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.