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Ting Dai; Yang Du; Jennifer Cromley; Tia Fechter; Frank Nelson – Journal of Experimental Education, 2024
Simple matrix sampling planned missing (SMS PD) design, introduce missing data patterns that lead to covariances between variables that are not jointly observed, and create difficulties for analyses other than mean and variance estimations. Based on prior research, we adopted a new multigroup confirmatory factor analysis (CFA) approach to handle…
Descriptors: Research Problems, Research Design, Data, Matrices
Ning Jiang – ProQuest LLC, 2022
The purpose of this study is to evaluate the performance of three commonly used model fit indices when measurement invariance is tested in the context of multiple-group CFA analysis with categorical-ordered data. As applied researchers are increasingly aware of the importance of testing measurement invariance, as well as Likert-type scales are…
Descriptors: Goodness of Fit, Factor Analysis, Data, Monte Carlo Methods
Xiaotong Yang – ProQuest LLC, 2020
Many popular global model-data fit indices (GFIs), such as Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residuals (SRMSR) are proposed and widely used in the context of structural equation modeling (SEM) with continuous data. The proposed cutoffs of those…
Descriptors: Item Response Theory, Goodness of Fit, Data, Indexes
DiStefano, Christine; McDaniel, Heather L.; Zhang, Liyun; Shi, Dexin; Jiang, Zhehan – Educational and Psychological Measurement, 2019
A simulation study was conducted to investigate the model size effect when confirmatory factor analysis (CFA) models include many ordinal items. CFA models including between 15 and 120 ordinal items were analyzed with mean- and variance-adjusted weighted least squares to determine how varying sample size, number of ordered categories, and…
Descriptors: Factor Analysis, Effect Size, Data, Sample Size
Xi, Nuo; Browne, Michael W. – Journal of Educational and Behavioral Statistics, 2014
A promising "underlying bivariate normal" approach was proposed by Jöreskog and Moustaki for use in the factor analysis of ordinal data. This was a limited information approach that involved the maximization of a composite likelihood function. Its advantage over full-information maximum likelihood was that very much less computation was…
Descriptors: Factor Analysis, Maximum Likelihood Statistics, Data, Computation
Maydeu-Olivares, Alberto; Brown, Anna – Multivariate Behavioral Research, 2010
The comparative format used in ranking and paired comparisons tasks can significantly reduce the impact of uniform response biases typically associated with rating scales. Thurstone's (1927, 1931) model provides a powerful framework for modeling comparative data such as paired comparisons and rankings. Although Thurstonian models are generally…
Descriptors: Item Response Theory, Rating Scales, Models, Comparative Analysis