This book emphasizes the statistical concepts and assumptions necessary to describe and make inferences about real data. Throughout the book the authors encourage the reader to plot and examine their data, find confidence intervals, use power analyses to determine sample size, and calculate effect sizes. The goal is to ensure the reader understands the underlying logic and assumptions of the analysis and what it tells them, the limitations of the analysis, and the possible consequences of violating assumptions.
The simpler, less abstract discussion of analysis of variance is presented prior to developing the more general model. A concern for alternatives to standard analyses allows for the integration of non-parametric techniques into relevant design chapters, rather than in a single, isolated chapter. This organization allows for the comparison of the pros and cons of alternative procedures within the research context to which they apply.
Basic concepts, such as sampling distributions, expected mean squares, design efficiency, and statistical models are emphasized throughout. This approach provides a stronger conceptual foundation in order to help the reader generalize the concepts to new situations they will encounter in their research and to better understand the advice of statistical consultants and the content of articles using statistical methodology.
The second edition features a greater emphasis on graphics, confidence intervals, measures of effect size, power analysis, tests of contrasts, elementary probability, correlation, and regression. A Free CD that contains several real and artificial data sets used in the book in SPSS, SYSTAT, and ASCII formats, is included in the back of the book. An Instructor's Solutions Manual, containing the intermediate steps to all of the text exercises, is available free to adopters.
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...a masterful job of presenting important concepts in a rigorous but accessible manner. They do not...leave students without any guidelines for selecting appropriate designs....where recommendations are appropriate, they make them and explain their reasoning. Where specific recommendations are not possible, they discuss the issues relevant to choice of design or statistics....A masterful teaching job.
—Robert Lorch, Ph.D.
University of Kentucky
...the authors include examples from real data sets. In contrast to most books, which rely on 'created' data sets, this provides a framework for talking about the complications of messy data....I find that it provides me with the basis for a terrific class.
—Celia Klin, Ph.D.
Nobody else presents the level of sophistication with the clarity that Myers does.
—Thomas Petros, Ph.D.
University of North Dakota
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