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題名 Simultaneous decision on the number of latent clusters and classes for multilevel latent class Models
作者 游琇婷
Yu, Hsiu-Ting;Park, Jungkyu
貢獻者 心理系
日期 2014.05
上傳時間 11-Jul-2016 17:21:39 (UTC+8)
摘要 The Multilevel Latent Class Model (MLCM) proposed by Vermunt (2003) has been shown to be an excellent framework for analyzing nested data with assumed discrete latent constructs. The nonparametric version of MLCM assumes 2 levels of discrete latent components to describe the dependency observed in data. Model selection is an important step in any statistical modeling. The task of model selection for MLCM amounts to the decision on the number of discrete latent components at both higher and lower levels and is more challenging than standard Latent Class Models. In this article, simulation studies were conducted to systematically examine the effects of sample sizes, clusters/classes distinctness, and the number of latent clusters and classes on the performance of various information criteria in recovering the true latent structure. Results of the simulation studies are summarized and presented. The final section presents the remarks and recommendations about the simultaneous decision regarding the number of latent classes and clusters when applying MLCMs to analyze empirical data.
關聯 Multivariate Behavioral Research, 49(3), 232-244
資料類型 article
DOI http://dx.doi.org/10.1080/00273171.2014.900431
dc.contributor 心理系
dc.creator (作者) 游琇婷zh_TW
dc.creator (作者) Yu, Hsiu-Ting;Park, Jungkyu
dc.date (日期) 2014.05
dc.date.accessioned 11-Jul-2016 17:21:39 (UTC+8)-
dc.date.available 11-Jul-2016 17:21:39 (UTC+8)-
dc.date.issued (上傳時間) 11-Jul-2016 17:21:39 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/98868-
dc.description.abstract (摘要) The Multilevel Latent Class Model (MLCM) proposed by Vermunt (2003) has been shown to be an excellent framework for analyzing nested data with assumed discrete latent constructs. The nonparametric version of MLCM assumes 2 levels of discrete latent components to describe the dependency observed in data. Model selection is an important step in any statistical modeling. The task of model selection for MLCM amounts to the decision on the number of discrete latent components at both higher and lower levels and is more challenging than standard Latent Class Models. In this article, simulation studies were conducted to systematically examine the effects of sample sizes, clusters/classes distinctness, and the number of latent clusters and classes on the performance of various information criteria in recovering the true latent structure. Results of the simulation studies are summarized and presented. The final section presents the remarks and recommendations about the simultaneous decision regarding the number of latent classes and clusters when applying MLCMs to analyze empirical data.
dc.format.extent 593386 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) Multivariate Behavioral Research, 49(3), 232-244
dc.title (題名) Simultaneous decision on the number of latent clusters and classes for multilevel latent class Models
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1080/00273171.2014.900431
dc.doi.uri (DOI) http://dx.doi.org/10.1080/00273171.2014.900431