學術產出-期刊論文

文章檢視/開啟

書目匯出

Google ScholarTM

政大圖書館

引文資訊

TAIR相關學術產出

題名 Recommendations on the Sample Sizes for Multilevel Latent Class Models
作者 游琇婷
Park, Jungkyu
Yu, Hsiu-Ting
貢獻者 心理系
日期 2018-10
上傳時間 28-九月-2017 17:38:51 (UTC+8)
摘要 A multilevel latent class model (MLCM) is a useful tool for analyzing data arising from hierarchically nested structures. One important issue for MLCMs is determining the minimum sample sizes needed to obtain reliable and unbiased results. In this simulation study, the sample sizes required for MLCMs were investigated under various conditions. A series of design factors, including sample sizes at two levels, the distinctness and the complexity of the latent structure, and the number of indicators were manipulated. The results revealed that larger samples are required when the latent classes are less distinct and more complex with fewer indicators. This study also provides recommendations about the minimum required sample sizes that satisfied all four criteria—model selection accuracy, parameter estimation bias, standard error bias, and coverage rate—as well as rules of thumb for sample size requirements when applying MLCMs in data analysis.
關聯 EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT, 78(5), 737-761
資料類型 article
DOI https://doi.org/10.1177/0013164417719111
dc.contributor 心理系zh_TW
dc.creator (作者) 游琇婷zh_TW
dc.creator (作者) Park, Jungkyuen_US
dc.creator (作者) Yu, Hsiu-Tingen_US
dc.date (日期) 2018-10-
dc.date.accessioned 28-九月-2017 17:38:51 (UTC+8)-
dc.date.available 28-九月-2017 17:38:51 (UTC+8)-
dc.date.issued (上傳時間) 28-九月-2017 17:38:51 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/113132-
dc.description.abstract (摘要) A multilevel latent class model (MLCM) is a useful tool for analyzing data arising from hierarchically nested structures. One important issue for MLCMs is determining the minimum sample sizes needed to obtain reliable and unbiased results. In this simulation study, the sample sizes required for MLCMs were investigated under various conditions. A series of design factors, including sample sizes at two levels, the distinctness and the complexity of the latent structure, and the number of indicators were manipulated. The results revealed that larger samples are required when the latent classes are less distinct and more complex with fewer indicators. This study also provides recommendations about the minimum required sample sizes that satisfied all four criteria—model selection accuracy, parameter estimation bias, standard error bias, and coverage rate—as well as rules of thumb for sample size requirements when applying MLCMs in data analysis.en_US
dc.format.extent 1232112 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT, 78(5), 737-761zh_TW
dc.title (題名) Recommendations on the Sample Sizes for Multilevel Latent Class Modelszh_TW
dc.type (資料類型) article-
dc.identifier.doi (DOI) 10.1177/0013164417719111-
dc.doi.uri (DOI) https://doi.org/10.1177/0013164417719111-