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Adrian Quintero; Emmanuel Lesaffre; Geert Verbeke – Journal of Educational and Behavioral Statistics, 2024
Bayesian methods to infer model dimensionality in factor analysis generally assume a lower triangular structure for the factor loadings matrix. Consequently, the ordering of the outcomes influences the results. Therefore, we propose a method to infer model dimensionality without imposing any prior restriction on the loadings matrix. Our approach…
Descriptors: Bayesian Statistics, Factor Analysis, Factor Structure, Sampling
Fujimoto, Ken A.; Neugebauer, Sabina R. – Educational and Psychological Measurement, 2020
Although item response theory (IRT) models such as the bifactor, two-tier, and between-item-dimensionality IRT models have been devised to confirm complex dimensional structures in educational and psychological data, they can be challenging to use in practice. The reason is that these models are multidimensional IRT (MIRT) models and thus are…
Descriptors: Bayesian Statistics, Item Response Theory, Sample Size, Factor Structure
Parsley, Kathryn M.; Daigle, Bernie J.; Sabel, Jaime L. – CBE - Life Sciences Education, 2022
Plant awareness disparity (PAD, formerly plant blindness) is the idea that students tend not to notice or appreciate the plants in their environment. This phenomenon often leads to naïve points of view, such as plants are not important or do not do anything for humans. There are four components of PAD: attitude (not liking plants), attention (not…
Descriptors: Test Construction, Test Validity, Test Reliability, Plants (Botany)
Stapleton, Laura M.; McNeish, Daniel M.; Yang, Ji Seung – Educational Psychologist, 2016
Multilevel models are often used to evaluate hypotheses about relations among constructs when data are nested within clusters (Raudenbush & Bryk, 2002), although alternative approaches are available when analyzing nested data (Binder & Roberts, 2003; Sterba, 2009). The overarching goal of this article is to suggest when it is appropriate…
Descriptors: Hierarchical Linear Modeling, Data Analysis, Statistical Data, Multivariate Analysis