these cases, using multiple imputation (MI) techniques to impute the missing variables is a common approach. The authors explore whether MI can be used when data on secondary respondents are systematically missing. Results from simulation studies show that imputation under the assumption of conditional independence for primary and secondary respondents variables leads to a strong bias toward zero in the estimated partial correlation between primary and secondary respondents. However, external data in the form of bridging studies can be used to estimate the partial correlation between the observed variable for the primary and the unobserved variable for the secondary respondent, leading to estimates with less bias after MI.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Mathematik, Informatik und Statistik > Statistik
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 000 Informatik, Wissen, Systeme ; 600 Technik, Medizin, angewandte Wissenschaften > 600 Technik
• ISSN: 0081-1750
• Bemerkung: First published online December 2, 2024
• Sprache: Englisch
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 316901171
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 430266278