Research Design and Statistical Analysis

Research Design and Statistical Analysis

Research Design and Statistical Analysis

Research Design and Statistical Analysis

Synopsis

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.

Excerpt

In writing this book, we had two overriding goals. The first was to provide a textbook from which graduate and advanced undergraduate students could really learn about data analysis. Over the years we have experimented with various organizations of the content and have concluded that bottom-up is better than top-down learning. In view of this, most chapters begin with an informal intuitive discussion of key concepts to be covered, followed by the introduction of a real data set along with some informal discussion about how we propose to analyze the data. At that point, having given the student a foundation on which to build, we provide a more formal justification of the computations that are involved both in exploring and in drawing conclusions about the data, as well as an extensive discussion of the relevant assumptions. The strategy of bottom-up presentation extends to the organization of the chapters. Although it is tempting to begin with an elegant development of the general linear model and then treat topics such as the analysis of variance as special cases, we have found that students learn better when we start with the simpler, less abstract, special cases, and then work up to more general formulations. Therefore, after we develop the basics of statistical inference, we treat the special case of analysis of variance in some detail before developing the general regression approach. Then, the now-familiar analyses of variance, covariance, and trend are reconsidered as special cases. We feel that learning statistics involves many passes; that idea is embodied in our text, with each successive pass at a topic becoming more general.

Our second goal was to provide a source book that would be useful to researchers. One implication of this is an emphasis on concepts and assumptions that are necessary to describe and make inferences about real data. Formulas and statistical packages are not enough. Almost anybody can run statistical analyses with a user-friendly statistical package. However, it is critically important to understand what the analyses really tell us, as well as their limitations and their underlying assumptions. No text can present every design and analysis that researchers will encounter in their own research or in their readings of the research literature. In view of this, we build a conceptual foundation that should permit the reader to generalize to new situations, to comprehend the advice of statistical consultants.

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