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…

A Monte Carlo study considers the use of meta analysis with the Solomon four-group design. Experiment-wise Type I error properties and the relative power properties of Stouffer's Z in the Solomon four-group design are explored. Obstacles to conducting meta analysis in the Solomon design are discussed. (SLD)

This article considers the impact of missing data arising from balanced incomplete block (BIB) spiraled designs on the chi-square goodness-of-fit test in factor analysis. The new approach is shown to outperform the pairwise available case method for continuous variables and to be comparatively better for dichotomous variables. (SLD)

A Monte Carlo experiment was conducted to investigate the performance of bootstrap methods in normal theory maximum likelihood factor analysis when the distributional assumption was satisfied or unsatisfied. Problems arising with the use of bootstrap methods are highlighted. (SLD)

Monte Carlo simulations were used to examine three cases using analyses of variance (ANOVAs) sequentially. Simulation results show that Type I error rates increase when using ANOVAs in this sequential fashion, and the detrimental effect is greatest in situations in which researchers would most likely use ANOVAs sequentially. (SLD)