Comparison of imputation methods for univariate categorical longitudinal data

Article Article scientifique Article Comparison of imputation methods for univariate categorical longitudinal data eng 2024-12-26 author Emery Kevin 0000-0002-8609-4591 982249 Centre interfacultaire de gérontologie et d'études des vulnérabilités (CIGE) author Studer Matthias 0000-0002-6269-1412 511630 Service enseignement, recherche, administratif et technique (SdS) (SCSPS) author Berchtold André 29051 10.1007/s11135-024-02028-z 0033-5177 https://link.springer.com/10.1007/s11135-024-02028-z <p>The life course paradigm emphasizes the need to study not only the situation at a given point in time, but also its evolution over the life course in the medium and long term. These trajectories are often represented by categorical data. This article aims to provide a comprehensive review of the multiple imputation methods proposed so far in the context of univariate categorical data and to assess their practical relevance through a simulation study based on real data. The primary goal is to provide clear methodological guidelines and improve the handling of missing data in life course research. In parallel, we develop the MICT-timing algorithm, which is an extension of the MICT algorithm. This innovative multiple imputation method improves the quality of imputation in trajectories subject to time-varying transition rates, a situation often encountered in life course data.</p> Dewey 304.6/305.3/306 Swiss National Science Foundation Strengthening Sequence Analysis 204740 CH Projects 195 Institut de démographie et de socioéconomie TH false 1282 Centre LIVES Quality and quantity 24bb0e5d-2072-4a09-9204-3b26c4c119e1 Article (Published version) Public Article_scientifique_from_crossref.pdf 1112228 application/pdf CC BY-4.0 5b49be7a08aa679292ff65019704ea99233b63047478ae3a3c9c86a10514133a openAccess Faculté des sciences de la société / Institut de démographie et de socioéconomie

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Comparison of imputation methods for univariate categorical longitudinal data	Quality and quantity			2024	70	363

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