- Cohen, J. (1988). The Effect Size. Statistical Power Analysis for the . . .
ABSTRACT: The aim of the study was to compare the effect of additional practice training of small-sided games (SSG) or repeated sprints (RS) on mood state, and physical performance in professional soccer players Twenty four professional soccer players took part in this study (age: 17 ± 0 19 years)
- Statistical Power Analysis for the Behavioral Sciences
The Second Edition includes: * a chapter covering power analysis in set correlation and multivariate methods; * a chapter considering effect size, psychometric reliability, and the efficacy of "qualifying" dependent variables and; * expanded power and sample size tables for multiple regression correlation
- Effect Sizes - Ryan T. Cragun
The standard citations are to Jacob Cohen’s work: Cohen, Jacob 1988 Statistical Power Analysis for the Behavioral Sciences Mahwah, N J : Lawrence Erlbaum Cohen, Jacob 1992 “A Power Primer ” Psychological Bulletin 112 (1):155–59 However, the new citation is to Sawilowsky: Sawilowsky, Shlomo S 2009 “New Effect Size Rules of
- Effect Size Guidelines, Sample Size Calculations, and Statistical Power . . .
The results of this study suggest that Cohen’s (1988, 1992) guidelines may overestimate average effect sizes in gerontology, which can result in sample size calculations and interpretations of observed effect sizes that are not necessarily appropriate for the field
- Statistical Power Analysis for the Behavioral Sciences
He may also find interesting the systematic treatment of population effect size, and particularly the proposed conventions or operational definitions of "small," "medium," and" large" effect sizes defined across all the statistical tests
- Effect size guidelines for individual differences researchers
Cohen (1988) provided guidelines for the purposes of interpreting the magnitude of a correlation, as well as estimating power Specifically, r = 0 10, r = 0 30, and r = 0 50 were recommended to be considered small, medium, and large in magnitude, respectively
- Cohens d - Diener - Major Reference Works - Wiley Online Library
Effect sizes provide an important complement to traditional null hypothesis statistical significance testing The traditional p value indicates the likelihood of obtaining the observed results if the null hypothesis is true (Cohen, 1988)
- Calculating and reporting effect sizes to facilitate cumulative science . . .
In an a-priori power analysis, researchers calculate the sample size needed to observe an effect of a specific size, with a pre-determined significance criterion, and a desired statistical power A generally accepted minimum level of power is 0 80 (Cohen, 1988)
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