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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 understanding:
*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 features:
*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.
"synopsis" may belong to another edition of this title.
Scott E. Maxwell is the Fitzsimons Professor of Psychology at the University of Notre Dame. His research interests are in the areas of research methodology and applied behavioral statistics, with much of his recent work focusing on statistical power and accuracy in parameter estimation, especially in randomized designs. Professor Maxwell has served as editor of Psychological Methods, received the Samuel J. Messick Award for Distinguished Scientific Contributions by the American Psychological Association’s Division of Evaluation, Measurement, & Statistics, and has received multiple teaching awards.
Harold D. Delaney is Emeritus Professor of Psychology at the University of New Mexico, where he received the University’s Outstanding Graduate Teacher of the Year award. His research interests in applied statistics include methods that accommodate individual differences among people. Professor Delaney received a Fulbright Award from the U.S. Department of State to spend an academic year lecturing in Budapest, Hungary.
Ken Kelley is Professor of Information Technology, Analytics, and Operations (ITAO) and the Associate Dean for Faculty and Research in the Mendoza College of Business at the University of Notre Dame. His work is on quantitative methodology, where he focuses on the development, improvement, and evaluation of statistical methods and measurement issues. Professor Kelley is an Accredited Professional Statistician (PStat®), recipient of the Anne Anastasi early career award by the APA's Division of Evaluation, Measurement, & Statistics, a fellow of the American Psychological Association, and an award-winning teacher.
"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."
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