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題名 The Impact of Ignoring the Level of Nesting Structure in Nonparametric Multilevel Latent Class Models
作者 游琇婷
Park, Jungkyu
Yu, Hsiu Ting
貢獻者 心理系
日期 2016-10
上傳時間 15-九月-2017 15:19:20 (UTC+8)
摘要 The multilevel latent class model (MLCM) is a multilevel extension of a latent class model (LCM) that is used to analyze nested structure data structure. The nonparametric version of an MLCM assumes a discrete latent variable at a higher-level nesting structure to account for the dependency among observations nested within a higher-level unit. In the present study, a simulation study was conducted to investigate the impact of ignoring the higher-level nesting structure. Three criteria—the model selection accuracy, the classification quality, and the parameter estimation accuracy—were used to evaluate the impact of ignoring the nested data structure. The results of the simulation study showed that ignoring higher-level nesting structure in an MLCM resulted in the poor performance of the Bayesian information criterion to recover the true latent structure, the inaccurate classification of individuals into latent classes, and the inflation of standard errors for parameter estimates, while the parameter estimates were not biased. This article concludes with remarks on ignoring the nested structure in nonparametric MLCMs, as well as recommendations for applied researchers when LCM is used for data collected from a multilevel nested structure.
關聯 Educational and Psychological Measurement, 76(5), 824-847
資料類型 article
DOI http://dx.doi.org/10.1177/0013164415618240
dc.contributor 心理系zh_TW
dc.creator (作者) 游琇婷zh_TW
dc.creator (作者) Park, Jungkyuen_US
dc.creator (作者) Yu, Hsiu Tingen_US
dc.date (日期) 2016-10
dc.date.accessioned 15-九月-2017 15:19:20 (UTC+8)-
dc.date.available 15-九月-2017 15:19:20 (UTC+8)-
dc.date.issued (上傳時間) 15-九月-2017 15:19:20 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/113035-
dc.description.abstract (摘要) The multilevel latent class model (MLCM) is a multilevel extension of a latent class model (LCM) that is used to analyze nested structure data structure. The nonparametric version of an MLCM assumes a discrete latent variable at a higher-level nesting structure to account for the dependency among observations nested within a higher-level unit. In the present study, a simulation study was conducted to investigate the impact of ignoring the higher-level nesting structure. Three criteria—the model selection accuracy, the classification quality, and the parameter estimation accuracy—were used to evaluate the impact of ignoring the nested data structure. The results of the simulation study showed that ignoring higher-level nesting structure in an MLCM resulted in the poor performance of the Bayesian information criterion to recover the true latent structure, the inaccurate classification of individuals into latent classes, and the inflation of standard errors for parameter estimates, while the parameter estimates were not biased. This article concludes with remarks on ignoring the nested structure in nonparametric MLCMs, as well as recommendations for applied researchers when LCM is used for data collected from a multilevel nested structure.en_US
dc.format.extent 354609 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) Educational and Psychological Measurement, 76(5), 824-847en_US
dc.title (題名) The Impact of Ignoring the Level of Nesting Structure in Nonparametric Multilevel Latent Class Modelsen_US
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1177/0013164415618240
dc.doi.uri (DOI) http://dx.doi.org/10.1177/0013164415618240