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Pere J. Ferrando; Ana Hernández-Dorado; Urbano Lorenzo-Seva – Structural Equation Modeling: A Multidisciplinary Journal, 2024
A frequent criticism of exploratory factor analysis (EFA) is that it does not allow correlated residuals to be modelled, while they can be routinely specified in the confirmatory (CFA) model. In this article, we propose an EFA approach in which both the common factor solution and the residual matrix are unrestricted (i.e., the correlated residuals…
Descriptors: Correlation, Factor Analysis, Models, Goodness of Fit
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Bryant, Fred B.; Satorra, Albert – Structural Equation Modeling: A Multidisciplinary Journal, 2012
We highlight critical conceptual and statistical issues and how to resolve them in conducting Satorra-Bentler (SB) scaled difference chi-square tests. Concerning the original (Satorra & Bentler, 2001) and new (Satorra & Bentler, 2010) scaled difference tests, a fundamental difference exists in how to compute properly a model's scaling correction…
Descriptors: Statistical Analysis, Structural Equation Models, Goodness of Fit, Least Squares Statistics
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Yang-Wallentin, Fan; Joreskog, Karl G.; Luo, Hao – Structural Equation Modeling: A Multidisciplinary Journal, 2010
Ordinal variables are common in many empirical investigations in the social and behavioral sciences. Researchers often apply the maximum likelihood method to fit structural equation models to ordinal data. This assumes that the observed measures have normal distributions, which is not the case when the variables are ordinal. A better approach is…
Descriptors: Structural Equation Models, Factor Analysis, Least Squares Statistics, Computation
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Chen, Fang Fang – Structural Equation Modeling: A Multidisciplinary Journal, 2007
Two Monte Carlo studies were conducted to examine the sensitivity of goodness of fit indexes to lack of measurement invariance at 3 commonly tested levels: factor loadings, intercepts, and residual variances. Standardized root mean square residual (SRMR) appears to be more sensitive to lack of invariance in factor loadings than in intercepts or…
Descriptors: Geometric Concepts, Sample Size, Monte Carlo Methods, Goodness of Fit
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Lei, Ming; Lomax, Richard G. – Structural Equation Modeling: A Multidisciplinary Journal, 2005
This simulation study investigated the robustness of structural equation modeling to different degrees of nonnormality under 2 estimation methods, generalized least squares and maximum likelihood, and 4 sample sizes, 100, 250, 500, and 1,000. Each of the slight and severe nonnormality degrees was comprised of pure skewness, pure kurtosis, and both…
Descriptors: Structural Equation Models, Simulation, Sample Size, Least Squares Statistics
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Ximenez, Carmen – Structural Equation Modeling: A Multidisciplinary Journal, 2006
The recovery of weak factors has been extensively studied in the context of exploratory factor analysis. This article presents the results of a Monte Carlo simulation study of recovery of weak factor loadings in confirmatory factor analysis under conditions of estimation method (maximum likelihood vs. unweighted least squares), sample size,…
Descriptors: Monte Carlo Methods, Factor Analysis, Least Squares Statistics, Sample Size