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Li, Jian; Lomax, Richard G. – Journal of Experimental Education, 2017
Using Monte Carlo simulations, this research examined the performance of four missing data methods in SEM under different multivariate distributional conditions. The effects of four independent variables (sample size, missing proportion, distribution shape, and factor loading magnitude) were investigated on six outcome variables: convergence rate,…
Descriptors: Monte Carlo Methods, Structural Equation Models, Evaluation Methods, Measurement Techniques
Nimon, Kim; Henson, Robin K. – Journal of Experimental Education, 2015
The authors empirically examined whether the validity of a residualized dependent variable after covariance adjustment is comparable to that of the original variable of interest. When variance of a dependent variable is removed as a result of one or more covariates, the residual variance may not reflect the same meaning. Using the pretest-posttest…
Descriptors: Statistical Analysis, Construct Validity, Pretesting, Pretests Posttests
Whittaker, Tiffany A. – Journal of Experimental Education, 2012
Model modification is oftentimes conducted after discovering a badly fitting structural equation model. During the modification process, the modification index (MI) and the standardized expected parameter change (SEPC) are 2 statistics that may be used to aid in the selection of parameters to add to a model to improve the fit. The purpose of this…
Descriptors: Structural Equation Models, Goodness of Fit, Sample Size, Statistical Analysis
Sun, Shaojing; Konold, Timothy R.; Fan, Xitao – Journal of Experimental Education, 2011
Interest in testing interaction terms within the latent variable modeling framework has been on the rise in recent years. However, little is known about the influence of nonnormality and model misspecification on such models that involve latent variable interactions. The authors used Mattson's data generation method to control for latent variable…
Descriptors: Structural Equation Models, Interaction, Sample Size, Computation
Klassen, Robert M.; Aldhafri, Said; Mansfield, Caroline F.; Purwanto, Edy; Siu, Angela F. Y.; Wong, Marina W.; Woods-McConney, Amanda – Journal of Experimental Education, 2012
This study explored the validity of the Utrecht Work Engagement Scale in a sample of 853 practicing teachers from Australia, Canada, China (Hong Kong), Indonesia, and Oman. The authors used multigroup confirmatory factor analysis to test the factor structure and measurement invariance across settings, after which they examined the relationships…
Descriptors: Job Satisfaction, Factor Structure, Measures (Individuals), Factor Analysis

Markus, Keith A. – Journal of Experimental Education, 1998
Using a fictional dialog between Tweedledee and Tweedledum (L. Carroll, 1856), the work of H. W. Marsh and K.-T. Hau (1996) on parsimony is interpreted in several ways. Definitions of "judgment" and "rules" are presented and argued before the author concludes that a main thesis of the Marsh article is that judgment is essential…
Descriptors: Goodness of Fit, Standards, Structural Equation Models
Sivo, Stephen A.; Xitao, Fan; Witta, E. Lea; Willse, John T. – Journal of Experimental Education, 2006
This study is a partial replication of L. Hu and P. M. Bentler's (1999) fit criteria work. The purpose of this study was twofold: (a) to determine whether cut-off values vary according to which model is the true population model for a dataset and (b) to identify which of 13 fit indexes behave optimally by retaining all of the correct models while…
Descriptors: Structural Equation Models, Goodness of Fit, Criteria, Sample Size

Mulaik, Stanley A. – Journal of Experimental Education, 1998
Argues that H. W. Marsh and K.-T. Hau (1996) misunderstood parsimony and its role in testing a hypothesis about an incompletely specified model to establish its objective validity. More parsimonious models represent more complete hypotheses having more ways of being tested and confirmed. Marsh and Hau could also have used more parsimonious…
Descriptors: Goodness of Fit, Hypothesis Testing, Statistical Studies, Structural Equation Models

Sivo, Stephen A.; Willson, Victor L. – Journal of Experimental Education, 1998
Critiques H. W. Marsh and K.-T. Hau's (1996) assertion that parsimony is not always desirable when assessing model-fit on a particular counterexample drawn from Marsh's previous research. This counterexample is neither general nor valid enough to support such a thesis and it signals an oversight of extant, stochastic models justifying correlated…
Descriptors: Correlation, Error of Measurement, Goodness of Fit, Statistical Studies

Hoyle, Rick H. – Journal of Experimental Education, 1998
In response to H. W. Marsh and K.-T. Hau's (1996) article on the potential for inferential errors when parsimony is rewarded in the evaluation of overall fit of structural equation models, a design-sensitive adjustment to the standard parsimony ratio is proposed. This ratio renders a more reasonable upper bound than does the standard parsimony…
Descriptors: Correlation, Error of Measurement, Goodness of Fit, Statistical Studies

Nevitt, Jonathan; Hancock, Gregory R. – Journal of Experimental Education, 2000
Studied incorporating adjusted model fit information into the root mean square error of approximation fit index (RMSEA). Monte Carlo simulation results show that incorporating robust information into the RMSEA may yield improved performance for assessing model fit under nonnormal data situations. (SLD)
Descriptors: Error of Measurement, Goodness of Fit, Monte Carlo Methods, Structural Equation Models