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Wainer, Howard – Journal of Educational and Behavioral Statistics, 2011
This article presents an interview with Karl Gustav Joreskog. Karl Gustav Joreskog was born in Amal, Sweden, on April 25, 1935. He did his undergraduate studies at Uppsala University from 1955 to 1957, with a major in mathematics and physics. He received a PhD in statistics at Uppsala University in 1963, and he was a research statistician at…
Descriptors: Statistics, Structural Equation Models, Computer Software, Factor Analysis
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Lee, Taehun; Cai, Li – Journal of Educational and Behavioral Statistics, 2012
Model-based multiple imputation has become an indispensable method in the educational and behavioral sciences. Mean and covariance structure models are often fitted to multiply imputed data sets. However, the presence of multiple random imputations complicates model fit testing, which is an important aspect of mean and covariance structure…
Descriptors: Statistical Inference, Structural Equation Models, Goodness of Fit, Statistical Analysis
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Shin, Yongyun – Journal of Educational and Behavioral Statistics, 2012
Does reduced class size cause higher academic achievement for both Black and other students in reading, mathematics, listening, and word recognition skills? Do Black students benefit more than other students from reduced class size? Does the magnitude of the minority advantages vary significantly across schools? This article addresses the causal…
Descriptors: African American Students, Class Size, Recognition (Achievement), Causal Models
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von Davier, Alina A. – Journal of Educational and Behavioral Statistics, 2008
The two most common observed-score equating functions are the linear and equipercentile functions. These are often seen as different methods, but von Davier, Holland, and Thayer showed that any equipercentile equating function can be decomposed into linear and nonlinear parts. They emphasized the dominant role of the linear part of the nonlinear…
Descriptors: Equated Scores, Causal Models, Structural Equation Models, Data Collection
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Ferron, John M.; Hess, Melinda R. – Journal of Educational and Behavioral Statistics, 2007
A concrete example is used to illustrate maximum likelihood estimation of a structural equation model with two unknown parameters. The fitting function is found for the example, as are the vector of first-order partial derivatives, the matrix of second-order partial derivatives, and the estimates obtained from each iteration of the Newton-Raphson…
Descriptors: Structural Equation Models, Computation, Statistics, Visual Aids
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Bauer, Daniel J. – Journal of Educational and Behavioral Statistics, 2003
Multilevel linear models (MLMs) provide a powerful framework for analyzing data collected at nested or non-nested levels, such as students within classrooms. The current article draws on recent analytical and software advances to demonstrate that a broad class of MLMs may be estimated as structural equation models (SEMs). Moreover, within the SEM…
Descriptors: Structural Equation Models, Data Analysis, Computer Software, Evaluation Methods