Designing Experiments and Analyzing Data: A Model Comparison PerspectiveThrough 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. |
Contents
Trend Analysis | 20 |
Threats to the Validity of Inferences from Experiments | 25 |
Exercises | 34 |
Copyright | |
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analysis ANOVA approach appropriate assume assumption average behavioral biofeedback Bonferroni calculated cell means Chapter compute condition consider correlation covariate critical F critical value degrees of freedom denominator dependent variable deviation diet difference discussion drug therapy equal Equation error term experiment experimental F statistic F test Figure full model grand mean group means homogeneity of variance individual interaction least-squares estimates linear trend logic main effect marginal means measure nonorthogonal normally distributed null hypothesis number of subjects numerical example obtained omnibus test orthogonal pairwise comparisons parameters particular performed population means possible predicted procedure psychologist quadratic trend regression restricted model result sample means Scheffé scores shown simply slope squared errors statistically significant sum of squares Table test statistic theory treatment effect Tukey's two-way Type I error validity within-group within-subjects design Y₁ Y₁j Y₂ Yijk zero μ₁ μ₂