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Raykov, Tenko – Structural Equation Modeling, 2001
Presents a didactic collection of covariance and mean structure hypotheses that can be tested using a widely applicable and easy to use structural equation modeling approach. The method is useful when the goal is to examine the observed multivariable structure or test hypotheses regarding interrelationships in measures and when large samples are…
Descriptors: Hypothesis Testing, Structural Equation Models
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Steiger, James H. – Structural Equation Modeling, 2000
Discusses two criticisms raised by L. Hayduk and D. Glaser of the most commonly used point estimate of the Root Mean Square Error (RMSEA) and points out misconceptions in their discussion. Although there are apparent flaws in their arguments, the RMSEA is open to question for several other reasons. (SLD)
Descriptors: Error of Measurement, Estimation (Mathematics), Factor Analysis, Hypothesis Testing
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Ferrer, Emilio; Hamagami, Fumiaki; McArdle, John J. – Structural Equation Modeling, 2004
This article offers different examples of how to fit latent growth curve (LGC) models to longitudinal data using a variety of different software programs (i.e., LISREL, Mx, Mplus, AMOS, SAS). The article shows how the same model can be fitted using both structural equation modeling and multilevel software, with nearly identical results, even in…
Descriptors: Computer Software, Structural Equation Models, Longitudinal Studies, Data Analysis
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Moulder, Bradley C.; Algina, James – Structural Equation Modeling, 2002
Used simulation to compare structural equation modeling methods for estimating and testing hypotheses about an interaction between continuous variables. Findings indicate that the two-stage least squares procedure exhibited more bias and lower power than the other methods. The Jaccard-Wan procedure (J. Jaccard and C. Wan, 1995) and maximum…
Descriptors: Comparative Analysis, Estimation (Mathematics), Hypothesis Testing, Least Squares Statistics
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Marsh, Herbert W.; Hau, Kit-Tai; Wen, Zhonglin – Structural Equation Modeling, 2004
Goodness-of-fit (GOF) indexes provide "rules of thumb"?recommended cutoff values for assessing fit in structural equation modeling. Hu and Bentler (1999) proposed a more rigorous approach to evaluating decision rules based on GOF indexes and, on this basis, proposed new and more stringent cutoff values for many indexes. This article discusses…
Descriptors: Statistical Significance, Structural Equation Models, Evaluation Methods, Evaluation Research