{"title":"单变量分类纵向数据的归算方法比较。","authors":"Kevin Emery, Matthias Studer, André Berchtold","doi":"10.1007/s11135-024-02028-z","DOIUrl":null,"url":null,"abstract":"<p><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><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11135-024-02028-z.</p>","PeriodicalId":49649,"journal":{"name":"Quality & Quantity","volume":"59 2","pages":"1767-1791"},"PeriodicalIF":0.0000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12104099/pdf/","citationCount":"0","resultStr":"{\"title\":\"Comparison of imputation methods for univariate categorical longitudinal data.\",\"authors\":\"Kevin Emery, Matthias Studer, André Berchtold\",\"doi\":\"10.1007/s11135-024-02028-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><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><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11135-024-02028-z.</p>\",\"PeriodicalId\":49649,\"journal\":{\"name\":\"Quality & Quantity\",\"volume\":\"59 2\",\"pages\":\"1767-1791\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12104099/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Quality & Quantity\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s11135-024-02028-z\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/12/26 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"Mathematics\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quality & Quantity","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s11135-024-02028-z","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/26 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"Mathematics","Score":null,"Total":0}
Comparison of imputation methods for univariate categorical longitudinal data.
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.
Supplementary information: The online version contains supplementary material available at 10.1007/s11135-024-02028-z.
期刊介绍：
Quality and Quantity constitutes a point of reference for European and non-European scholars to discuss instruments of methodology for more rigorous scientific results in the social sciences. In the era of biggish data, the journal also provides a publication venue for data scientists who are interested in proposing a new indicator to measure the latent aspects of social, cultural, and political events. Rather than leaning towards one specific methodological school, the journal publishes papers on a mixed method of quantitative and qualitative data. Furthermore, the journal’s key aim is to tackle some methodological pluralism across research cultures. In this context, the journal is open to papers addressing some general logic of empirical research and analysis of the validity and verification of social laws. Thus The journal accepts papers on science metrics and publication ethics and, their related issues affecting methodological practices among researchers.
Quality and Quantity is an interdisciplinary journal which systematically correlates disciplines such as data and information sciences with the other humanities and social sciences. The journal extends discussion of interesting contributions in methodology to scholars worldwide, to promote the scientific development of social research.
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