"Statistical Power Analysis" is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. 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.
This book is THE canonical reference for effect size and power testing; it coined the term “Cohen's d”. References in R functions, such as pwr.2p2n.test, are to this book, so it is a required reference.
That said, I feel it has not aged well as a book to actually read cover-to-cover. Written for the pre-computer era, it offers numerous tables and examples in their use, but offers little exposition on why certain measures were chosen. For example, the effect size transformation for proportions, phi=2*arcsin(sqrt(p)), is presented entirely without explanation of any kind, verbal or mathematical.
There are nuggets of wisdom here, but you have to sift for them.