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 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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Helena Chmura Kraemer is professor emerita of biostatistics in the Department of Psychiatry and Behavioral Sciences at Stanford University. She earned her BA in mathematics from Smith College, attended Manchester University on a Fulbright Scholarship, and received her PhD in statistics from Stanford University. Kraemer's specific research interests include improvement to randomized clinical trial methodology, assessment of reliability and validity of diagnoses and clinical measurement, and developing mathematical models for specific problems in behavioral and clinical research. She has published extensively in the behavioral as well as statistical literature. Kraemer has received the Harvard Prize in Psychiatric Biostatistics and Epidemiology (2001), the Andrew C. Leon Distinguished Career Award (2014), an Honorary Doctor of Science from Wesleyan University (2014), and is a member of the Institute of Medicine, National Academy of Sciences (2003). In retirement, she continues to serve on several editorial boards, and consult on research projects.
Addressing a common question posed by researchers, this book introduces readers to power analysis and sample size determination and clearly illustrates why sample sizes need to be sufficiently large to give good power properties and low error rates.
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