General-purpose imputation of planned missing data in social surveys: Different strategies and their effect on correlations-Reference-Cited by-同舟云学术

General-purpose imputation of planned missing data in social surveys: Different strategies and their effect on correlations

Published:2022-01-01 Issue:none Volume:16 Page:
ISSN:1935-7516
Container-title:Statistics Surveys
language:
Short-container-title:Statist. Surv.

Author:

Axenfeld Julian B.¹,Bruch Christian²,Wolf Christof²

Affiliation:

1. Mannheim Centre for European Social Research, A5, 6, 68159 Mannheim, Germany

2. GESIS Leibniz Institute for the Social Sciences B6, 4-5, 68159 Mannheim, Germany

Publisher

Institute of Mathematical Statistics

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Reference73 articles.

1. ADIGÜZEL, F. and WEDEL, M. (2008). Split questionnaire design for massive surveys. Journal of Marketing Research 45 608–617.

2. ALLISON, P. D. (2005). Imputation of Categorical Variables with PROC MI. In Proceedings of the SAS Users Group International (SUGI) 30 113–30. SAS Institute, Cary.

3. AKANDE, O, LI, F. and REITER, J. (2017). An Empirical Comparison of Multiple Imputation Methods for Categorical Data. The American Statistician 71 162–170.

4. AXENFELD, J. B., BRUCH, C. and WOLF, C. (2022). Code and Data Availability. Supplement to “General-purpose imputation of planned missing data in social surveys: Different strategies and their effect on correlations.”

5. AXENFELD, J. B., BLOM, A.G., BRUCH, C. and WOLF, C. (2022). Split Questionnaire Designs for Online Surveys: The Impact of Module Construction on Imputation Quality. Journal of Survey Statistics and Methodology. https://doi.org/10.1093/jssam/smab055

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