Effect Sizes For Research

Bol

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 include: three new multivariate chapters covering effect sizes for analysis of covariance, multiple regression/correlation, and multivariate analysis of variance more learning tools in each chapter including introductions, summaries, "Tips and Pitfalls" and more conceptual and computational questions more coverage of univariate effect sizes, confidence intervals, and effect sizes for repeated measures to reflect their increased use in research more software references for calculating effect sizes and their confidence intervals including SPSS, SAS, R, and S-Plus the data used in the book are now provided on the web along with new data and suggested calculations with IBM SPSS syntax for computational practice. 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.

Bol Partner

Emphasis on effect sizes is rapidly rising since more than 20 journals in various fields of research now require that authors of research reports provide estimates of effect size. Especially in applied research, results need to be supplemented with estimates of how different the average results for studied groups are or how strong the association between variables is. Those who apply the results of research often need to know more than that one therapy, teaching method, marketing campaign, or medication that appears to be better than another; they often need evidence of how much better it is (estimated effect size). The purpose of this book is to inform a broad readership - broad with respect to fields of research and extent of knowledge of general statistics - about a variety of measures and estimators of effect sizes for research, their proper applications and interpretations, and their limitations. It focuses on both analyzing post-research results in terms of size of the obtained effects and also the analysis of data from an individual piece of research (called primary research). The text also deals with a broad variety of kinds of effect sizes for diverse variables, designs, circumstances, and purpose; cites alternative viewpoints; pays much attention to the statistical assumptions of methods; and includes more than 300 references. The approach encompasses detailed discussions of standardized differences between measures, some of the correlational measures, strength of association, confidence intervals, other common methods, and less-known measures, such as stochastic superiority. Effect Sizes for Research: A Broad Practical Approach is intended as a supplement for graduate courses in statistics in the fields of psychology, education, the social sciences, business, management, and medicine. It is also a valuable source for professional researchers, graduate students who are analyzing data for a master's or doctoral thesis, or advanced undergraduates.