| dc.contributor | 心理系 | |
| dc.creator (作者) | Park, Jungkyu | en_US |
| dc.creator (作者) | 游琇婷 | zh_TW |
| dc.creator (作者) | Yu, Hsiu-Ting | en_US |
| dc.date (日期) | 2018-08 | |
| dc.date.accessioned | 18-Dec-2018 15:57:09 (UTC+8) | - |
| dc.date.available | 18-Dec-2018 15:57:09 (UTC+8) | - |
| dc.date.issued (上傳時間) | 18-Dec-2018 15:57:09 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/121420 | - |
| dc.description.abstract (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. | en_US |
| dc.format.extent | 573207 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | Structural Equation Modeling: A Multidisciplinary Journal, Vol.25, No.5, pp.778-790 | |
| dc.subject (關鍵詞) | covariate effects; latent class models; multilevel modeling | en_US |
| dc.title (題名) | A Comparison of Approaches for Estimating Covariate Effects in Nonparametric Multilevel Latent Class Models | en_US |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1080/10705511.2018.1448711 | |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1080/10705511.2018.1448711 | |
