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政大典藏 > College of Science > Department of Psychology > Periodical Articles > Item 140.119/113035

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Title:	The Impact of Ignoring the Level of Nesting Structure in Nonparametric Multilevel Latent Class Models
Authors:	游琇婷 Park, Jungkyu Yu, Hsiu Ting
Contributors:	心理系
Date:	2016-10
Issue Date:	2017-09-15 15:19:20 (UTC+8)
Abstract:	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.
Relation:	Educational and Psychological Measurement, 76(5), 824-847
Data Type:	article
DOI link:	http://dx.doi.org/10.1177/0013164415618240
DOI:	10.1177/0013164415618240
Appears in Collections:	[Department of Psychology] Periodical Articles

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