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

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: *flowchartsassist in choosing the most appropriate technique; *an equation cross-referencing systemaids in locating the initial, detailed definition andnumerous summary equation tablesassist readers in understanding differences between different methods for analyzing their data; *examples based on actual researchin a variety of behavioral sciences help students see the applications of the material; *numerous exerciseshelp develop a deeper understanding of the subject.Detailed solutionsare provided for some of the exercises and *realistic data setsallow 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 designscovered in the book (ch. 2); *a CD with SPSS and SAS data setsfor many of the text exercises, as well astutorials reviewing basic statisticsandregression;and *a Web sitecontaining examples ofSPSS and SASsyntax 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. AnInstructor's Solutions Manualis available to those who adopt the book for classroom use.