Analysis of Covariance (ANCOVA) is robust with respect to dual violations of the assumptions of equal regression slopes and normality of distributions provided that group sizes are equal, but displays disruptions of empirical significant levels when unequal regression slopes and unequal group sizes are coupled with nonnormal distributions.…

This program calculates the chi-square statistic for testing the assumptions of equality and symmetry of covariance matrices in a repeated measures design. (Author)

The author contends Glass, Peckham, and Sanders erred in discouraging the use of nonparametric counterparts of the t-test, even when it was known data were sampled from skewed distributions. He believes Wilcoxon's rank-sum test has power properties that make it preferable in most nonnormal population situations. (Author/DWH)

Presents a new nonparametric method for constructing a spatial representation of multidimensional test structure, the Conditional Covariance-based SCALing (CCSCAL) method. Describes an index to measure the accuracy of the representation. Uses simulation and real-life data analyses to show that the method provides a suitable approximation to…

The program presented computes a chi-square statistic for testing pattern hypotheses on correlation matrices. The statistic is based on a multivariate generalization of the Fisher r-to-z transformation. This statistic has small sample performance which is superior to an analogous likelihood ratio statistic obtained via the analysis of covariance…

Three nonparametric procedures that use estimates of covariances of item-pair responses conditioned on examinee trait level for assessing dimensionality of a test are described. The HCA/CCPROX, DIMTEST, and DETECT are applied to a dimensionality study of the Law School Admission Test. (SLD)

A statistical test for analysis of repeated measures is presented. It is shown that an appropriate chi-square statistic can be computed for related measures by taking account of the covariance among trials in computing a variance for the chi square statistic. (Author/JKS)

Using computer simulation, parametric analysis of covariance (ANCOVA) was compared to ANCOVA with data transformed using ranks, in terms of proportion of Type I errors and statistical power. Results indicated that parametric ANCOVA was robust to violations of either normality or homoscedasticity, but practiced significant power differences favored…

Five distribution-free alternatives to parametric analysis of covariance are presented and demonstrated: Quade's distribution-free test, Puri and Sen's solution, McSweeney and Porter's rank transformation, Burnett and Barr's rank difference scores, and Shirley's general linear model solution. The results of simulation studies regarding Type I…