OVERCOMING FEELINGS OF POWERLESSNESS IN AGING RESEARCHERS - A PRIMER ON STATISTICAL POWER IN ANALYSIS OF VARIANCE DESIGNS

A general rationale and specific procedures for examining the statisti cal power characteristics of psychology-of-aging empirical studies are provided. First, 4 basic ingredients of statistical hypothesis testin g are reviewed. Then, 2 measures of effect size are introduced (standa rdized mean differences and the proportion of variation accounted for by the effect of interest), and methods are given for estimating these measures from already-completed studies. Power and sample size formul as, examples, and discussion are provided for common comparison-of-mea ns designs, including independent samples I-factor and factorial analy sis of variance (ANOVA) designs, analysis of covariance designs, repea ted measures (correlated samples) ANOVA designs, and split-plot (combi ned between- and within-subjects) ANOVA designs. Because of past conce ptual differences, special attention is given to the power associated with statistical interactions, and cautions about applying the various procedures are indicated. Illustrative power estimations also are app lied to a published study from the literature. It is argued that psych ology-of-aging researchers will be both better informed consumers of w hat they read and more ''empowered'' with respect to what they researc h by understanding the important roles played by power and sample size in statistical hypothesis testing.