Statistical Power Analysis : A Simple and General Model for Traditional and Modern Hypothesis Tests

by Murphy, Kevin R.; Myors, Brett; Druck, Helmut; Junker, Frau

• ISBN13: 9780805845266
• ISBN10: 0805845267
• Edition: 2nd
• Format: Paperback
• Copyright: 2003-08-01
• Publisher: Lawrence Erlbau

Summary

This book presents a simple and general method for conducting statistical power analysis based on the widely used Fstatistic. The book illustrates how these analyses work and how they can be applied to problems of studying design, to evaluate others' research, and to choose the appropriate criterion for defining "statistically significant" outcomes. Statistical Power Analysisexamines the four major applications of power analysis, concentrating on how to determine: *the sample size needed to achieve desired levels of power; *the level of power that is needed in a study; *the size of effect that can be reliably detected by a study; and *sensible criteria for statistical significance. Highlights of the second edition include:a CDwith an easy-to-use statistical power analysis program; a new chapter on power analysis in multi-factor ANOVA,including repeated-measuresdesigns; and a new One-Stop PV Tableto serve as a quick reference guide. The book discusses the application of power analysis to both traditional null hypothesis tests and to minimum-effect testing. It demonstrates how the same basic model applies to both types of testing and explains how some relatively simple procedures allow researchers to ask a series of important questions about their research. Drawing from the behavioral and social sciences, the authors present the material in a nontechnical way so that readers with little expertise in statistical analysis can quickly obtain the values needed to carry out the power analysis. Ideal for students and researchers of statistical and research methodology in the social, behavioral, and health sciences who want to know how to apply methods of power analysis to their research.

Author Biography

Dr. Kevin R. Murphy is a Professor of Psychology at the Pennsylvania State University. He received his PhD from Penn State University in 1979. He served as the editor of the Journal of Applied Psychology Dr. Brett Myors is the Director of Organizational Training at Griffith University in Australia. He received his PhD in Industrial and Organizational Psychology at the University of New South Wales. His research interests include research methods, psychological assessment, and computer applications in these fields

Table of Contents

Preface	vii
1 The Power of Statistical Tests	1	(21)
The Structure of Statistical Tests	2	(5)
The Mechanics of Power Analysis	7	(8)
Statistical Power of Research in the Social and Behavioral Sciences	15
Using Power Analysis	11	(9)
Hypothesis Tests Versus Confidence Intervals	20	(1)
Conclusions	21	(1)
2 A Simple and General Model for Power Analysis	22	(33)
The General Linear Model, the F Statistic, and Effect Size	24	(1)
The F Distribution and Power	25	(5)
Translating Common Statistics and Effect Size Measures Into F	30	(3)
Alternatives to the Traditional Null Hypothesis	33	(3)
Minimum-Effect Tests as Alternatives to Traditional Null Hypothesis Tests	36	(5)
Analytic and Tabular Methods of Power Analysis	41	(2)
Using the One-Stop F Table	43	(5)
The One-Stop PV Table	48	(1)
The One-Stop F Calculator	49	(2)
Effect Size Conventions for Defining Minimum-Effect Hypotheses	51	(2)
Conclusions	53	(2)
3 Using Power Analyses	55	(14)
Estimating the Effect Size	56	(3)
Four Applications of Statistical Power Analysis	59	(9)
Conclusions	68	(1)
4 Multi-Factor ANOVA and Repeated-Measures Studies	69	(15)
The Factorial Analysis of Variance	70	(6)
Repeated Measures Designs	76	(6)
The Multivariate Analysis of Variance	82	(1)
Conclusions	83	(1)
5 Illustrative Examples	84	(14)
Simple Statistical Tests	84	(6)
Statistical Tests in Complex Experiments	90	(6)
Conclusions	96	(2)
6 The Implications of Power Analyses	98	(15)
Tests of the Traditional Null Hypothesis	99	(1)
Tests of Minimum-Effect Hypotheses	100	(4)
Power Analysis: Benefits, Costs, and Implications for Hypothesis Testing	104	(6)
Conclusions	110	(3)
References	113
Appendix A - Working With the Noncentral F Distribution	111	(8)
Appendix B - One-Stop F Table	119	(12)
Appendix C - One-Stop PV Table	131	(18)
Appendix D - df Needed for Power =.80 (α = .05) in Tests of Traditional Null Hypothesis	149	(4)
Appendix E - df Needed for Power =.80 (α = .05) in Tests of the Hypothesis That Treatments Account for 1% or Less of the Variance in Outcomes	153
Author Index	151	(8)
Subject Index	159