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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
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Raykov, Tenko – Multivariate Behavioral Research, 1997
It is shown that, for equivalent structural equation models that have been extended to multigroup models, imposing cross-group equality constraints on no parameters, all parameters, or any number of parameters for which the models are identical preserves the model equivalence property. Results are illustrated with two-group cognitive intervention…
Descriptors: Cognitive Psychology, Groups, Intervention, Mathematical Models
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Kenny, David A.; McCoach, D. Betsy – Structural Equation Modeling, 2003
Used three approaches to understand the effect of the number of variables in the model on model fit in structural equation modeling through computer simulation. Developed a simple formula for the theoretical value of the comparative fit index. (SLD)
Descriptors: Computer Simulation, Goodness of Fit, Models, Structural Equation Models
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Lee, Sik-Yum; Song, Xin-Yuan – Psychometrika, 2003
Proposed a new nonlinear structural equation model with fixed covariates to deal with some complicated substantive theory and developed a Bayesian path sampling procedure for model comparison. Illustrated the approach with an illustrative example using data from an international study. (SLD)
Descriptors: Bayesian Statistics, Comparative Analysis, Sampling, Structural Equation Models
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Raykov, Tenko – Structural Equation Modeling, 1997
Structural equation modeling is used in the simultaneous study of individual and group latent change patterns on several longitudinally assessed variables. The approach, which is based on a special case of the comprehensive latent curve analysis of W. Meredith and J. Tisak (1990), is illustrated with a two-group study. (SLD)
Descriptors: Change, Groups, Individual Differences, Longitudinal Studies
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Hancock, Gregory R. – Educational and Psychological Measurement, 1997
Methods are offered for conducting hypothesis testing associated with disattenuated validity coefficients to overcome limitations of some other suggested approaches. Through using classical test theory's notion of reliability in the form of structured path models, such hypothesis testing may be done with hierarchically related structural equation…
Descriptors: Correlation, Hypothesis Testing, Reliability, Scores
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Brown, Roger L. – Structural Equation Modeling, 1997
Reviews the use of structural equation modeling for providing an overall assessment of mediation, the mechanism that accounts for the relation between the predictor and the criterion. A strategy for supplemental details is presented that measures the magnitude of mediational effects. (SLD)
Descriptors: Computer Software, Mathematical Models, Predictor Variables, Structural Equation Models
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Lubke, Gitta H.; Dolan, Connor V. – Structural Equation Modeling, 2003
Simulation results show that the power to detect small mean differences when fitting a model with free residual variances across groups decreases as the difference in R squared increases. This decrease is more pronounced in the presence of correlated errors and if group sample sizes differ. (SLD)
Descriptors: Correlation, Factor Structure, Sample Size, Simulation
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Hamaker, Ellen L.; Dolan, Conor V.; Molenaar, Peter C. M. – Structural Equation Modeling, 2002
Reexamined the nature of structural equation modeling (SEM) estimates of autoregressive moving average (ARMA) models, replicated the simulation experiments of P. Molenaar, and examined the behavior of the log-likelihood ratio test. Simulation studies indicate that estimates of ARMA parameters observed with SEM software are identical to those…
Descriptors: Maximum Likelihood Statistics, Regression (Statistics), Simulation, Structural Equation Models
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Stapleton, Laura M. – Structural Equation Modeling, 2002
Studied the use of different weighting techniques in structural equation modeling and found, through simulation, that the use of an effective sample size weight provides unbiased estimates of key parameters and their sampling variances. Also discusses use of a popular normalization technique of scaling weights. (SLD)
Descriptors: Estimation (Mathematics), Sample Size, Scaling, Simulation
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Rigdon, Edward E. – Multivariate Behavioral Research, 1995
This article presents a straightforward classification system that is a necessary and sufficient condition for identification of the structural component of structural equation models of the block-recursive type with no more than two equations per block. Limitations of other identification techniques are discussed. (SLD)
Descriptors: Classification, Equations (Mathematics), Estimation (Mathematics), Identification
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Buczynski, Patricia L. – Measurement and Evaluation in Counseling and Development, 1994
Describes two strategies for assessing measures of practical fit and the goodness of fit from structural equation models. Focuses on linear structural relations (LISREL), chi-square fit index, practical fit indices, and hierarchical analyses. Includes empirical example of hierarchical model procedures used with practical and statistical indices to…
Descriptors: Evaluation Methods, Research Methodology, Statistical Analysis, Structural Equation Models
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Markus, Keith A. – Structural Equation Modeling, 2000
Explores the four-step procedure for testing structural equation models and outlines some problems with the approach advocated by L. Hayduk and D. Glaser (2000) and S. Mulaik and R. Milsap (2000). Questions the idea that there is a "correct" number of constructs for a given phenomenon. (SLD)
Descriptors: Factor Analysis, Factor Structure, Research Methodology, Structural Equation Models
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Hennessy, Michael; Greenberg, Judith – American Journal of Evaluation, 1999
Describes the integration of programmatic theory and structural equation modeling to serve as the basic intellectual machinery for designing and evaluating behavioral interventions. Illustrates this integration with the example of a program to reduce sexual risk taking. (SLD)
Descriptors: Evaluation Methods, Intervention, Program Evaluation, Structural Equation Models
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Green, Samuel B.; Thompson, Marilyn S.; Babyak, Michael A. – Multivariate Behavioral Research, 1998
Simulated data for factor analytic models is used in the evaluation of three methods for controlling Type I errors: (1) the standard approach that involves testing each parameter at the 0.05 level; (2) the Bonferroni approach; and (3) a simultaneous test procedure (STP). Advantages offered by the Bonferroni approach are discussed. (SLD)
Descriptors: Factor Analysis, Monte Carlo Methods, Simulation, Structural Equation Models
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