Statistical Power Analysis explains the key concepts in statistical
power analysis and illustrates their application in both tests of
traditional null hypotheses (that treatments or interventions have no
effect in the population) and in tests of the minimum-effect hypotheses
(that the population effects of treatments or interventions are so small
that they can be safely treated as unimportant). It provides readers
with the tools to understand and perform power analyses for virtually
all the statistical methods used in the social and behavioral sciences.
Brett Myors and Kevin Murphy apply the latest approaches of power
analysis to both null hypothesis and minimum-effect testing using the
same basic unified model. This book starts with a review of the key
concepts that underly statistical power. It goes on to show how to
perform and interpret power analyses, and the ways to use them to
diagnose and plan research. We discuss the uses of power analysis in
correlation and regression, in the analysis of experimental data, and in
multilevel studies. This edition includes new material and new power
software. The programs used for power analysis in this book have been
re-written in R, a language that is widely used and freely
available. The authors include R codes for all programs, and we have
also provided a web-based app that allows users who are not comfortable
with R to perform a wide range of analyses using any computer or
device that provides access to the web.
Statistical Power Analysis helps readers design studies, diagnose
existing studies, and understand why hypothesis tests come out the way
they do. The fifth edition includes updates to all chapters to
accommodate the most current scholarship, as well as recalculations of
all examples. This book is intended for graduate students and faculty in
the behavioral and social sciences; researchers in other fields will
find the concepts and methods laid out here valuable and applicable to
studies in many domains.
