Designing Experiments and Analyzing Data: A Model Comparison Perspective, Second Edition (Hardcover)

by Scott E. Maxwell (Author), Harold D. Delaney (Author)

Editorial Reviews

Review

Overall, this is an excellent resource for those designing and analyzing experiments, and for those wishing to consolidate their knowledge of individual designs into a unified conceptual framework.
—American Statistician

Overall, this is an excellent resource for those designing and analyzing experiments, and for those wishing to consolidate their knowledge of individual designs into a unified conceptual framework.
—American Statistician

Product Description
Advanced UG/grad lvl txt or ref book for experimental design in psych, ed, & stats depts. N/E updated throughout to reflect recent developments.


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Product Details

• Hardcover: 920 pages
• Publisher: Lawrence Erlbaum; 2 edition (September 2003)
• Language: English
• ISBN-10: 0805837183
• ISBN-13: 978-0805837186
• Product Dimensions: 10 x 7.3 x 1.8 inches
• Shipping Weight: 4.2 pounds (View shipping rates and policies)
• Average Customer Review: 4.2 out of 5 stars See all reviews (6 customer reviews)
• Amazon.com Sales Rank: #153,263 in Books (See Bestsellers in Books)

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Customer Reviews

Most Helpful Customer Reviews

26 of 26 people found the following review helpful:

5.0 out of 5 stars This is a scholarly presentation of a difficult subject., April 29, 1999

By	[e-mail] (Baltimore) - See all my reviews

This review is from: Designing Experiments and Analyzing Data: A Model Comparison Perspective (Hardcover)

I continue to select this text for a graduate course in advanced experimental design. The book is not easy, and that is one reason why I like it. It presents a thoughtful scholarship on controversial issues pertaining to the use of inferential statistics as a tool to assist the researcher in making decisions about the validity and strength of functional relationships present in the data. The book eliminates the naive overconfidence that sometimes results from cookbook applications of algorithms to the results of experiments. It shows that the process of discovery and data analysis is never complete and that the general linear model is an imperfect technique by which to discern orderliness in nature where intersubject variability is a given.

22 of 24 people found the following review helpful:

5.0 out of 5 stars Fabulous book, June 11, 1999

By	SeanFurl (San Francisco) - See all my reviews

This review is from: Designing Experiments and Analyzing Data: A Model Comparison Perspective (Hardcover)

This is a practitioner's book, not a scolar's. I'm not a scolar -- never have been and never will be. I love this book. It's lucid, it's sensible and it's great statistics. It goes thoroughly into the logic of linear model ANOVAs yet the bulk of the exposition is in the simple English language -- it has no calculus and no eigenvectors and almost no matrix algebra. You need an acquaintance with elementary one-way ANOVA but no more background than that. The authors pace it carefully and are not afraid of a bit of repetition for the sake of clarity (something I always appreciate when I'm reading new technical material). It's one of the best $105 dollars I've ever spent. I got loads out of it all the way through and it's a big book. The explanation of multivariate repeated measures is worth the price alone.

2 of 2 people found the following review helpful:

5.0 out of 5 stars Second Edition, June 17, 2006

By	Mr. Teacherman - See all my reviews

This edition is similar to the previous one. It is without peer in terms of scholarship. It is also not easy to read and digest, and that's the way it should be for a serious treatment of research methods. After all, the applications of such methods and the interpretation of results can profoundly affect our lives.