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)

Computer programs for analysis of covariance use classical experimental, regression, or hierarchical methods of least squares. In a 3 X 3 factorial experiment with equal cell frequencies, three solutions yielded different sums of squares for main effects although correlation between variables was negligible and cell frequencies were equal.…

The hypothesis that two variables have a perfect disattenuated correlation and hence measure the same trait, except for errors of measurement, is discussed. Equivalently, the underlying variables, the true scores, are related linearly. It is shown that previously proposed ad hoc tests are, in fact, likelihood ratio tests. (Author/JKS)

Fixed-width confidence intervals for a population regression line over a finite interval of x have recently been derived by Gafarian. The method is extended to provide fixed-width confidence intervals for the difference between two population regression lines, resulting in a simple procedure analogous to the Johnson-Neyman technique. (Author)

The relationship between the factor structure of a convariance matrix and the factor structure of a partial convariance matrix when one or more variables are partialled out of the original matrix is given in this brief note. (JKS)

A process is described for multiple comparisons when covariates are involved in the analysis. The method can be accomplished with considerable ease whenever pairwise comparisons are involved. More complex contrasts require the use of full and restricted models of variance. (CTM)

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)

Issues in analysis of covariance, multiple regression analysis, and the analysis of variance such as the assumption of independence and directional hypotheses are discussed. (JKS)

The effects of the violation of the assumption of homogeneity of regression on the Type I error rate and on the power of analysis of covariance are investigated. The results indicate that analysis of covariance is robust when sample sizes are equal. (Author/JKS)

An evaluation of a college summer enrichment program is described in which analysis of covariance is used to focus on the impact of the program on college academic work. The integration of this statistical study into the decision making process of an evaluation task force is also described. (Author/JKS)

Perspective on article by P. Isaac published in the Psychological Bulletin, 1970, 74, 213-18. (CB)

Repeated measures designs often involve dichotomization of a continuous variable in order to be amenable to the analysis of variance nature of such designs. An alternative to that approach wherein the independent variable is kept continuous is presented. (JKS)