The use of Monte Carlo methods demonstrated that a formula presented by M. G. Kendall for estimating Pearson's rho from tau is somewhat more accurate than a formula presented by K. Pearson for estimating Pearson's rho from a Spearman's rho coefficient. Implications for meta-analysis of correlations are discussed. (SLD)

A Monte Carlo study compared the accuracy of different estimates of the standard error of correlations corrected for restriction in range. The procedure suggested by P. Bobko and A. Rieck (1980) generated the most accurate estimates of the standard error. Aspects of accuracy are discussed. (SLD)

Monte Carlo methods were used to investigate the effect of misspecification of the second level in a two-level hierarchical linear model (HLM). Sample composition, heterogeneity of the group size, level of intraclass correlation, and correlation between second-level predictors were manipulated. Each of 20 generated data sets was analyzed nine…

The use of U-statistics based on rank correlation coefficients in estimating the strength of concordance among a group of rankers is examined for cases where the null hypothesis of random rankings is not tenable. (Author/BW)

Two measures of association between sets of variables have been proposed for set correlation: the proportion of generalized variance, and the proportion of additionive variance. Because these measures are strongly positively biased, approximate expected values and estimators of these measures are derived and checked. (Author/BW)

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 Monte Carlo study was conducted to compare the efficiency of Listwise deletion, Pairwise deletion, Allvalue, and Samemean methods in estimating the correlation matrix from data that had randomly occurring missing values. The four methods were compared in a 3x3x4 factorial design representing sample size, proportion of incomplete records in the…

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)

The estimation of mean and standard errors of the eigenvalues and category quantifications in generalized non-linear canonical correlation analysis (OVERALS) is discussed. Starting points are the delta method equations. The jackknife and bootstrap methods are compared for providing finite difference approximations to the derivatives. Examining the…

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…

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)

Empirical estimates of the power and Type I error rate of the test of the classrooms-within-treatments effect in the nested analysis of variance approach are provided for a variety of nominal alpha levels and a range of classroom effect sizes and research designs. (SLD)

Validity generalization meta-analysis (VG) examines the extent to which the validity of an instrument can be transported across settings. VG offers correction and summarization procedures designed in part to remove the effects of statistical artifacts on estimates of association between criterion and predictor. By employing a random effects model,…

The Bootstrap method, a computer-intensive statistical method of estimation, is illustrated using a simple and efficient Statistical Analysis System (SAS) routine. The utility of the method for generating unknown parameters, including standard errors for simple statistics, regression coefficients, discriminant function coefficients, and factor…