Contrasts and Effect Sizes in Behavioral Research: A Correlational Approach [Paperback]

Robert Rosenthal (Author), Ralph L. Rosnow (Author), Donald B. Rubin (Author)

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Product Details

Paperback: 224 pages
Publisher: Cambridge University Press; 1 edition (Dec 28 1999)
Language: English
ISBN-10: 0521659809
ISBN-13: 978-0521659802
Product Dimensions: 1.7 x 2.4 x 0.1 cm
Shipping Weight: 458 g
Amazon Bestsellers Rank: #597,915 in Books (See Top 100 in Books)

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Review

"A milestone. This book is bound to remain the definitive word on contrast analysis for many years to come." Miron Zuckerman, University of Rochester

"This is a great boook that everybody who uses ANOVA techniques should read...it could definitely be used in courses for advanced undergraduates and graduate students; even experienced researchers will find that they still can learn a lot from it." APA Review of Books

"Rosenthal, Rosnow, and Rubin have delivered as promised. The book is written in a lively style and fulfills expectations. It will add to our body of knowledge in proportion to its use by researchers, teachers, and students." Malcolm James Ree, Personal Psychology

Book Description

Contrasts are statistical procedures for asking focused questions of data. Researchers, teachers of research methods and graduate students will be familiar with the principles and procedures of contrast analysis included here. But they, for the first time, will also be presented with a series of newly developed concepts, measures, and indices that permit a wider and more useful application of contrast analysis. This volume takes on this new approach by introducing a family of correlational effect size estimates. By returning to these correlations throughout the book, the authors demonstrate special adaptations in a variety of contexts from two group comparison to one way analysis of variance contexts, to factorial designs, to repeated measures designs and to the case of multiple contrasts.

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Amazon.com: 3.0 out of 5 stars (1 customer review)

12 of 12 people found the following review helpful

3.0 out of 5 stars Good topic, needed debate., Aug 12 2004

By James Daniels - Published on Amazon.com

This review is from: Contrasts and Effect Sizes in Behavioral Research: A Correlational Approach (Paperback)

Omnibus tests of statistical significance, and in particular null hypothesis tests, have fallen under continuous criticism since Fisher first promulgated the analysis of variance in 1935. Despite the often contentious debate, few practical alternatives have been proposed in the literature. Rosenthal, Rosnow, and Rubin have changed that, with their discussion of contrast analysis and effect sizes.

Plus side: The text gives in depth coverage on the design and conduct of contrast analysis, testing particular theoretical predictions using more general data sets. A good example from the book is the influence of age on performance in a video game: do older children perform better than younger children? With five age groups sampled, a standard ANOVA might inform you that the data are very likely close to what is expected from random sampling. However, the ANOVA treats all 120 possible orders of the age groups as irrelevant to your theory. In fact, there is only one ordering that is important to your theory, but ANOVA can't test it! Contrast analysis can. The authors also offer several novel descriptive statistics, using regression and correlation, that can inform you about relationships in a data set that ordinary omnibus tests can't detect. This approach is a serious improvement over the advice to merely use confidence intervals, or the positively primitive reliance on omnibus null hypothesis tests.

That said, the book has a few defects. First, it assume the reader has a solid grasp on ANOVA, regression, and inferential testing generally. It is thus an advanced text. For an introductory text I recommend Howitt & Cramer's An Introduction to Statistics in Psychology: A Complete Guide for Students.

The text is also quite mathematically oriented, and does not have adequate expositions of all the computations used across the book. If you're not comfortable with inferential statistics and mathematical notations, the book will be a ponderous read. Finally, the book gives very short shrift to the history of omnibus tests and ways they are misinterpreted. On this score, I recommend Gigerenzer's Empire of Chance, and Oakes' Statistical Inference. The latter is now unfortunately out of print.

It is clear that complex theories generate complex predictions, and can be tested using only complex tools applied to large data sets. Omnibus tests like ANOVA are clearly inadequate to the task. Contrast analysis is a novel and potentially rich approach to this problem.

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