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Power analysis is an essential tool for determining whether a statistically significant result can be expected in a scientific experiment prior to the experiment being performed. Many funding agencies and institutional review boards now require power analyses to be carried out before they will approve experiments, particularly where they involve the use of human subjects. This comprehensive, yet accessible, book provides practising researchers with step-by-step instructions for conducting power/sample size analyses, assuming only basic prior knowledge of summary statistics and the normal distribution. It contains a unified approach to statistical power analysis, with numerous easy-to-use tables to guide the reader without the need for further calculations or statistical expertise. This will be an indispensable text for researchers and graduates in the medical and biological sciences needing to apply power analysis in the design of their experiments.<br><br>
Introduction
1. The conceptual underpinnings of statistical power
2. Strategies for increasing statistical power
3. General guidelines for conducting a power analysis
4. The t-test for independent samples
5. The paired t-test
6. One-way between subjects analysis of variance
7. One-way between subjects analysis of covariance
8. One-way repeated measures analysis of variance
9. Interaction effects for factorial analysis of variance
10. Power analysis for more complex designs
11. Other power analytic issues and resources for addressing them
Technical appendix
Bibliography
Index
R. Barker Bausell is Professor at the University of Maryland School of Medicine and Director of Research of the Complementary Medicine Program. Yu-Fang Li is a Data Analyst at the Department of Veteran's Affairs, Puget Sound Health Care System in Seattle, Washington.
