Research Design and Statistical Analysis / Edition 1by Jerome L. Myers, Arnold D. Well
Pub. Date: 01/28/1995
Publisher: Taylor & Francis
This volume provides an extensive treatment of important inferential concepts, experimental designs and related analyses, and regression and correlation analyses. Emphasizing such basic concepts as sampling distributions, expected mean squares, design efficiency, and statistical models, the authors detail the assumptions underlying statistical procedures, the… See more details below
This volume provides an extensive treatment of important inferential concepts, experimental designs and related analyses, and regression and correlation analyses. Emphasizing such basic concepts as sampling distributions, expected mean squares, design efficiency, and statistical models, the authors detail the assumptions underlying statistical procedures, the consequences of their violations, methods for detecting those violations, and alternative methods in the face of severe violations. Written for researchers engaged in experimentation and those who conduct observational studies, this volume treats both analysis of variance and regression analysis, and relates the two. And because the assumptions underlying such procedures are often not met by the researcher's data, nonparametric methods are also set forth. Features include:
• comprehensive coverage that provides the reader with an understanding of the appropriate statistical analyses for many of the research designs that are likely to be encountered;
• emphasis on underlying concepts rather than on computations to promote understanding and enable the reader to generalize new applications;
• a bottom-up organization that provides concrete examples and intuitive arguments before going into more complex explanations to give readers overviews of major topics that encourage understanding;
• extensive sets of exercises at the ends of chapters and answers to selected exercises to allow readers to test understanding of concepts as well as the ability to perform calculations;
• the development of the basic ideas of multiple regression analysis without using matrix algebra plus an optional section that illustrates the power of the matrix approach to make regression analysis accessible to a wide audience; and
• discussions of available computer statistical packages, examples of outputs and an appendix about the control information for running the statistical programs to help researchers select a computer package and interpret the output.
Table of ContentsContents: Preface. Preliminary Considerations. Samples and Populations. Some Important Distributions. Between-Subjects Designs: One Factor. Between-Subjects Designs: Several Factors. Contrasts Among Means. Trend Analysis. Repeated-Measures Designs. Mixed Designs: Combining Between-Subject and Within-Subjects Factors. Hierarchical Designs. Latin Squares and Related Designs. Bivariate Correlation and Regression. Analysis of Covariance. More About Correlation. Multiple Regression. Regression With Categorical Variables. Appendices: Notation and Summation Operations. Expected Values and Their Applications. Matrix Algebra. Statistical Tables. Control Information for Computer Programs for Statistical Analysis.
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