Comparison of imputation methods for univariate categorical longitudinal data

• Published in: Quality & quantity Vol. 59; no. 2; pp. 1767 - 1791
• Main Authors: Emery, Kevin, Studer, Matthias, Berchtold, André
• Format: Journal Article
• Language: English
• Published: Switzerland Springer Nature B.V 01.04.2025; Springer Netherlands
• Subjects: Algorithms; Life; Life course; Missing data; Multiple imputation; Normal distribution; Simulation; Social sciences; Time series; Fully conditional specification; Missing data; Imputation; Multiple imputation for categorical time series; Life course data
• ISSN: 0033-5177; 1573-7845
• DOI: 10.1007/s11135-024-02028-z

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.