The homogeneity of group regressions test and regions of significance test are two procedures which are frequently recommended for testing and aptitude-treatment or trait-treatment interaction (ATI) hypothesis. The former is used to determine if treatment group regressions are nonparallel while the latter is used to determine the range of aptitude…

Designed for use with Research for Better Schools' evaluation system, this guide focuses upon basic concepts and statistical procedures applicable to specific research hypotheses emerging from evaluation questions. The first of three sections presents a discussion of descriptive statistics in terms of frequency, central tendency, variability, and…

When examining a repeated measures design with independent groups for a significant group by trial interaction, classical analysis of variance or multivariate procedures can be used if the assumptions underlying the tests are met. Neither procedure may be justified for designs with small sample sizes and dichotomous dependent variables. An omnibus…

The purpose of this paper is to demonstrate the use of the general linear model (GLM) in problems with repeated measures on a dependent variable. Such problems include pretest-posttest designs, multitrial designs, and groups by trials designs. For each of these designs, a GLM analysis is demonstrated wherein full models are formed and restrictions…

Considerable thought, research, and concern has been expanded in an effort to determine whether the assumption of a quadratic relation between a single predictor and a criterion violated the assumptions which Johnson and Neyman (1936) state for calculating regions of significance about interacting regressions. In particular, there has been special…

This study is an analysis of the robustness of the Box-Tiao integrated moving averages model for analysis of time series quasi experiments. One of the assumptions underlying the Box-Tiao model is that all N values of alpha subscript t come from the same population which has a variance sigma squared. The robustness was studied only in terms of…

When the variance assumptions of a repeated measures ANOVA are not met, the F distribution of the mean square ratio should be adjusted by the sample estimate of the Box correction factor. An alternative is proposed which is shown by Monte Carlo methods to be less biased for a moderately large factor. (RC)

The problem of determining the significance level which should be used in statistical tests of item validity in order to minimize type I errors is discussed. (Author/JKS)

This paper develops a general method for comparing treatment magnitudes for research employing multiple treatment fixed effects analysis of variance designs, which may be used for main effects with any number of levels without regard to directionality. (Author/BS)

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…

The method of unweighted means in the multivariate analysis of variance with unequal sample sizes was investigated. By approximating the distribution of the hypothesis sums-of-squares-and-cross-products with a Wishart distribution, multivariate test statistics were derived. Monte Carlo methods and a numerical example illustrate the technique.…

The relation between the power of a significance test in a block design with correlated measurements and the reliability of the measuring instrument is analyzed in terms of the components of variance entering the reliability coefficient and the noncentrality parameter. (Author/LMO)

Attempts to distill a growing technical literature on repeated-measures analysis of variance into a few simple principles for selecting an analytic technique. Argues that researchers ought not opt for a general analysis strategy when current computer technology makes it possible to select the optimal analysis technique for a given data set. (RH)

This paper discusses the sample size problem and four factors affecting its solution: significance level, statistical power, analysis procedure, and effect size. The interrelationship between these factors is discussed and demonstrated by calculating minimal sample size requirements for a variety of research conditions. (Author)

The basic notion of variability is generalized from a univariate context to a multivariate context using two matrix functions, a determinant, and a trace, yielding a number of alternative multivariate indices of shared variation. Some problems in the interpretation of tests of multivariate hypotheses are reviewed. (Author/BW)