Intended both as a textbook for students and as a resource for researchers, 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 authors adopt a "bottom-up" approach--a 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.
The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education