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)

The problems inherent in analyzing data in which subjects are "nested" within hierarchical units (such as classrooms or schools), and thus are not independent of one another, are addressed through the use of Joreskog's LISREL model for analyzing covariance matrices. A solution to the problem is proposed and illustrated using data from…

Four computer programs using the general purpose multiple linear regression program have been developed. Setwise regression analysis is a stepwise procedure for sets of variables; there will be as many steps as there are sets. Covarmlt allows a solution to the analysis of covariance design with multiple covariates. A third program has three…

Joreskog's general method for analysis of covariance structures was developed for estimating a model involving structures of a very general form on means, variances, and covariances of multivariate observations. This method achieves a great deal of generality and flexibility, in that it is capable of handling most standard statistical models as…

Paper reviews the test for regression lines and provides equations for expanding this test to regression planes. (Author)

Problems associated with the use of gain scores, analysis of covariance, multicollinearity, part and partial correlation, and the lack of rectilinearity in regression are discussed. Particular attention is paid to the misuse of statistical techniques. (JKS)

This article discusses M. Rosenberg's Test-Factor Standardization as a type of marginal standardization. To illustrate this relation, the author utilizes a table on abortion attitudes formerly published by K.W. Smith in his commentary on Rosenberg's work. (Author/GC)

A method for the estimation of covariance structure models under polynomial constraints (such as quadratic constraints) is presented. Estimation is on maximum likelihood principles, and the test statistics, parameter estimates, and standard errors are based on a statistical theory which takes the constraints into account. (Author/JKS)

A variety of algorithms for analyzing covariance structures are considered. Additionally, two methods of estimation, maximum likelihood, and weighted least squares are considered. Comparisons are made between these algorithms and factor analysis. (Author/JKS)

Systematic procedures are outlined for testing the assumption made on the dependent variables in a variety of statistical techniques. Specifically, procedures are outlined for testing the assumption that the dependent variables are measuring the same constructs in the same metrics with equivalent reliabilities across all subgroups. (Author/JKS)

Multiple linear regression is a versatile model for encompassing analysis of variance, analysis of covariance, and aptitude-by-treatment interaction designs. The questions of how to teach the coding of levels of a qualitative variable is addressed in this paper. (Author/LMO)

Similarities between regression analysis and analysis of variance are noted and it is argued that the former has advantages over the latter. It is also argued that canonical correlation analysis is more suitable than factor analysis in certain cases. The argument is illustrated with four recent pieces of educational research. (DG)

The effects of violation of the assumption of homogeneity of regression on the Type I error rate and on the power of analysis of covariance (ANCOVA) were investigated. The data situations included in the study involved two groups with one covariate and one criterion, with varying equal and unequal group sizes, and varying degrees of violation of…