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ERIC Number: EJ1113846
Record Type: Journal
Publication Date: 2016-Oct
Pages: 24
Abstractor: As Provided
ISSN: ISSN-0013-1644
The Impact of Ignoring the Level of Nesting Structure in Nonparametric Multilevel Latent Class Models
Park, Jungkyu; Yu, Hsiu-Ting
Educational and Psychological Measurement, v76 n5 p824-847 Oct 2016
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.
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A