## 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. |

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#### Review: Designing Experiments and Analyzing Data: A Model Comparison Perspective

User Review - Ohud - GoodreadsIt 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. Read full review

#### Review: Designing Experiments and Analyzing Data: A Model Comparison Perspective

User Review - GoodreadsIt 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. Read full review

### Contents

Threats to the Validity of Inferences from Experiments | 25 |

Introduction to the Fisher Tradition | 38 |

Exercises | 56 |

Copyright | |

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absent adjusted analysis ANCOVA angle appropriate assume average between-subjects design biofeedback Bonferroni calculated cell means Chapter condition consider correlation covariate critical F critical value degrees of freedom denominator degrees dependent variable deviation diet difference discussed distribution drug equal Equation F statistic F test factorial design formula full model grand mean homogeneity of variance Kruskal-Wallis test linear trend main effect marginal means matrix mean square method mixed-model approach multivariate approach multivariate test noise null hypothesis number of subjects observed F value obtained omnibus test one-way design orthogonal pairwise comparisons parameters performed population means predicted present procedure quadratic trend random factor regression restricted model sample means sample sizes scores separate error term simple effects simply slope specific split-plot design squared errors statistically significant sum of squares test statistic therapy treatment effect Type I error Type III sum within-subjects design within-subjects factor yields zero