Read more
Informationen zum Autor 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. Christine Blasey is a professor in the PGSP-Stanford Consortium, an academic program taught by faculty drawn from Palo Alto University and the Stanford University School of Medicine Department of Psychiatry. Klappentext 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. Zusammenfassung 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. Inhaltsverzeichnis 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...
