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Yasuhiro Yamamoto; Yasuo Miyazaki – Journal of Experimental Education, 2025
Bayesian methods have been said to solve small sample problems in frequentist methods by reflecting prior knowledge in the prior distribution. However, there are dangers in strongly reflecting prior knowledge or situations where much prior knowledge cannot be used. In order to address the issue, in this article, we considered to apply two Bayesian…
Descriptors: Sample Size, Hierarchical Linear Modeling, Bayesian Statistics, Prior Learning
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Chang, Wanchen; Pituch, Keenan A. – Journal of Experimental Education, 2019
When data for multiple outcomes are collected in a multilevel design, researchers can select a univariate or multivariate analysis to examine group-mean differences. When correlated outcomes are incomplete, a multivariate multilevel model (MVMM) may provide greater power than univariate multilevel models (MLMs). For a two-group multilevel design…
Descriptors: Hierarchical Linear Modeling, Multivariate Analysis, Research Problems, Error of Measurement
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Moeyaert, Mariola; Ugille, Maaike; Ferron, John M.; Beretvas, S. Natasha; Van den Noortgate, Wim – Journal of Experimental Education, 2014
One approach for combining single-case data involves use of multilevel modeling. In this article, the authors use a Monte Carlo simulation study to inform applied researchers under which realistic conditions the three-level model is appropriate. The authors vary the value of the immediate treatment effect and the treatment's effect on the time…
Descriptors: Hierarchical Linear Modeling, Monte Carlo Methods, Case Studies, Research Design