Imputation of repeatedly observed multinomial variables in longitudinal surveys. Issue 4 (21st April 2017)

• Record Type: Journal Article
• Authors: Berchtold, André; Surís, Joan-Carles
• Is Part Of: Communications in statistics. Volume 46:Issue 4(2017)
• Page Start: 3267
• Page End: 3283
• Publication Date: 2017-04-21
• Subjects: Causality -- Chained equations -- Longitudinal survey -- Missing data -- Multiple imputation; Primary 62 -- Secondary 62D05 -- 62P25; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2015.1082588
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: It is now a standard practice to replace missing data in longitudinal surveys with imputed values, but there is still much uncertainty about the best approach to adopt. Using data from a real survey, we compared different strategies combining multiple imputation and the chained equations method, the two main objectives being (1) to explore the impact of the explanatory variables in the chained regression equations and (2) to study the effect of imputation on causality between successive waves of the survey. Results were very stable from one simulation to another, and no systematic bias did appear. The critical points of the method lied in the proper choice of covariates and in the respect of the temporal relation between variables.