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Effect sizes are critical to result interpretation and synthesis across studies. Although statistical significance testing has historically dominated the determination of result importance, modern views emphasize the role of effect sizes and confidence intervals. This article accessibly discusses how to calculate and interpret the effect sizes that counseling psychologists use most frequently. To provide context, the author presents a brief history of statistical significance tests. Second, the author discusses the difference between statistical, practical, and clinical significance. Third, the author reviews and graphically demonstrates two common types of effect sizes, commenting on multivariate and corrected effect sizes. Fourth, the author emphasizes meta-analytic thinking and the potential role of confidence intervals around effect sizes. Finally, the author gives a hypothetical example of how to report and potentially interpret some effect sizes.

T. Vacha-Haase (1998) proposed her 'reliability generalization' methodology to characterize (a) typical score reliability for a measure across studies, (b) the variability of score reliabilities, and (c) what measurement protocol [...]

Although often ignored, reliability is critical when interpreting study effects and test results. Accordingly, this article focuses on the most commonly used estimate of reliability internal consistency coefficients, with emphasis [...]

Methods of reliability generalization characterize the reliability of a scale's scores across studies. Charter in 2003 presented useful formulae for computing combined estimates of reliability. The present article illustrates use of Charter's formulae and application of confidence intervals for reliability coefficients, including the combined estimate. Researchers may find these methods for reliability generalization useful.

Reliability generalization studies are increasingly frequently reported in the literature, including Mji and Alkhateeb's 2005 study of the Conceptions of Mathematics Questionnaire. The present article comments on an issue of reliability generalization as used in that study and clarifies a theoretical point regarding the meaning of coefficient alpha and what data features tend to influence it as a measure of score reliability.