About the book
Noted for its accessible approach, this bestseller applies power analysis to both null hypothesis and minimum-effect testing using the same basic model.
Through the use of a few relatively simple procedures and examples from the behavioral and social sciences, the authors show readers with little expertise in statistical analysis how to quickly obtain the values needed to carry out the power analysis for their research.
Illustrations of how these analyses work and how they can be used to understand problems of study design, to evaluate others' research, and to choose the appropriate criterion for defining "statistically significant" outcomes are sprinkled throughout. The book presents a simple and general model for statistical power analysis that is based on the F statistic.
Statistical Power Analysis reviews how to determine:
- the sample size needed to achieve desired levels of power;
- the level of power needed in a study;
- the size of effect that can be reliably detected by a study; and
- sensible criteria for statistical significance.
The third edition features:
- A new chapter (7) on Complex ANOVA Designs that demonstrates the use of power analysis in split-plot, randomized block, and other ANOVA designs.
- Re-designed, more user-friendly software (on the book's website) that allows users to perform all of the book's analyses on a wider range of tests and more flexibility in defining the hypothesis, the desired power level, and the alpha level.
- New boxed sections that provide examples of power analysis in action and unique issues that arise when applying power analyses in different designs.
- Expanded coverage of minimum-effect tests (Ch. 3), the fundamentals of power analysis (Ch.4), correlational hypotheses (Ch. 5), and t tests (Ch. 6).
Ideal for students and researchers in the social, behavioral, and health sciences, business, and education who need to apply methods of power analysis to their research, this valuable resource helps readers design and diagnose studies and understand why hypothesis tests come out the way they do. PV and F tables serve as a quick reference.