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    政大機構典藏 > 理學院 > 心理學系 > 期刊論文 >  Item 140.119/120372
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/120372

    Title: A Comparison of Approaches for Estimating Covariate Effects in Nonparametric Multilevel Latent Class Models
    Authors: Park, Jungkyu
    Yu, Hsiu-Ting
    Contributors: 心理系
    Keywords: covariate effects;latent class models;multilevel modeling
    Date: 2018-03
    Issue Date: 2018-10-05 16:29:07 (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, Volume 25, Issue 5, 778-790
    Data Type: article
    DOI 連結: https://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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