Used meta analytic methods to summarize results of Monte Carlo studies of test size and power of the F test in the single-factor, fixed-effects analysis of covariance model, updating and extending narrative reviews of this literature. (SLD)

The meta-analytic method proposed by S. W. Raudenbush (1988) for studying variance heterogeneity was studied. Results of a Monte Carlo study indicate that the Type I error rate of the test is sensitive to even modestly platykurtic score distributions and to the ratio of study sample size to the number of studies. (SLD)

A Monte Carlo evaluation of four test statistics for comparing dependent zero-order correlations was conducted with four sample sizes and three population distributions. Results indicate that choice of optimal test statistic depends on sample size and distribution, and predictor intercorrelation and effect size or magnitude of the…

Several methods of constructing confidence intervals for Spearman's rho (rank correlation coefficient) (C. Spearman, 1904) were tested in a Monte Carlo study using 2,000 samples of 3 different sizes. Results support the continued use of Spearman's rho in behavioral research. (SLD)

Developed Markov Chain Monte Carlo procedures to perform Bayesian inference, model checking, and model comparison in heterogeneous factor analysis. Tested the approach with synthetic data and data from a consumption emotion study involving 54 consumers. Results show that traditional psychometric methods cannot fully capture the heterogeneity in…

Investigates the standard rounding rule for multiplication and division including its derivation from a basic assumption. Uses Monte-Carlo simulations to show that this rule predicts the minimum number of significant digits needed to preserve precision only 46.4% of the time and leads to a loss in precision 53.5% of time. Suggests an alternative…

A Monte Carlo simulation study compared the power and Type I errors of the Wilks lambda statistic and the statistic of M. L. Puri and P. K. Sen (1971) on transformed data in a one-way multivariate analysis of variance. Preferred test procedures, based on robustness and power, are discussed. (SLD)

A method for multidimensional scaling that is highly resistant to the effects of outliers is described. Some Monte Carlo simulations illustrate the efficacy of the procedure, which performs well with or without outliers. (SLD)

A Monte Carlo study involving 1,000 random samples from each of 64 different population matrices investigated bias in both canonical correlation and redundancy coefficients. Results indicate that the Wherry correction provides a reasonable solution to this problem and that canonical results are not as biased as has been believed. (TJH)

This paper explores the use of K. G. Joreskog's congeneric modeling approach to reliability using censored quantitative variables. In addition, the compound problem of non-normality and attenuation that occurs when estimating censored continuous variables is discussed. (TJH)

A Monte Carlo study was conducted to examine the performance of several strategies for estimating the squared cross-validity coefficient of a sample regression equation in the context of best subset regression. Results concerning sample size effects and the validity of estimates are discussed. (TJH)

An iterative procedure designed to minimize item bias affecting metrics in item response theory (IRT) was examined in a Monte Carlo investigation using the two-parameter IRT model. Two methods for transforming parameter estimates to a common metric were incorporated into the procedure. Results indicate that the procedure is effective. (TJH)

The performance was studied of five small-sample statistics--by F. M. Lord, W. Kristof, Q. McNemar, R. A. Forsyth and L. S. Feldt, and J. P. Braden--that test whether two variables measure the same trait except for measurement error. Effects of non-normality were investigated. The McNemar statistic was most powerful. (TJH)

A Monte Carlo simulation and applications to eight sets of proximity data are presented to support the practical utility of a network scaling algorithm (NETSCAL)--NETwork SCALing. The algorithm determines which vertices within a network are directly connected by an arc and estimates the length of each arc. (TJH)

The accuracy of four empirical techniques to estimate shrinkage in multiple regression was studied through Monte Carlo simulation. None of the techniques provided unbiased estimates of the population squared multiple correlation coefficient, but the normalized jackknife and bootstrap techniques demonstrated marginally acceptable performance with…