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Gottfredson, Nisha C.; Panter, A. T.; Daye, Charles E.; Allen, Walter F.; Wightman, Linda F. – Multivariate Behavioral Research, 2009
Controversy surrounding the use of race-conscious admissions can be partially resolved with improved empirical knowledge of the effects of racial diversity in educational settings. We use a national sample of law students nested in 64 law schools to test the complex and largely untested theory regarding the effects of educational diversity on…
Descriptors: Law Students, Race, Law Schools, Structural Equation Models
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Kaplan, David – Multivariate Behavioral Research, 1999
Proposes an extension of the propensity score adjustment method to the analysis of group differences on latent variable models. Uses multiple indicators-multiple causes (MIMIC) structural equation modeling to test hypotheses about treatment group differences. Discusses the role of factorial invariance as it relates to this approach. (SLD)
Descriptors: Groups, Hypothesis Testing, Scores, Structural Equation Models
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Stelzl, Ingeborg – Multivariate Behavioral Research, 1991
Criteria for factor identification in factor analysis according to J. Algina (1980) are summarized, and a procedure is presented to determine rotationally underidentified factors by adding restrictors and to carry out the rotation for old and new restrictions and in latent path analysis. Two illustrations are presented. (SLD)
Descriptors: Equations (Mathematics), Hypothesis Testing, Mathematical Models, Path Analysis
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Lee, Sik-Yum; Song, Xin-Yuan – Multivariate Behavioral Research, 2001
Demonstrates the use of the well-known Bayes factor in the Bayesian literature for hypothesis testing and model comparison in general two-level structural equation models. Shows that the proposed method is flexible and can be applied to situations with a wide variety of nonnested models. (SLD)
Descriptors: Bayesian Statistics, Comparative Analysis, Goodness of Fit, Hypothesis Testing
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Neale, Michael C.; And Others – Multivariate Behavioral Research, 1994
In studies of relatives, conventional multiple regression may not be appropriate because observations are not independent. Obtaining estimates of regression coefficients and correct standard errors from these populations through a structural equation modeling framework is discussed and illustrated with data from twins. (SLD)
Descriptors: Analysis of Covariance, Causal Models, Data Collection, Error of Measurement