The analysis of contingency tables via set correlation allows the assessment of subhypotheses involving contrast functions of the categories of the nominal scales. The robustness of such methods with regard to Type I error and statistical power was studied via a Monte Carlo experiment. (TJH)

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…

This study integrates into one comprehensive Monte Carlo simulation a vast array of previously defined and substantively interrelated research studies of the robustness of analysis of variance (ANOVA) and analysis of covariance (ANCOVA) statistical procedures. Three sets of balanced ANOVA and ANCOVA designs (group sizes of 15, 30, and 45) and one…

This Monte Carlo study examines whether, given various numbers of variables, treatments, and sample sizes, in a one-way multivariate analysis of variance, Type I error rates of the test approximations provided by the BMDP program, the Statistical Analysis System (SAS), and the Statistical Package for the Social Sciences (SPSS) for Roy's largest…

The Random Normal Scores Test (RNST) has been suggested as a powerful alternative to the use of the parametric analysis of variance (ANOVA) test when the underlying population is non-normally distributed. The major support for this suggestion rests on asymptotic theory. An empirical analysis of the RNST performed under the F and Chi-square…

A method is proposed for sequential evaluation of reliability of psychometric instruments. Sample size is unfixed; a test statistic is computed after each person is sampled and a decision is made in each stage of the sampling process. Results from a series of Monte-Carlo experiments establish the method's efficiency. (SLD)

The empirical performance of the technique proposed by P. O. Johnson and J. Neyman (1936) (the JN technique) and the modification of R. F. Potthoff (1964) was studied in simulated data settings. The robustness of the two JN techniques was investigated with respect to their ability to control Type I and Type III errors. Factors manipulated in the…

A Monte Carlo study explores the robustness assumption in structural equation modeling of using a full information normal theory generalized least-squares estimation procedure on Type I censored data. The efficacy of the following proposed alternate estimation procedures is assessed: asymptotically distribution free estimator and a latent…

Meta-analytic methods were used to summarize results of Monte Carlo (MC) studies investigating the robustness of various statistical procedures for testing within-subjects effects in split-plot repeated measures designs. Through a literature review, accessible MC studies were identified, and characteristics (simulation factors) and outcomes (rates…