Imputation of Repeatedly-observed Multinomial Variables in Longitudinal Surveys

Détails

ID Serval
serval:BIB_274BD3B8F7C6
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Imputation of Repeatedly-observed Multinomial Variables in Longitudinal Surveys
Périodique
Communications in Statistics - Simulation and Computation
Auteur(s)
Berchtold A, Suris JC
ISSN
0361-0918 (Print)
1532-4141 (Online)
Statut éditorial
In Press
Peer-reviewed
Oui
Langue
anglais
Résumé
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, 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.
Mots-clé
Longitudinal survey, missing data, multiple imputation, chained equations, causality
Création de la notice
11/01/2016 11:11
Dernière modification de la notice
01/11/2019 10:29
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