The importance of reporting explained variance in ANOVA designs is discussed. Three methods that can be used to obtain estimates of explained variance in ANOVA designs are described and applied to sixteen studies that were reported in recent NARST volumes. Recommends that researchers in science education report explained variance. (Author/DS)

Variance reduction analysis is a simple method for determining the effect of inequality of cell size on analysis of variance calculations. The suggested procedure has three steps. (Author/CK)

This article reviews some of the less well-known hypothesis tests for variance, how they are employed, and how the results may be interpreted. Tests include testing for a single variance and the T-test for correlated variances. (CT)

Focuses on Binomial Effect Size Display (a concept helpful in interpreting size of an experimental effect). Suggests that both statistical significance and explained variance be reported in experimental research and that values for the latter concepts can be viewed with a more accurate perspective by using BESD. (Author/JN)

Special issue devoted to neurolinguistics and neuropsychology. (VM)

The use of cost indexes is encouraged as a method of controlling cost and aiding planning. The use of analysis of variance is encouraged as a method of controlling cost standards. (MSE)

Provides an expository presentation of multivariate analysis (MANOVA). Addresses several questions relating to why one should use MANOVA, its structure, how MANOVA test statistics and follow-up tests are obtained and interpreted, presentation of results, and alternatives to MANOVA. Uses an example data set to illustrate these points. (Author/KS)

The Z-variance and Box-Scheffe tests for homogeneity of variance are both relatively simple to perform and readily utilized in complex, multi-factor designs. The Z-variance test is not robust against non-normality; the Box-Scheffe test is robust against non-normality but is not nearly as powerful as the Z-variance test. (Author/BJG)

The value of variance is emphasized, and the element of design, frequently not adequately understood, is clarified to underscore the importance of variance to the researcher. Two analytic methods, analysis of variance (ANOVA) and multiple regression, are discussed in terms of how each uses/applies variance. Advantages and major difficulties with…

A method of analysis is developed which accounts for whatever variance is present in the responses to test items, additive, configural or the two combined. The method is applied to successive column matrices which report frequencies or endorsements by categories of subjects to answer alternative sets of test items. Approaches are included which…

Two sampling studies were run to determine the robustness of the several tests of homogeneous variances to deviations from normality, and to compare the power of these tests. (Authors)

Proposes three different multiple range tests based upon the Newman-Keuls philosophy with respect to significance levels. The three tests utilize the Fmax statistic, Cochran's statistic and a normalizing log transformation of the sample variances respectively. (Author/RC)

In numerous applications of the analysis of variance, it is necessary to compute the power of F tests having numerically high alpha (significance) levels. This article tabulates the power of F tests for a variety of degrees of freedom. (Author/JKS)

Reasons for transforming dependent variable measures for use in analysis of variance is discussed, including nonnormality, heteroscedasticity and scale-induced interactions. It is pointed out that nonlinear transformations change the interpretation of interactions, and a method for reducing heteroscedasticity is described. (Author/RM)