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…

Second-order latent growth curve models (S. C. Duncan & Duncan, 1996; McArdle, 1988) can be used to study group differences in change in latent constructs. We give exact formulas for the covariance matrix of the parameter estimates and an algebraic expression for the estimation of slope differences. Formulas for calculations of the required sample…

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)

There is evidence to suggest that the effects of behavioral interventions may be limited to specific types of individuals, but methods for evaluating such outcomes have not been fully developed. This study proposes the use of finite mixture models to evaluate whether interventions, and, specifically, group randomized trials, impact participants…

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…

Meehl's taxometric method was developed to distinguish categorical and continuous constructs. However, taxometric output can be difficult to interpret because expected results for realistic data conditions and differing procedural implementations have not been derived analytically or studied through rigorous simulations. By applying bootstrap…

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)