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https://ah.lib.nccu.edu.tw/handle/140.119/120372
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: | Mar-2018 | 上傳時間: | 5-Oct-2018 | 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: | https://doi.org/10.1080/10705511.2018.1448711 |
Appears in Collections: | 期刊論文 |
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