Because of the continuing debates about statistics, many researchers may feel confused about how to analyze and interpret data. Current guidelines in psychology advocate the use of effect sizes and confidence intervals (CIs). However, researchers may be unsure about how to extract effect sizes from factorial designs. Contrast analysis is helpful…

An alternative to null hypothesis significance testing is presented and discussed. This approach, referred to as observation-oriented modeling, is centered on model building in an effort to explicate the structures and processes believed to generate a set of observations. In terms of analysis, this novel approach complements traditional methods…

Type I error rates in multiple regression, and hence the chance for false positive research findings, can be drastically inflated when multiple regression models are used to analyze data that contain random measurement error. This article shows the potential for inflated Type I error rates in commonly encountered scenarios and provides new…

When comparing k normal populations an investigator might want to know the probability that the population with the largest population mean will have the largest sample mean. This paper describes and illustrates methods of approximating this probability when the variances are unknown and possibly unequal. (Author/BW)

The computation of F ratios from cell means and standard deviations is shown for a two-way analysis of variance. (JKS)

A general analysis strategy is proposed such that the universe of inference is increased incrementally. The strategy prevents logically incongruent findings that occasionally result when the conventional analysis strategy is employed. (Author)

When homogeneity of slopes is not present, the Johnson-Neyman technique has been considered as an alternative to analysis of covariance. This paper describes how to apply the Johnson-Neyman technique for one or two covariates using the Statistical Package for the Social Sciences (SPSS) or BMDP (Biomedical Computer Programs). (Author/BW)

It has been shown that the degree of restriction of range taken into account in testing the hypothesis that rho equals zero, entails risks of incorrect inferences. It is argued that an alternative is to disregard the restriction of range and to use the common t-statistics proposed by regression theory. (Author/JKS)

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)

An alternative analysis of the two-group single response variable design is proposed. It involves the classification of experimental units to populations represented by the two groups. Three real data sets are provided to illustrate the utility of the classification analysis. A table of sample sizes required for the analysis is presented.…

To determine the effect of violating the assumption of homogeneity of covariance for the Tukey Wholly Significant Difference (WSD) test, Monte Carlo simulations varied the number of treatment groups, sample size, and degree of covariance heterogeneity. As covariance heterogeneity was increased, the empirical significance levels increased beyond…

EXPLORE is a flexible computer program for analyzing multiple data sets. The investigator has the option of focusing on the original variables, or of selecting a reduced rank solution where original variables are summarized by a principal components analysis. (Author/JKS)

The PM statistical index, which indicates the probability that a person will belong to a particular clinical class, is described. The coefficient is similar to the G index but is easier to compute. An empirical example is presented. (JKS)

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)