政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/121420

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題名:	A Comparison of Approaches for Estimating Covariate Effects in Nonparametric Multilevel Latent Class Models
作者:	Park, Jungkyu 游琇婷 Yu, Hsiu-Ting
貢獻者:	心理系
關鍵詞:	covariate effects;latent class models;multilevel modeling
日期:	2018-08
上傳時間:	2018-12-18 15:57:09 (UTC+8)
摘要:	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.
關聯:	Structural Equation Modeling: A Multidisciplinary Journal, Vol.25, No.5, pp.778-790
資料類型:	article
DOI 連結:	http://dx.doi.org/10.1080/10705511.2018.1448711
DOI:	10.1080/10705511.2018.1448711
顯示於類別:	[心理學系] 期刊論文

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