Imputation of repeatedly observed multinomial variables in longitudinal surveys

TitleImputation of repeatedly observed multinomial variables in longitudinal surveys
Publication TypeJournal Article
Year of Publication2017
AuthorsBerchtold, A, Suris, J-C
JournalCommunications in Statistics - Simulation and Computation
Volume46
Issue4
Pagination3267–3283
ISSN0361-0918
Keywords62P25, causality, chained equations, longitudinal survey, missing data, multiple imputation, Primary 62, Secondary 62D05
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.

URLhttps://www.tandfonline.com/doi/full/10.1080/03610918.2015.1082588
DOI10.1080/03610918.2015.1082588
Refereed DesignationRefereed