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Showing 1 to 15 of 35 results Save | Export
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Sideridis, Georgios D.; Jaffari, Fathima – Measurement and Evaluation in Counseling and Development, 2022
The present study describes an R function that implements six corrective procedures developed by Bartlett, Swain, and Yuan in the correction of 21 statistics associated with the omnibus Chi-square test, the residuals, or fit indices in confirmatory factor analysis (CFA) and structural equation modeling (SEM).
Descriptors: Statistical Analysis, Goodness of Fit, Factor Analysis, Structural Equation Models
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van Laar, Saskia; Braeken, Johan – Practical Assessment, Research & Evaluation, 2021
Despite the sensitivity of fit indices to various model and data characteristics in structural equation modeling, these fit indices are used in a rigid binary fashion as a mere rule of thumb threshold value in a search for model adequacy. Here, we address the behavior and interpretation of the popular Comparative Fit Index (CFI) by stressing that…
Descriptors: Goodness of Fit, Structural Equation Models, Sampling, Sample Size
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Marcoulides, Katerina M.; Yuan, Ke-Hai – International Journal of Research & Method in Education, 2020
Multilevel structural equation models (MSEM) are typically evaluated on the basis of goodness of fit indices. A problem with these indices is that they pertain to the entire model, reflecting simultaneously the degree of fit for all levels in the model. Consequently, in cases that lack model fit, it is unclear which level model is misspecified.…
Descriptors: Goodness of Fit, Structural Equation Models, Correlation, Inferences
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Peugh, James; Feldon, David F. – CBE - Life Sciences Education, 2020
Structural equation modeling is an ideal data analytical tool for testing complex relationships among many analytical variables. It can simultaneously test multiple mediating and moderating relationships, estimate latent variables on the basis of related measures, and address practical issues such as nonnormality and missing data. To test the…
Descriptors: Structural Equation Models, Goodness of Fit, Statistical Analysis, Computation
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Walker, David A.; Smith, Thomas J. – Measurement and Evaluation in Counseling and Development, 2017
Nonnormality of data presents unique challenges for researchers who wish to carry out structural equation modeling. The subsequent SPSS syntax program computes bootstrap-adjusted fit indices (comparative fit index, Tucker-Lewis index, incremental fit index, and root mean square error of approximation) that adjust for nonnormality, along with the…
Descriptors: Robustness (Statistics), Sampling, Statistical Inference, Goodness of Fit
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Ockey, Gary J.; Choi, Ikkyu – Language Assessment Quarterly, 2015
Studies that use structural equation modeling (SEM) techniques are increasingly encountered in the language assessment literature. This popularity has created the need for a set of guidelines that can indicate what should be included in a research report and make it possible for research consumers to judge the appropriateness of the…
Descriptors: Second Language Learning, Structural Equation Models, Language Tests, Guidelines
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Lewis, Todd F. – Measurement and Evaluation in Counseling and Development, 2017
American Educational Research Association (AERA) standards stipulate that researchers show evidence of the internal structure of instruments. Confirmatory factor analysis (CFA) is one structural equation modeling procedure designed to assess construct validity of assessments that has broad applicability for counselors interested in instrument…
Descriptors: Educational Research, Factor Analysis, Structural Equation Models, Construct Validity
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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
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Levy, Roy – Structural Equation Modeling: A Multidisciplinary Journal, 2011
Bayesian approaches to modeling are receiving an increasing amount of attention in the areas of model construction and estimation in factor analysis, structural equation modeling (SEM), and related latent variable models. However, model diagnostics and model criticism remain relatively understudied aspects of Bayesian SEM. This article describes…
Descriptors: Bayesian Statistics, Structural Equation Models, Goodness of Fit, Computation
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Lee, Taehun; Cai, Li – Journal of Educational and Behavioral Statistics, 2012
Model-based multiple imputation has become an indispensable method in the educational and behavioral sciences. Mean and covariance structure models are often fitted to multiply imputed data sets. However, the presence of multiple random imputations complicates model fit testing, which is an important aspect of mean and covariance structure…
Descriptors: Statistical Inference, Structural Equation Models, Goodness of Fit, Statistical Analysis
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Raykov, Tenko; Zajacova, Anna – Structural Equation Modeling: A Multidisciplinary Journal, 2012
An interval estimation procedure for proportion of explained observed variance in latent curve analysis is discussed, which can be used as an aid in the process of choosing between linear and nonlinear models. The method allows obtaining confidence intervals for the R[squared] indexes associated with repeatedly followed measures in longitudinal…
Descriptors: Longitudinal Studies, Structural Equation Models, Computation, 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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Ryu, Ehri; West, Stephen G. – Structural Equation Modeling: A Multidisciplinary Journal, 2009
In multilevel structural equation modeling, the "standard" approach to evaluating the goodness of model fit has a potential limitation in detecting the lack of fit at the higher level. Level-specific model fit evaluation can address this limitation and is more informative in locating the source of lack of model fit. We proposed level-specific test…
Descriptors: Structural Equation Models, Evaluation Methods, Goodness of Fit, Simulation
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Saris, Willem E.; Satorra, Albert; van der Veld, William M. – Structural Equation Modeling: A Multidisciplinary Journal, 2009
Assessing the correctness of a structural equation model is essential to avoid drawing incorrect conclusions from empirical research. In the past, the chi-square test was recommended for assessing the correctness of the model but this test has been criticized because of its sensitivity to sample size. As a reaction, an abundance of fit indexes…
Descriptors: Structural Equation Models, Validity, Goodness of Fit, Evaluation Methods
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Bai, Yun; Poon, Wai-Yin – Structural Equation Modeling: A Multidisciplinary Journal, 2009
Two-level data sets are frequently encountered in social and behavioral science research. They arise when observations are drawn from a known hierarchical structure, such as when individuals are randomly drawn from groups that are randomly drawn from a target population. Although 2-level data analysis in the context of structural equation modeling…
Descriptors: Structural Equation Models, Data Analysis, Simulation, Goodness of Fit
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