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Designing Experiments and Analyzing Data: A Model Comparison Perspective, Third Edition
Designing Experiments and Analyzing A Model Comparison Perspective (3rd edition) offers an integrative conceptual framework for understanding experimental design and data analysis. Maxwell, Delaney, and Kelley first apply fundamental principles to simple experimental designs followed by an application of the same principles to more complicated designs. Their integrative conceptual framework better prepares readers to understand the logic behind a general strategy of data analysis that is appropriate for a wide variety of designs, which allows for the introduction of more complex topics that are generally omitted from other books. Numerous pedagogical features further facilitate examples of published research demonstrate the applicability of each chapter’s content; flowcharts assist in choosing the most appropriate procedure; end-of-chapter lists of important formulas highlight key ideas and assist readers in locating the initial presentation of equations; useful programming code and tips are provided throughout the book and in associated resources available online, and extensive sets of exercises help develop a deeper understanding of the subject. Detailed solutions for some of the exercises and realistic data sets are included on the website ( DesigningExperiments.com ). The pedagogical approach used throughout the book enables readers to gain an overview of experimental design, from conceptualization of the research question to analysis of the data. The book and its companion website with web apps, tutorials, and detailed code are ideal for students and researchers seeking the optimal way to design their studies and analyze the resulting data.
1080 pages, Hardcover
First published January 1, 1990
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Displaying 1 - 4 of 4 reviews
February 4, 2020
This book is a resource and reference book rather than a book you read from cover to cover. However, during the course of my Ph.D. studies I probably read every word at some point. The authors explain very technical details with some mathematics but a good theoretical discussion as well, and even if the mathematics can get overwhelming at times, the explanations were understandable. This book was especially helpful to me in working out which test of the family of ANOVAs, Chi Squares, or t-tests and their parametric versions was appropriate given the particular tests I wanted to apply, and in understanding whether the results were meaningful or not in a practical sense. A newer edition has been released and I`ve found this book so useful that I`ve bought the new edition in order to keep up with some advances in the field of statistics. It really helps to be proficient in statistical software, such as SPSS, so that concepts can be checked for oneself to help learn.
November 23, 2013
Full disclosure: I cannot speak to Chapters 15 or 16, as they were not part of my course. It was an advanced graduate level course on analysis of variance.
This is probably the clearest and most thorough statistics textbook I've ever come across. It tackles analysis of variance from the ground up, presenting it in terms of the statistical model comparisons that underlie stats packages like SPSS or SAS (and the theory that built them) and in this way demonstrating the ultimate cohesion of all analyses, for any design, based on the general linear model. Maxwell and Delaney write with impressive patience and clarity on increasingly challenging topics-- each one is broken down in turn and shown to be a logical and mathematical extension of the basic concepts. Examples are used throughout to illustrate concepts, and exercises are given at the end of every chapter. Moreover, syntax for stats packages is occasionally provided.
Though the course was heck of tough, it was also incredibly rewarding, and this textbook perfectly complemented the lectures and assignments to ease my understanding. I had only two small complaints about the text. First, that it grows a bit repetitive in extensions from lower- to higher-order designs of the same type; while I understand they were trying to be as explicit as possible, it felt redundant at times. Second, especially further on, that some sections involved drastic leaps in complexity certain to flummox readers with a lesser grasp on the materials. I came to this book with several upper-level statistics courses under my belt, but the book is meant to be used with undergraduates as well. Even so, the optional endnotes regularly flummoxed me, and I found myself wishing they were written with just a touch more consideration for readers without mathematical backgrounds-- I was terribly interested by the ideas, but often could not follow the maths.
Aside from those two details, however, I found an unexpected enjoyment in learning from this book, and would recommend it as required reading (or at least required owning, for reference) for any graduate student in psychology.
This is probably the clearest and most thorough statistics textbook I've ever come across. It tackles analysis of variance from the ground up, presenting it in terms of the statistical model comparisons that underlie stats packages like SPSS or SAS (and the theory that built them) and in this way demonstrating the ultimate cohesion of all analyses, for any design, based on the general linear model. Maxwell and Delaney write with impressive patience and clarity on increasingly challenging topics-- each one is broken down in turn and shown to be a logical and mathematical extension of the basic concepts. Examples are used throughout to illustrate concepts, and exercises are given at the end of every chapter. Moreover, syntax for stats packages is occasionally provided.
Though the course was heck of tough, it was also incredibly rewarding, and this textbook perfectly complemented the lectures and assignments to ease my understanding. I had only two small complaints about the text. First, that it grows a bit repetitive in extensions from lower- to higher-order designs of the same type; while I understand they were trying to be as explicit as possible, it felt redundant at times. Second, especially further on, that some sections involved drastic leaps in complexity certain to flummox readers with a lesser grasp on the materials. I came to this book with several upper-level statistics courses under my belt, but the book is meant to be used with undergraduates as well. Even so, the optional endnotes regularly flummoxed me, and I found myself wishing they were written with just a touch more consideration for readers without mathematical backgrounds-- I was terribly interested by the ideas, but often could not follow the maths.
Aside from those two details, however, I found an unexpected enjoyment in learning from this book, and would recommend it as required reading (or at least required owning, for reference) for any graduate student in psychology.
February 7, 2008
I'll admit that I'm a little biased, since my dad is the author. ;)
September 22, 2014
It is a great book, but coming from a technical background I had a challenge to really have a deep understanding of it, classes help actually to discuss it with other classmates.
Displaying 1 - 4 of 4 reviews