How Many Subjects?
Statistical Power Analysis in Research

Second Edition

Helena Chmura Kraemer - Stanford University (Stanford, California, United States)
Christine Blasey - Palo Alto University

February 2015 | 160 pages | SAGE Publications, Inc

With increased emphasis on helping readers understand the context in which power calculations are done, this Second Edition introduces a simple technique of statistical power analysis that allows researchers to compute approximate sample sizes and power for a wide range of research designs. Because the same technique is used with only slight modifications for different statistical tests, researchers can then easily compare the sample sizes required by different designs and tests to make cost-effective decisions in planning a study. These comparisons demonstrate important principles of design, measurement, and analysis that are rarely discussed in courses or textbooks, making this book a valuable instructional resource as well as a must-have guide for frequent reference.

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ISBN: 9781483319544	Paperback	Suggested Retail Price: $42.00	Bookstore Price: $33.60
ISBN: 9781483319551	Electronic Version	Suggested Retail Price: $36.00	Bookstore Price: $28.80

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PREFACE

1. The Rules of the Game

Exploratory Studies

Hypothesis Formulation

Null Hypothesis

Design

The Statistical Test

Effect Sizes: Critical, True, and Estimated

Power

2. General Concepts

Introduction to the Power Table

Statistical Considerations

3. The Pivotal Case: Interclass Correlation

The Intraclass Correlation Test

The ANOVA Approach to Intraclass Correlation Test

Normal Approximation to the Intraclass Theory

Non-Central t

Variance Ratios

Conclusion

4. Equality of Means: Z- and T-Test, Balanced ANOVA

Single-Sample Test, Variance Known: z-test

Single-Sample t-test

Two Sample t-test

An Exercise in Planning

Balanced Analysis of Variance (ANOVA)

5. Correlation Coefficients

Intraclass Correlation Coefficient

Product-Moment Correlation Coefficient

Rank Correlation Coefficients

You Study What You Measure!

6. Linear Regression Analysis

Simple Linear Regression

Experimental Design: Choosing the X-Values

Simple Linear Moderation Example

Problems: Collinearity and Interactions

Multiple Linear Regression

7. Homogeneity of Variance Tests

Two Independent Samples

Matched Samples

8. Binomial Tests

Single-Sample Binomial Tests

Two-Sample Binomial Tests

9. Contingency Table Analysis

Introduction

The I X J x^2-test

An Example of a 3 X 2 Contingency Table Analysis

10. Wrap-Up

"Kraemer and Blasey provide an authoritative and readable introduction into applied statistical power analysis and its application with many common statistical procedures."

Glenn Gamst

University of La Verne

“This is a necessary text for anyone conducting research in the real world. Nowhere else will you find a better answer to the question, 'How Many Subjects?'”

Bryan Rooney

Concordia University College of Alberta

Too advanced for the course; but on additional/further readings list.

Ms Susanne Kirchmair

Accounting , MCI Management Center Innsbruck

February 26, 2016

Report this review

Key features

NEW TO THIS EDITION:

Power computations are now placed in the proper context as one small but crucial step in applying the scientific method.
The number of tests to which the methods can be applied has been extended.
The book now incorporates the authors’ experience where errors in design and interpretation of statistical hypothesis testing occur.
Recent emphasis on effect sizes rather than p-values is endorsed and emphasized throughout.
Recent developments in consideration of moderators and mediators are acknowledged.

KEY FEATURES:

The book provides empirical researchers with the means to quickly determine a valuable piece of information; namely, what sample size is needed for a particular study.
The book’s tables can be used for a variety of different common tests by modifying the relationship of the effect size, design parameters, and sample size to the row and column definitions.
The authors’ approach corresponds to “differential diagnosis” in medicine, in this case, shifting through various valid options available for testing a hypothesis and choosing the one most likely to succeed.

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