Effect Sizes for Research: Univariate and Multivariate Applications, Second Edition

by Robert J. Grissom, John J. Kim (Contributor)

Noted for its comprehensive coverage, this greatly expanded new edition now covers the use of univariate and multivariate effect sizes. Many measures and estimators are reviewed along with their application, interpretation, and limitations. Noted for its practical approach, the book features numerous examples using real data for a variety of variables and designs, to help readers apply the material to their own data. Tips on the use of SPSS, SAS, R, and S-Plus are provided. The book's broad disciplinary appeal results from its inclusion of a variety of examples from psychology, medicine, education, and other social sciences. Special attention is paid to confidence intervals, the statistical assumptions of the methods, and robust estimators of effect sizes. The extensive reference section is appreciated by all. With more than 40% new material, highlights of the new editon Effect Sizes for Research covers standardized and unstandardized differences between means, correlational measures, strength of association, and parametric and nonparametric measures for between- and within-groups data. Intended as a resource for professionals, researchers, and advanced students in a variety of fields, this book is also an excellent supplement for advanced statistics courses in psychology, education, the social sciences, business, and medicine. A prerequisite of introductory statistics through factorial analysis of variance and chi-square is recommended.

452 pages, Paperback

First published March 1, 2005

Reviews

[G · Rating 4 out of 5]

A modern tradition that no one can really understand has built a gold-standard method to produce scientific evidence. The core idea of such method relies on decisions guided by probabilities, which are known as p-values in Mathematical Statistics. Such p-values are tools designed to avoid spurious explanations. If a set of observations can be attributed to random processes, then no explanatory model can be simultaneously proposed. That´s all. It sounds poor because it is poor. To overcome this situation, effect sizes have been created. Effect sizes are mathematical techniques aimed to produce more sophisticated evidence. Effect sizes are focused on the actual magnitude of the phenomena of interest. Grissom and Kim describe in this useful book the most often required effect sizes. Magnitude estimators both for observational and experimental studies are reviewed and clearly explained. Since non-Gaussian vectors of observations are frequently found in everyday research practice, maybe more pages focused on non-parametric effect sizes would have been welcomed. However, this book is a nice introduction to the logic, meaning, and calculation of effect sizes for research.