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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/121420

Title:	A Comparison of Approaches for Estimating Covariate Effects in Nonparametric Multilevel Latent Class Models
Authors:	Park, Jungkyu 游琇婷 Yu, Hsiu-Ting
Contributors:	心理系
Keywords:	covariate effects;latent class models;multilevel modeling
Date:	2018-08
Issue Date:	2018-12-18 15:57:09 (UTC+8)
Abstract:	The inclusion of covariates improves the prediction of class memberships in latent class analysis (LCA). Several methods for examining covariate effects have been developed over the past decade; however, researchers have limited to the comparisons of the performance among these methods in cases of the single-level LCA. The present study investigated the performance of three different methods for examining covariate effects in a multilevel setting. We conducted a simulation to compare the performance of the three methods when level-1 and level-2 covariates were simultaneously incorporated into the nonparametric multilevel latent class model to predict latent class membership at each level. The simulation results revealed that the bias-adjusted three-step maximum likelihood method performed equally well as the one-step method when the sample sizes were sufficiently large and the latent classes were distinct from each other. However, the unadjusted three-step method significantly underestimated the level-1 covariate effect in most conditions.
Relation:	Structural Equation Modeling: A Multidisciplinary Journal, Vol.25, No.5, pp.778-790
Data Type:	article
DOI 連結:	http://dx.doi.org/10.1080/10705511.2018.1448711
DOI:	10.1080/10705511.2018.1448711
Appears in Collections:	[心理學系] 期刊論文

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