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Statistical Power Analysis for the Behavioral Sciences
  • Jacob Cohen
  • Mathematics
    The SAGE Encyclopedia of Research Design
  • 1 December 1969
Contents: Prefaces. The Concepts of Power Analysis. The t-Test for Means. The Significance of a Product Moment rs (subscript s). Differences Between Correlation Coefficients. The Test That a
A Coefficient of Agreement for Nominal Scales
CONSIDER Table 1. It represents in its formal characteristics a situation which arises in the clinical-social-personality areas of psychology, where it frequently occurs that the only useful level of
Applied multiple regression/correlation analysis for the behavioral sciences
Contents: Preface. Introduction. Bivariate Correlation and Regression. Multiple Regression/Correlation With Two or More Independent Variables. Data Visualization, Exploration, and Assumption
Statistical Power Analysis
pression, Journal of Clinical Psychiatry, 51, 61-69 (1990). 11. L.R. Baxter, Jr., J.M. Schwartz, B.H. Guze, J.C. Mazziotta, M.P. Szuba, K. Bergman, A. Alazraki, C.E. Selin, H.K. Freng, P. Munford,
The earth is round (p < .05)
After 4 decades of severe criticism, the ritual of null hypothesis significance testing (mechanical dichotomous decisions around a sacred .05 criterion) still persists. This article reviews the
One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient,
The Equivalence of Weighted Kappa and the Intraclass Correlation Coefficient as Measures of Reliability
or weighted kappa (Spitzer, Cohen, Fleiss and Endicott, 1967; Cohen, 1968a). Kappa is the proportion of agreement corrected for chance, and scaled to vary from -1 to +1 so that a negative value
This is an account of what I have learned (so far) about the application of statistics to psychology and the other sociobiomedical sciences. It includes the principles "less is more" (fewer
Set Correlation and Contingency Tables
Set correlation is a realization of the general multi variate linear model, can be viewed as a multivariate generalization of multiple correlation analysis, and may be employed in the analysis of