Describes the MANOVA (multivariate analysis of variance) Computer Program for ascertaining whether k groups differ significantly from one another on p dependent variables. (Author/RC)

When investigating differences between two sets of scores, the t test is appropriate. If the two sets of data are from two groups of subjects, then the independent t test is appropriate. If the two sets are from the same subjects, the dependent t test is required. In this paper, the authors describe the use of a third test when part of a data set…

The Cochran Q and the Minimum X sub one squared statistics are two ways to test a hypothesis of equivalent correlated proportions. This study investigated the small sample properties of Q and X sub one squared by Monte Carlo methods. The observed distributions were compared for their rates of covergence to the limiting theoretical X sub one…

There are good reasons for the growing popularity of Monte Carlo procedures; but with increasing use comes increasing misuse. A variety of exact and approximate alternatives should be considered before one chooses to approach a problem with Monte Carlo methods. Once it has been decided that simulation is desirable, consideration should be given to…

Simultaneous test procedures (STPS for short) in the context of the unrestricted full rank general linear multivariate model for population cell means are introduced and utilized to analyze interactions in factorial designs. By appropriate choice of an implying hypothesis, it is shown how to test overall main effects, interactions, simple main,…

A numerical procedure is outlined for obtaining an interval estimate of true score. The procedure is applied to several sets of test data. (Author)

Available approaches for determing significant differences between various groups are considered for use when the homogeneity of slopes precludes standard analysis of covariance. The advantages and statistical procedures for relating to a reference group are presented and criteria are suggested for choosing the best groups. (Author)

This article presents a system for avoiding Type I error increases when increasing the number of contrasts computed. Based on the Bonferroni inequality, the procedure corrects for the number of contrasts tested. Although conservative, the Bonferroni system is recommended for its flexibility, simplicity, and generality. (Author/BS)

While Bonferroni procedures control the risk of Type I errors, their cost is loss of power. Ordered Bonferroni procedures conserve power for more important tests while sacrificing power for less important tests. Both costs and benefits should be considered when choosing weights for individual tests and the overall level of Type I error protection.…

Whether one should use null hypothesis testing, confidence intervals, and/or effect sizes is a source of continuing controversy in educational research. An alternative to testing for statistical significance, known as equivalence testing, is little used in educational research. Equivalence testing is useful in situations where the researcher…

The program with extensive annotation is available in an unpublished dissertation (Schabert, 1970). (Authors)

Consulting editors (N=101) of the Journal of Counseling Psychology and the Journal of Consulting and Clinical Psychology evaluated three versions, differing only with regard to level of statistical significance, of a research manuscript. Statistically nonsignificant and approach significance versions were more likely to be recommended for…

Discusses (1) the use of chi square significance tests in social science and educational literature; (2) chi square in relation to sample size; (3) field difficulties with chi square analysis as exemplified in Milwaukee County Juvenile Court data; and (4) alternative methods for using chi square in field research. (Author/GC)

Regions of significance in aptitude-by-treatment-interaction studies are examined by the traditional statistical approach and an alternative approach which integrates: (1) testing for the parallelism of two or more regression lines; (2) testing for their identity; and (3) Scheffe's theorem. (Author/RL)