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Title:	Recommendations on the Sample Sizes for Multilevel Latent Class Models
Authors:	游琇婷 Park, Jungkyu Yu, Hsiu-Ting
Contributors:	心理系
Date:	2018-10
Issue Date:	2017-09-28 17:38:51 (UTC+8)
Abstract:	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.
Relation:	EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT, 78(5), 737-761
Data Type:	article
DOI 連結:	https://doi.org/10.1177/0013164417719111
DOI:	10.1177/0013164417719111
Appears in Collections:	[心理學系] 期刊論文

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