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Showing 271 to 285 of 315 results Save | Export
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Sivo, Stephen; Fan, Xitao; Witta, Lea – Structural Equation Modeling: A Multidisciplinary Journal, 2005
The purpose of this study was to evaluate the robustness of estimated growth curve models when there is stationary autocorrelation among manifest variable errors. The results suggest that when, in practice, growth curve models are fitted to longitudinal data, alternative rival hypotheses to consider would include growth models that also specify…
Descriptors: Structural Equation Models, Interaction, Correlation, Test Bias
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Raykov, Tenko; du Toit, Stephen H. C. – Structural Equation Modeling: A Multidisciplinary Journal, 2005
A method for estimation of reliability for multiple-component measuring instruments with clustered data is outlined. The approach is applicable with hierarchical designs where individuals are nested within higher order units and exhibit possibly related performance on components of a scale of interest. The procedure is developed within the…
Descriptors: Structural Equation Models, Computation, Measurement Techniques, Test Reliability
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Hershberger, Scott L. – Structural Equation Modeling: A Multidisciplinary Journal, 2003
This study examines the growth and development of structural equation modeling (SEM) from the years 1994 to 2001. The synchronous development and growth of the Structural Equation Modeling journal was also examined. Abstracts located on PsycINFO were used as the primary source of data. The major results of this investigation were clear: (a) The…
Descriptors: Primary Sources, Journal Articles, Structural Equation Models, Periodicals
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Hancock, Gregory R.; Choi, Jaehwa – Structural Equation Modeling: A Multidisciplinary Journal, 2006
In its most basic form, latent growth modeling (latent curve analysis) allows an assessment of individuals' change in a measured variable X over time. For simple linear models, as with other growth models, parameter estimates associated with the a construct (amount of X at a chosen temporal reference point) and b construct (growth in X per unit…
Descriptors: Structural Equation Models, Item Response Theory, Statistical Analysis, Research Methodology
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Beretvas, S. Natasha; Furlow, Carolyn F. – Structural Equation Modeling: A Multidisciplinary Journal, 2006
Meta-analytic structural equation modeling (MA-SEM) is increasingly being used to assess model-fit for variables' interrelations synthesized across studies. MA-SEM researchers have analyzed synthesized correlation matrices using structural equation modeling (SEM) estimation that is designed for covariance matrices. This can produce incorrect…
Descriptors: Structural Equation Models, Matrices, Statistical Analysis, Synthesis
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Asparouhov, Tihomir; Muthen, Bengt – Structural Equation Modeling: A Multidisciplinary Journal, 2009
Exploratory factor analysis (EFA) is a frequently used multivariate analysis technique in statistics. Jennrich and Sampson (1966) solved a significant EFA factor loading matrix rotation problem by deriving the direct Quartimin rotation. Jennrich was also the first to develop standard errors for rotated solutions, although these have still not made…
Descriptors: Structural Equation Models, Testing, Factor Analysis, Research Methodology
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Dolan, Conor; van der Sluis, Sophie; Grasman, Raoul – Structural Equation Modeling: A Multidisciplinary Journal, 2005
We consider power calculation in structural equation modeling with data missing completely at random (MCAR). Muth?n and Muth?n (2002) recently demonstrated how power calculations with data MCAR can be carried out by means of a Monte Carlo study. Here we show that the method of Satorra and Saris (1985), which is based on the nonnull distribution of…
Descriptors: Computation, Monte Carlo Methods, Structural Equation Models, Statistical Analysis
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van der Sluis, Sophie; Dolan, Conor V.; Stoel, Reinoud D. – Structural Equation Modeling: A Multidisciplinary Journal, 2005
This article is concerned with the seemingly simple problem of testing whether latent factors are perfectly correlated (i.e., statistically indistinct). In recent literature, researchers have used different approaches, which are not always correct or complete. We discuss the parameter constraints required to obtain such perfectly correlated latent…
Descriptors: Testing, Factor Structure, Structural Equation Models, Correlation
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Ferrando, Pere J.; Condon, Lorena – Structural Equation Modeling: A Multidisciplinary Journal, 2006
This article proposes procedures for assessing acquiescence in a balanced set of binary personality items. These procedures are based on the bidimensional item-factor analysis model, which is an alternative parameterization of the bidimensional 2-parameter normal-ogive item response theory model. First the rationale and general approach are…
Descriptors: Factor Analysis, Item Response Theory, Personality Measures, Models
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French, Brian F.; Finch, W. Holmes – Structural Equation Modeling: A Multidisciplinary Journal, 2006
Confirmatory factor analytic (CFA) procedures can be used to provide evidence of measurement invariance. However, empirical evaluation has not focused on the accuracy of common CFA steps used to detect a lack of invariance across groups. This investigation examined procedures for detection of test structure differences across groups under several…
Descriptors: Factor Analysis, Structural Equation Models, Evaluation Criteria, Error of Measurement
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Raykov, Tenko – Structural Equation Modeling: A Multidisciplinary Journal, 2006
A structural equation modeling based method is outlined that accomplishes interval estimation of individual optimal scores resulting from multiple-component measuring instruments evaluating single underlying latent dimensions. The procedure capitalizes on the linear combination of a prespecified set of measures that is associated with maximal…
Descriptors: Scores, Structural Equation Models, Reliability, Validity
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Herzog, Walter; Boomsma, Anne; Reinecke, Sven – Structural Equation Modeling: A Multidisciplinary Journal, 2007
According to Kenny and McCoach (2003), chi-square tests of structural equation models produce inflated Type I error rates when the degrees of freedom increase. So far, the amount of this bias in large models has not been quantified. In a Monte Carlo study of confirmatory factor models with a range of 48 to 960 degrees of freedom it was found that…
Descriptors: Monte Carlo Methods, Structural Equation Models, Effect Size, Maximum Likelihood Statistics
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Sass, Daniel A.; Smith, Philip L. – Structural Equation Modeling: A Multidisciplinary Journal, 2006
Structural equation modeling allows several methods of estimating the disattenuated association between 2 or more latent variables (i.e., the measurement model). In one common approach, measurement models are specified using item parcels as indicators of latent constructs. Item parcels versus original items are often used as indicators in these…
Descriptors: Structural Equation Models, Item Analysis, Error of Measurement, Measures (Individuals)
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Byrne, Barbara M.; Stewart, Sunita M. – Structural Equation Modeling: A Multidisciplinary Journal, 2006
The overarching intent of this article is to exemplify strategies associated with tests for measurement invariance that are uncommonly applied and reported in the extant literature. Designed within a pedagogical framework, the primary purposes are 3-fold and illustrate (a) tests for measurement invariance based on the analysis of means and…
Descriptors: Factor Structure, Item Response Theory, Testing, Statistical Analysis
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Beauducel, Andre; Wittmann, Werner W. – Structural Equation Modeling: A Multidisciplinary Journal, 2005
Fit indexes were compared with respect to a specific type of model misspecification. Simple structure was violated with some secondary loadings that were present in the true models that were not specified in the estimated models. The c2 test, Comparative Fit Index, Goodness-of-Fit Index, Incremental Fit Index, Nonnormed Fit Index, root mean…
Descriptors: Comparative Analysis, Personality Traits, Simulation, Goodness of Fit
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