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

by Scott E. Maxwell, Harold D. Delaney, Harold D Delaney

Book Overview

Designing Experiments and Analyzing Data: A Model Comparison Perspective (3rd edition) offers an integrative conceptual framework for understanding experimental design and data analysis. Maxwell,... This description may be from another edition of this product.

• Format: Hardcover
• Language: English
• ISBN: 0805837183
• ISBN13: 9780805837186
• Release Date: July 2003
• Publisher: Routledge
• Length: 1104 Pages
• Weight: 4.00 lbs.
• Dimensions: 2.5" x 7.1" x 10.2"

Customer Reviews

Excellent Textbook

Maxwell & Delaney have created one of the best textbooks for introducing the reader to advanced topics in statistics. The book gives extensive coverage in both theory and application of ANOVA, Post-hoc comparisons, ANCOVA, Factorial Design, and Multilevel Modeling. I would highly recommend this text for a graduate statistics course. I continue to use this book as a reference in my analyses and writing. While many will find this text to be heavy (and it is), it is because of the comprehensive coverage of topics. Like most statistics books, re-reading this book offers further insight and understanding.

Awesome

Maxwell and Delaney are the Sigfreid and Roy of the statistical world. Their examples are like elaborate shows meant to tantalize the senses. I only wish I could give it more than 5 stars.

Second Edition

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.

Fabulous book

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.

This is a scholarly presentation of a difficult subject.

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.