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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
Heene, Moritz; Hilbert, Sven; Freudenthaler, H. Harald; Buhner, Markus – Structural Equation Modeling: A Multidisciplinary Journal, 2012
This simulation study investigated the sensitivity of commonly used cutoff values for global-model-fit indexes, with regard to different degrees of violations of the assumption of uncorrelated errors in confirmatory factor analysis. It is shown that the global-model-fit indexes fell short in identifying weak to strong model misspecifications under…
Descriptors: Structural Equation Models, Goodness of Fit, Factor Analysis, Correlation
Westfall, Peter H.; Henning, Kevin S. S.; Howell, Roy D. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
This article shows how interfactor correlation is affected by error correlations. Theoretical and practical justifications for error correlations are given, and a new equivalence class of models is presented to explain the relationship between interfactor correlation and error correlations. The class allows simple, parsimonious modeling of error…
Descriptors: Psychometrics, Correlation, Error of Measurement, Structural Equation Models
Wu, Jiun-Yu; Kwok, Oi-man – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Both ad-hoc robust sandwich standard error estimators (design-based approach) and multilevel analysis (model-based approach) are commonly used for analyzing complex survey data with nonindependent observations. Although these 2 approaches perform equally well on analyzing complex survey data with equal between- and within-level model structures…
Descriptors: Structural Equation Models, Surveys, Data Analysis, Comparative Analysis
Castro-Schilo, Laura; Widaman, Keith F.; Grimm, Kevin J. – Structural Equation Modeling: A Multidisciplinary Journal, 2013
In 1959, Campbell and Fiske introduced the use of multitrait-multimethod (MTMM) matrices in psychology, and for the past 4 decades confirmatory factor analysis (CFA) has commonly been used to analyze MTMM data. However, researchers do not always fit CFA models when MTMM data are available; when CFA modeling is used, multiple models are available…
Descriptors: Multitrait Multimethod Techniques, Factor Analysis, Structural Equation Models, Monte Carlo Methods
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
DiStefano, Christine; Motl, Robert W. – Structural Equation Modeling: A Multidisciplinary Journal, 2009
The Rosenberg Self-Esteem scale (RSE) has been widely used in examinations of sex differences in global self-esteem. However, previous examinations of sex differences have not accounted for method effects associated with item wording, which have consistently been reported by researchers using the RSE. Accordingly, this study examined the…
Descriptors: Self Esteem, Gender Differences, Measures (Individuals), Correlation
LaGrange, Beth; Cole, David A. – Structural Equation Modeling: A Multidisciplinary Journal, 2008
This article examines 4 approaches for explaining shared method variance, each applied to a longitudinal trait-state-occasion (TSO) model. Many approaches have been developed to account for shared method variance in multitrait-multimethod (MTMM) data. Some of these MTMM approaches (correlated method, orthogonal method, correlated method minus one,…
Descriptors: Structural Equation Models, Longitudinal Studies, Multitrait Multimethod Techniques, Correlation
Marsh, Herbert W.; Ludtke, Oliver; Trautwein, Ulrich; Morin, Alexandre J. S. – Structural Equation Modeling: A Multidisciplinary Journal, 2009
In this investigation, we used a classic latent profile analysis (LPA), a person-centered approach, to identify groups of students who had similar profiles for multiple dimensions of academic self-concept (ASC) and related these LPA groups to a diverse set of correlates. Consistent with a priori predictions, we identified 5 LPA groups representing…
Descriptors: Structural Equation Models, Goodness of Fit, Profiles, Prediction
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
Bai, Yun; Poon, Wai-Yin; Cheung, Gordon Wai Hung – Structural Equation Modeling: A Multidisciplinary Journal, 2006
Two-level data sets are frequently encountered in social and behavioral science research. They arise when observations are drawn from a known hierarchical structure, as when individuals are randomly drawn from groups that are randomly drawn from a target population. When the covariance structures in the group level and the individual level are the…
Descriptors: Evaluation Methods, Predictor Variables, Social Science Research, Behavioral Science Research