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Bryant, Fred B.; Satorra, Albert – Structural Equation Modeling: A Multidisciplinary Journal, 2012
We highlight critical conceptual and statistical issues and how to resolve them in conducting Satorra-Bentler (SB) scaled difference chi-square tests. Concerning the original (Satorra & Bentler, 2001) and new (Satorra & Bentler, 2010) scaled difference tests, a fundamental difference exists in how to compute properly a model's scaling correction…
Descriptors: Statistical Analysis, Structural Equation Models, Goodness of Fit, Least Squares Statistics
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Demir, Kamile – Educational Sciences: Theory and Practice, 2015
The purpose of this research is to examine the effect of the level of trust of primary school teachers towards their organization in relation to their perceptions of the school having a culture of teacher leadership. Participants of the study consisted of 378 teachers working in Burdur public primary schools. The data collection tool used two…
Descriptors: Foreign Countries, Teacher Leadership, Elementary School Teachers, Elementary Schools
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van Buuren, Stef – Psychometrika, 1997
This paper outlines how the stationary ARMA (p,q) model (G. Box and G. Jenkins, 1976) can be specified as a structural equation model. Maximum likelihood estimates for the parameters in the ARMA model can be obtained by software for fitting structural equation models. The method is applied to three problem types. (SLD)
Descriptors: Computer Software, Goodness of Fit, Maximum Likelihood Statistics, Structural Equation Models
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Fox, John – Structural Equation Modeling: A Multidisciplinary Journal, 2006
R is free, open-source, cooperatively developed software that implements the S statistical programming language and computing environment. The current capabilities of R are extensive, and it is in wide use, especially among statisticians. The sem package provides basic structural equation modeling facilities in R, including the ability to fit…
Descriptors: Structural Equation Models, Computer Software, Least Squares Statistics, Programming Languages
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Hipp, John R.; Bauer, Daniel J. – Psychological Methods, 2006
Finite mixture models are well known to have poorly behaved likelihood functions featuring singularities and multiple optima. Growth mixture models may suffer from fewer of these problems, potentially benefiting from the structure imposed on the estimated class means and covariances by the specified growth model. As demonstrated here, however,…
Descriptors: Monte Carlo Methods, Maximum Likelihood Statistics, Computation, Case Studies