Table of Contents

About the Book

A comprehensive review of analyses of basic and complex ANOVA models through traditional approaches and multiple regression, integrating the most recent releases of MINITAB, SAS, SPSS, and SYSTAT.

In all chapters of this comprehensive text, both the basic model and its numerous complexities are presented along with discussions of effect size, relative efficiency and comparisons, illustrated by numerous examples. For each major model, the text provides tests for assumptions, a hand-worked example, and an example with real data including a write-up of the results using APA format. The text also provides data sets, syntax, and output for accomplishing numerous additional analyses through recent releases of MINITAB, SAS, SPSS and SYSTAT, often neglected in software manuals.

  • Inclusion of syntax and output from MINITAB, SAS, SPSS, and SYSTAT allows students to concentrate on the research question rather than on the specifics of the software program and provides the most thorough integration of text and software packages. The Companion Website contains datasets that accompany the text.
  • An example for each model, using real data and proceeding from assessment of assumptions through a Results section in APA format, provides students with step-by-step guidance for conducting and reporting an analysis.
  • Unique Chapter 10 on screening designs includes coverage of software to generate designs, familiarizing students with fractional factorials, Taguchi designs, Plackett-Burman designs, and response surface designs that are extremely useful in the early stages of research and/or in research with rare subjects.
  • Syntax and output for many of the complexities of analysis of covariance (Ch. 8), Latin square (Ch. 9), and random effects and nested models (Ch. 11) provide students with solutions for numerous problems that arise during research.
  • Presentation of both the traditional and multiple regression approaches to the basic model in Chapters 4-11 using the same example provides students with an alternative approach to ANOVA that offers much greater flexibility in understanding and analyzing a data set.

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