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Designing Experiments and Analyzing Data: A Model Comparison Perspective
Through this book's unique model comparison approach, students and researchers are introduced to a set of fundamental principles for analyzing data. After seeing how these principles can be applied in simple designs, students are shown how these same principles also apply in more complicated designs.
Drs. Maxwell and Delaney believe that the model comparison approach better prepares students to understand the logic behind a general strategy of data analysis appropriate for various designs; and builds a stronger foundation, which allows for the introduction of more complex topics omitted from other books.
Several learning tools further strengthen the reader's
*flowcharts assist in choosing the most appropriate technique;
*an equation cross-referencing system aids in locating the initial, detailed definition and numerous summary equation tables assist readers in understanding differences between different methods for analyzing their data;
*examples based on actual research in a variety of behavioral sciences help students see the applications of the material;
*numerous exercises help develop a deeper understanding of the subject. Detailed solutions are provided for some of the exercises and *realistic data sets allow the reader to see an analysis of data from each design in its entirety.
Updated throughout, the second edition
*significantly increased attention to measures of effects, including confidence intervals, strength of association, and effect size estimation for complex and simple designs;
*an increased use of statistical packages and the graphical presentation of data;
*new chapters (15 & 16) on multilevel models;
*the current controversies regarding statistical reasoning, such as the latest debates on hypothesis testing (ch. 2);
*a new preview of the experimental designs covered in the book (ch. 2);
*a CD with SPSS and SAS data sets for many of the text exercises, as well as tutorials reviewing basic statistics and regression; and
*a Web site containing examples of SPSS and SAS syntax for analyzing many of the text exercises.
Appropriate for advanced courses on experimental design or analysis, applied statistics, or analysis of variance taught in departments of psychology, education, statistics, business, and other social sciences, the book is also ideal for practicing researchers in these disciplines. A prerequisite of undergraduate statistics is assumed. An Instructor's Solutions Manual is available to those who adopt the book for classroom use.
Drs. Maxwell and Delaney believe that the model comparison approach better prepares students to understand the logic behind a general strategy of data analysis appropriate for various designs; and builds a stronger foundation, which allows for the introduction of more complex topics omitted from other books.
Several learning tools further strengthen the reader's
*flowcharts assist in choosing the most appropriate technique;
*an equation cross-referencing system aids in locating the initial, detailed definition and numerous summary equation tables assist readers in understanding differences between different methods for analyzing their data;
*examples based on actual research in a variety of behavioral sciences help students see the applications of the material;
*numerous exercises help develop a deeper understanding of the subject. Detailed solutions are provided for some of the exercises and *realistic data sets allow the reader to see an analysis of data from each design in its entirety.
Updated throughout, the second edition
*significantly increased attention to measures of effects, including confidence intervals, strength of association, and effect size estimation for complex and simple designs;
*an increased use of statistical packages and the graphical presentation of data;
*new chapters (15 & 16) on multilevel models;
*the current controversies regarding statistical reasoning, such as the latest debates on hypothesis testing (ch. 2);
*a new preview of the experimental designs covered in the book (ch. 2);
*a CD with SPSS and SAS data sets for many of the text exercises, as well as tutorials reviewing basic statistics and regression; and
*a Web site containing examples of SPSS and SAS syntax for analyzing many of the text exercises.
Appropriate for advanced courses on experimental design or analysis, applied statistics, or analysis of variance taught in departments of psychology, education, statistics, business, and other social sciences, the book is also ideal for practicing researchers in these disciplines. A prerequisite of undergraduate statistics is assumed. An Instructor's Solutions Manual is available to those who adopt the book for classroom use.
920 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