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Raykov, Tenko; Marcoulides, George A. – Structural Equation Modeling, 2000
Outlines a method for comparing completely standardized solutions in multiple groups. The method is based on a correlation structure analysis of equal-size samples and uses the correlation distribution theory implemented in the structural equation modeling program RAMONA. (SLD)
Descriptors: Comparative Analysis, Correlation, Sample Size, Structural Equation Models
Peer reviewed Peer reviewed
Rigdon, Edward E. – Structural Equation Modeling, 1998
An alternative baseline model for comparative fit assessment of structural equation models is described, evaluated, and compared to the standard "null" baseline model. The new "equal correlation" model constrains all variables to have equal, rather than zero, correlations, but all variances are free. Advantages and limitations…
Descriptors: Comparative Analysis, Correlation, Goodness of Fit, Structural Equation Models
Peer reviewed Peer reviewed
Marsh, Herbert W. – Structural Equation Modeling, 1998
Discusses concerns with the model proposed by E. Rigdon for computing incremental fit indices in which all measured variables are equally correlated (as opposed to the traditional null model). Proposes retaining the traditional null model with emphasis on the comparative fit of alternative models within a nested sequence that could include the new…
Descriptors: Comparative Analysis, Correlation, Goodness of Fit, Structural Equation Models
Peer reviewed Peer reviewed
Rigdon, Edward E. – Structural Equation Modeling, 1998
Continuing a discussion of the topic of fit assessment in structural equation modeling, this article accepts the compromise proposed by H. Marsh (1998) and offers tentative heuristic models for interpreting fit indices that involve the new baseline model proposed by E. Rigdon (1998). (SLD)
Descriptors: Comparative Analysis, Correlation, Goodness of Fit, Heuristics
Peer reviewed Peer reviewed
Rigdon, Edward E. – Structural Equation Modeling, 1999
Explores the use of the Friedman method of ranks (H. Friedman, 1937) as an inferential procedure for evaluating competing models in structural-equation modeling. Describes the attractive features of this approach, but raises important issues regarding the lack of independence of observations and the power of the test. (SLD)
Descriptors: Comparative Analysis, Nonparametric Statistics, Power (Statistics), Selection
Peer reviewed Peer reviewed
Duncan, Terry E.; Duncan, Susan C.; Li, Fuzhong – Structural Equation Modeling, 1998
Presents an application of latent growth curve methodology to the analysis of longitudinal developmental change in alcohol consumption of 586 young adults, illustrating three approaches to the analysis of missing data: (1) multiple-sample structural equation modeling procedures; (2) raw maximum likelihood analyses; and (3) multiple modeling and…
Descriptors: Algorithms, Change, Comparative Analysis, Drinking
Peer reviewed Peer reviewed
Bandalos, Deborah L. – Structural Equation Modeling, 1997
Monte Carlo methods were used to study the accuracy and utility of estimators of overall error and error due to approximation in structural equation modeling. Effects of sample size, indicator reliabilities, and degree of misspecification were examined. The rescaled noncentrality parameter also was examined. Choosing among competing models is…
Descriptors: Comparative Analysis, Error of Measurement, Estimation (Mathematics), Monte Carlo Methods
Peer reviewed Peer reviewed
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