This book presents a simple and general method for conducting statistical power analysis based on the widely used F statistic. 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 Analysis examines 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 CD with an easy-to-use statistical power analysis program; a new chapter on power analysis in multi-factor ANOVA, including repeated-measures designs; and a new One-Stop PV Table to 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.
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; * ...
Snowdon , D. A. , and Phillips , R. L. ( 1984 ) . “ Coffee consumption and risk of fatal cancers , ” American Journal of Public Health 74 ( 8 ) , 820-823 . Whitsett , T. L. , Manion , C. V. , Christensen , H. D. ( 1984 ) .
This is followed by examples that demonstrate how to produce power tables and charts. The book clearly shows how to calculate power by providing open code for every design and procedure in R, SAS, and SPSS.
Tanaka, J. S. 42 Tang, N.S. 232 Taylor, B. J. 48, 144, 147, 152, 164 Tibshirani, R. 53 Tucker, L. R. 42 Tufte, E. R. 170 Tukey, J. W. 170. V. Vale, C. D. 177 Van Alstine, J. 41 van der Sluis, S. 117, 140, 152, 154 VictoriaFeser, ...
Without careful planning, a study can easily fail to detect an existing effect by chance. This book teaches how to conduct power analysis for a range of models from correlation and t-test to structural equation models and multilevel models.
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.
With increased emphasis on helping readers understand the context in which power calculations are done, this Second Edition of How Many Subjects? by Helena Chmura Kraemer and Christine Blasey introduces a simple technique of statistical ...
The book gives a thorough overview of power analysis that details terminology and notation, outlines key concepts of statistical power and power analysis, and explains why they are necessary in trial de
Written primarily for mid-to-upper level undergraduates, this compelling introduction to power analysis offers a clear, conceptual understanding of the factors that influence statistical power, as well as guidance on improving and ...
This volume brings statistical power and incomplete data together under a common framework, in a way that is readily accessible to those with only an introductory familiarity with structural equation modeling.