Simulation studies use computer-generated data to examine questions of interest that have traditionally been used to study properties of statistics and estimating algorithms. With the recent advent of powerful processing capabilities in affordable computers along with readily usable software, it is now feasible to use a simulation study to aid in…

Rosenthal and Rubin introduced a general effect size index, r[subscript equivalent], for use in meta-analyses of two-group experiments; it employs p values from reports of the original studies to determine an equivalent t test and the corresponding point-biserial correlation coefficient. The present investigation used Monte Carlo-simulated…

Grading on the curve is a common practice in higher education. While there are many critics of the practice it still finds wide spread acceptance particularly in science classes. Advocates believe that in large classes student ability is likely to be normally distributed. If test scores are also normally distributed instructors and students tend…

Computer sampling from a multivariate normal spherical population was used to evaluate Type I error rates for a test of P = I based on Fisher's tanh(sup minus 1) variance stabilizing transformation of the correlation coefficient. (Author/TJH)

In the present study Monte Carlo methods were employed to evaluate the degree to which canonical function and structure coefficients may be differentially sensitive to sampling error. Sampling error influences were investigated across variations in variable and sample (n) sizes, and across variations in average within-set correlation sizes and in…

This article proposes a method for testing equality of variances which exploits Pitman's idea and the computational power of simulations. Several advantages to this method are illustrated. A Monte Carlo study for several combinations of sample sizes and number of variables is presented. (Author/LMO)

Computer sampling from a multivariate normal spherical population was used to evaluate the type one error rates for a test of sphericity based on the distribution of the determinant of the sample correlation matrix. (Author/LMO)

Using a Monte Carlo simulation, a bootstrap procedure was evaluated for setting a confidence interval on the unrestricted population correlation (rho) assuming various degrees of incomplete truncation on the predictor. Sample size was the most important factor in determining accuracy and stability. Sample size should be at least 50. (SLD)

A method for estimating the first eigenvalue of random data correlation matrices is reported, and its precision is demonstrated via comparison to the method of S. J. Allen and R. Hubbard (1986). Data generated in Monte Carlo simulations with 10 sample sizes reaching up to 1,000 were used. (SLD)

Two new methods of estimating the mean population correlation (M) and the standard deviation of population correlations (SD) were suggested and tested by Monte Carlo simulations. Results show no consistent advantage to using the Pearson correlation or Fisher's Z in estimating M or SD; estimates from all methods are similar. (SLD)

Monte Carlo methods were used to evaluate the degree to which canonical function and structure coefficients may be differentially sensitive to sampling error. For each of 64 research situations, 1,000 random samples were drawn. Both sets of coefficients were roughly equally influenced; some exceptions are noted. (SLD)

Canonical correlation analysis is a powerful statistical method subsuming other parametric significance tests as special cases, and which can often best honor the complex reality to which most researchers wish to generalize. However, it has been suggested that the canonical correlation coefficient is positively biased. A Monte Carlo study…

A maximum likelihood approach to correlational outlier identification is introduced and compared to the Mahalanobis D squared and Comrey D statistics through Monte Carlo simulation. Identification performance depends on the nature of correlational outliers and the measure used, but the maximum likelihood approach is the most robust performance…

Effects of violating the independence assumption when combining correlation coefficients in a meta-analysis were studied. This Monte-Carlo simulation varied sample size, predictor number, population intercorrelation among predictors, and population correlation between predictors and criterion. Combining statistics from nonindependent data in a…

A mathematical formula is introduced for the effect of integrating data. A method is then derived to eliminate the effect from correlations of variables, including mean composites, thus allowing for a clustering algorithm that requires allocation of variables according to the magnitude of their correlations. Examples illustrate the procedure. (SLD)