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Statistical Power Analysis
Noted for its accessible approach, this text applies the latest approaches of power analysis to both null hypothesis and minimum-effect testing using the same basic unified model. Through the use of a few simple procedures and examples, the authors show readers with little expertise in statistical analysis how to 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 choose the appropriate criterion for defining "statistically significant" outcomes are sprinkled throughout. The book presents a simple and general model for statistical power analysis based on the "F" statistic and 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 book helps readers design studies, diagnose existing studies, and understand why hypothesis tests come out out the way they do.
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The fourth edition features: "
-New "Boxed Material" sections provide examples of power analysis in action and discuss unique issues that arise as a result of applying power analyses in different designs.
-Many more worked examples help readers apply the concepts presented.
-Expanded coverage of power analysis for multifactor analysis of variance (ANOVA) to show readers how to analyze up to four factors with repeated measures on any or all of the factors.
-Re-designed and expanded web based "One Stop F Calculator" software and data sets that allow users to perform all of the book's analyses and conduct significance tests, power analyses, and assessments of N and alpha needed for traditional and minimum-effects tests.
-Easy to apply formulas for approximating the number of subjects required to reach adequate levels of power in a wide range of studies.
Intended as a supplement for graduate/advanced undergraduate courses in research methods or experimental design, intermediate, advanced, or multivariate statistics, statistics II, or psychometrics, taught in psychology, education, business, and other social and health sciences, researchers also appreciate the book s applied approach.
"
The fourth edition features: "
-New "Boxed Material" sections provide examples of power analysis in action and discuss unique issues that arise as a result of applying power analyses in different designs.
-Many more worked examples help readers apply the concepts presented.
-Expanded coverage of power analysis for multifactor analysis of variance (ANOVA) to show readers how to analyze up to four factors with repeated measures on any or all of the factors.
-Re-designed and expanded web based "One Stop F Calculator" software and data sets that allow users to perform all of the book's analyses and conduct significance tests, power analyses, and assessments of N and alpha needed for traditional and minimum-effects tests.
-Easy to apply formulas for approximating the number of subjects required to reach adequate levels of power in a wide range of studies.
Intended as a supplement for graduate/advanced undergraduate courses in research methods or experimental design, intermediate, advanced, or multivariate statistics, statistics II, or psychometrics, taught in psychology, education, business, and other social and health sciences, researchers also appreciate the book s applied approach.
244 pages, Paperback
First published August 1, 1995
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Displaying 1 - 2 of 2 reviews
October 31, 2016
tl;dr - fix the null hypothesis, use confidence intervals, and avoid moderated relationships
The null hypothesis has always bothered me, principally because you claim to be testing for a thing you hope to not find. This book lays out a more sinister problem: the odds of "no change" are so vanishingly small that your ability to find a 'significant' deviation from that null is easily created by simply collecting more samples. Moving to a distinction between nil (zero change) and null (insignificant change, defined by the authors as <1%) does change the statistics by forcing use of the noncentral F distribution, but it finally allows the null hypothesis to actually test something.
All of this is in the context of Type I and Type II errors. When you use the traditional null hypothesis, your chances of actually having "no change" are so small that the risk of a Type I error was already essentially zero, and yet most statistical methods revolve around helping and hoping to prevent it. The Type II error has been largely ignored, although the last 20 years have shown increasing awareness of the risk associated with the error of not finding an effect that actually does exist. This book lays the necessary groundwork to understand statistical power in a largely conversational fashion.
Cut to the chase: {alpha} is your willingness to accept a Type I, and is 0.05 by convention. {beta} is your willingness to accept a Type II, and is 0.2 by convention, giving a power of 0.8. Change those if you want, but it will make for an uphill battle to be accepted by peer review and won't actually change the risks as much as you imagine. Then, report the value (versus the nil or the null hypothesis) with a confidence interval so that the amount of change can be seen in contrast to the 'no change' effect.
At the end of the day, the real problem comes down to the size of the population being studied. If you're looking at over 1000 data points, you're almost certainly fine. If you're looking at less than 100, you're almost certainly at risk. I was hoping to learn something beyond the "just collect more" perspective of my Stat Mech class, but apparently state of the art isn't there yet.
The null hypothesis has always bothered me, principally because you claim to be testing for a thing you hope to not find. This book lays out a more sinister problem: the odds of "no change" are so vanishingly small that your ability to find a 'significant' deviation from that null is easily created by simply collecting more samples. Moving to a distinction between nil (zero change) and null (insignificant change, defined by the authors as <1%) does change the statistics by forcing use of the noncentral F distribution, but it finally allows the null hypothesis to actually test something.
All of this is in the context of Type I and Type II errors. When you use the traditional null hypothesis, your chances of actually having "no change" are so small that the risk of a Type I error was already essentially zero, and yet most statistical methods revolve around helping and hoping to prevent it. The Type II error has been largely ignored, although the last 20 years have shown increasing awareness of the risk associated with the error of not finding an effect that actually does exist. This book lays the necessary groundwork to understand statistical power in a largely conversational fashion.
Cut to the chase: {alpha} is your willingness to accept a Type I, and is 0.05 by convention. {beta} is your willingness to accept a Type II, and is 0.2 by convention, giving a power of 0.8. Change those if you want, but it will make for an uphill battle to be accepted by peer review and won't actually change the risks as much as you imagine. Then, report the value (versus the nil or the null hypothesis) with a confidence interval so that the amount of change can be seen in contrast to the 'no change' effect.
At the end of the day, the real problem comes down to the size of the population being studied. If you're looking at over 1000 data points, you're almost certainly fine. If you're looking at less than 100, you're almost certainly at risk. I was hoping to learn something beyond the "just collect more" perspective of my Stat Mech class, but apparently state of the art isn't there yet.
May 24, 2015
Excellent book for gaining a good understanding of Power Analysis.
Displaying 1 - 2 of 2 reviews