Use of the Extreme Groups Approach: A Critical Reexamination and New Recommendations.

Analysis of continuous variables sometimes proceeds by selecting individuals on the basis of extreme scores of a sample distribution and submitting only those extreme scores to further analysis. This sampling method is known as the extreme groups approach (EGA). EGA is often used to achieve greater statistical power in subsequent hypothesis tests. However, there are several largely unrecognized costs associated with EGA that must be considered. The authors illustrate the effects EGA can have on power, standardized effect size, reliability, model specification, and the interpretability of results. Finally, the authors discuss alternative procedures, as well as possible legitimate uses of EGA. The authors urge researchers, editors, reviewers, and consumers to carefully assess the extent to which EGA is an appropriate tool in their own research and in that of others. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Comments on "A general canonical correlation index."

Challenges D. Stewart and W. Love's (see record 1968-18070-001) claims that their proposed general canonical correlation index (a) represents a proportion of overlapping, or redundant, variance; and (b) is the average of certain squared multiple correlations. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Reliability of measurement and the power of statistical tests: Some new results.

The authors (see record 1979-00153-001) argued that the reliability coefficient for the dependent variable in a controlled experiment has no direct relevance for hypothesis testing. Specifically, they demonstrated that increasing the reliability coefficient for the dependent variable did not necessarily increase the power of standard statistical tests. The authors present further evidence that large reliability coefficients are not always desirable in true experiments, and replies to J. P. Sutcliffe's (see record 1980-29332-001) basic criticisms of Nicewander and Price's contentions. (6 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Dependent variable reliability and the power of significance tests.

Assuming that the linear models for classical test theory and ANOVA hold simultaneously for some dependent variable, it is shown that 2 contradictory statements concerning the relationship between reliability and statistical power are both correct. J. E. Overall and J. A. Woodward (see PA, Vol 53:8623, 57:7284) showed that when the reliability of a difference or change score is zero, the power of a statistical test of a hypothesis of no change can be at a maximum. J. L. Fleiss (see record 1977-07259-001) found the opposite result (i.e., that the power of a statistical test of no pre–post change is at a maximum when the reliability of the difference or gain scores is equal to one). The role of the reliability of the dependent variable in statistical evaluations of controlled experiments is examined. It is argued that the conditions that yield high reliability coefficients are not necessarily optimal for significance testing. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

On the mathematical bases of the general canonical correlation index: Rejoinder to Miller.

Shows that J. K. Miller's (see PA, Vol 54:Issue 1) major contentions regarding the general canonical correlation index, or redundancy index, are either incorrect or have no direct bearing on canonical analysis. A derivation of the redundancy index is provided which holds even in the case of reduced rank canonical solutions. The utility of the redundancy index is again questioned. (PsycINFO Database Record (c) 2010 APA, all rights reserved)