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Kim, Rae-Seon; Becker, Betsy Jane – Multivariate Behavioral Research, 2010
We examined the degree of dependence between standardized-mean-difference effect sizes in multiple-treatment studies in meta-analysis in terms of the correlation formula provided by Gleser and Olkin (1994). To explore the impact of group size and the values of the true multiple-treatment effect sizes, we simplified the formula for the correlation…
Descriptors: Effect Size, Meta Analysis, Correlation, Control Groups
Peer reviewedStavig, Gordon R.; Acock, Alan C. – Multivariate Behavioral Research, 1981
Examples are given to show how the semistrandardized (SS) regression coefficient provides information not given by the conventional standardized regression coefficients used in factor, canonical, and path analysis. (Author/RL)
Descriptors: Factor Analysis, Mathematical Formulas, Multivariate Analysis, Path Analysis
Computational Formulas for Multivariate Strength of Association from Approximate "F" and "X2" Tests.
Peer reviewedHaase, Richard F. – Multivariate Behavioral Research, 1991
Computational formulas are developed for recovering measures of strength of association from approximate "F" tests and chi-square tests associated with four multivariate test statistics. The four statistics include Wilke's Lambda; Pillai's Trace "V"; Hotelling's Trace "T"; and Roy's greatest characteristic root…
Descriptors: Chi Square, Estimation (Mathematics), Mathematical Formulas, Mathematical Models
Peer reviewedEveritt, B. S. – Multivariate Behavioral Research, 1981
Results show that the proposed sampling distribution of the test appears to be appropriate only for sample sizes above 50, and for data where the sample size is 10 times the number of variables. For such cases the power of the test is found to be fairly low. (Author/RL)
Descriptors: Mathematical Formulas, Maximum Likelihood Statistics, Monte Carlo Methods, Multivariate Analysis

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