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Chunhua Cao; Yan Wang; Eunsook Kim – Structural Equation Modeling: A Multidisciplinary Journal, 2025
Multilevel factor mixture modeling (FMM) is a hybrid of multilevel confirmatory factor analysis (CFA) and multilevel latent class analysis (LCA). It allows researchers to examine population heterogeneity at the within level, between level, or both levels. This tutorial focuses on explicating the model specification of multilevel FMM that considers…
Descriptors: Hierarchical Linear Modeling, Factor Analysis, Nonparametric Statistics, Statistical Analysis
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Svetina, Dubravka; Levy, Roy – Educational Assessment, 2014
A framework is introduced for considering dimensionality assessment procedures for multidimensional item response models. The framework characterizes procedures in terms of their confirmatory or exploratory approach, parametric or nonparametric assumptions, and applicability to dichotomous, polytomous, and missing data. Popular and emerging…
Descriptors: Item Response Theory, Models, National Competency Tests, Science Tests
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Ravand, Hamdollah; Baghaei, Purya – Practical Assessment, Research & Evaluation, 2016
Structural equation modeling (SEM) has become widespread in educational and psychological research. Its flexibility in addressing complex theoretical models and the proper treatment of measurement error has made it the model of choice for many researchers in the social sciences. Nevertheless, the model imposes some daunting assumptions and…
Descriptors: Least Squares Statistics, Structural Equation Models, Nonparametric Statistics, Sample Size
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Koris, Riina; Nokelainen, Petri – International Journal of Educational Management, 2015
Purpose: The purpose of this paper is to study Bayesian dependency modelling (BDM) to validate the model of educational experiences and the student-customer orientation questionnaire (SCOQ), and to identify the categories of educatonal experience in which students expect a higher educational institutions (HEI) to be student-customer oriented.…
Descriptors: College Students, Questionnaires, Bayesian Statistics, Educational Experience
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Tchumtchoua, Sylvie; Dey, Dipak K. – Psychometrika, 2012
This paper proposes a semiparametric Bayesian framework for the analysis of associations among multivariate longitudinal categorical variables in high-dimensional data settings. This type of data is frequent, especially in the social and behavioral sciences. A semiparametric hierarchical factor analysis model is developed in which the…
Descriptors: Factor Analysis, Bayesian Statistics, Behavioral Sciences, Social Sciences
Reynolds, Thomas J. – 1976
A method of factor extraction specific to a binary matrix, illustrated here as a person-by-item response matrix, is presented. The extraction procedure, termed ERGO, differs from the more commonly implemented dimensionalizing techniques, factor analysis and multidimensional scaling, by taking into consideration item difficulty. Utilized in the…
Descriptors: Discriminant Analysis, Factor Analysis, Item Analysis, Matrices