Imputation of Repeatedly-observed Multinomial Variables in Longitudinal Surveys
Details
State: Public
Version: Author's accepted manuscript
License: Not specified
Serval ID
serval:BIB_274BD3B8F7C6
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Imputation of Repeatedly-observed Multinomial Variables in Longitudinal Surveys
Journal
Communications in Statistics - Simulation and Computation
ISSN
0361-0918 (Print)
1532-4141 (Online)
1532-4141 (Online)
Publication state
Published
Issued date
2017
Peer-reviewed
Oui
Volume
46
Number
4
Pages
3267-3283
Language
english
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, 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.
Keywords
Longitudinal survey, missing data, multiple imputation, chained equations, causality
Create date
11/01/2016 12:11
Last modification date
22/01/2020 8:08