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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: 2018-03
    上傳時間: 2018-10-05 16:29:07 (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, Volume 25, Issue 5, 778-790
    資料類型: article
    DOI 連結: https://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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