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.…

Real world data often fail to meet the underlying assumption of population normality. The Rank Transformation (RT) procedure has been recommended as an alternative to the parametric factorial analysis of Covariance (ANCOVA). The purpose of this study was to compare the Type I error and power properties of the RT ANCOVA to the parametric procedures…

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

A unified approach, based on ranks, to the statistical analysis of data arising from complex experimental designs is presented. The rank methods closely parallel the familiar methods of least squares, so that the estimates and tests have natural interpretations. (Author/JKS)

A nonparametric test of the hypothesis of no treatment effect is suggested for a situation where measures of the severity of the condition treated can be obtained and ranked both pre- and post-treatment. The test allows the pre-treatment rank to be used as a concomitant variable. (Author/JKS)

The Type I error and power properties of the parametric F test and three nonparametric competitors were compared in terms of 3 x 4 factorial analysis of covariance layout. The focus of the study was on the test for interaction either in the presence or absence of main effects. A variety of conditional distributions, sample sizes, levels of variate…

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…

Many methodologists are aware that parametric tests associated with the analysis of variance and the analysis of covariance can be computed using regression procedures. It is shown that multiple linear regression can also be employed to compute the Kruskal-Wallis nonparametric analysis of variance. (Author)

The author is generally critical of the previous article (TM 503 686), which concerned the use of multiple regression for nonparametric analysis of variance. (JKS)

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)