Journal ArticleDOI
Statistical Power Analysis for the Behavioral Sciences (2nd ed.)
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This article is published in Journal of the American Statistical Association.The article was published on 1989-12-01. It has received 49129 citations till now. The article focuses on the topics: Effect size & Statistical power.read more
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G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences
TL;DR: G*Power 3 provides improved effect size calculators and graphic options, supports both distribution-based and design-based input modes, and offers all types of power analyses in which users might be interested.
Journal ArticleDOI
A power primer.
TL;DR: A convenient, although not comprehensive, presentation of required sample sizes is providedHere the sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests.
Journal ArticleDOI
Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.
TL;DR: In the new version, procedures to analyze the power of tests based on single-sample tetrachoric correlations, comparisons of dependent correlations, bivariate linear regression, multiple linear regression based on the random predictor model, logistic regression, and Poisson regression are added.
Book
Experimental Design and Data Analysis for Biologists
Gerry P. Quinn,Michael J. Keough +1 more
TL;DR: An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data is as discussed by the authors, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced.
Journal ArticleDOI
Power analysis and determination of sample size for covariance structure modeling.
TL;DR: In this article, a framework for hypothesis testing and power analysis in the assessment of fit of covariance structure models is presented, where the value of confidence intervals for fit indices is emphasized.