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This best-selling text is written for those who use, rather than develop, advanced statistical methods. Dr. Stevens focuses on a conceptual understanding of the material rather than proving results.
This best-selling text is written for those who use, rather than develop, advanced statistical methods. Dr. Stevens focuses on a conceptual understanding of the material rather than proving results.
Helpful narrative and numerous examples enhance understanding, and a chapter on matrix algebra serves as a review.
Printouts from SPSS and SAS with annotations indicate what the numbers mean and encourage interpretation of the results. In addition to demonstrating how to use the packages effectively, the author stresses the importance of checking the data, assessing the assumptions, and ensuring adequate sample size (by providing guidelines) so that the results can be generalized.
The book is noted for its extensive applied coverage of MANOVA, its emphasis on statistical power, and numerous conceptual, numerical, and computer-related exercises including answers to half.
The new edition features:
- New chapters on the Hierarchical Linear Model (Chapter 15) and Structural Equation Modeling (Chapter 16).
- New exercises that feature recent top journal articles to demonstrate the actual use of multiple regression (Chapter 3), MANOVA (Chapter 5), and repeated measures (Chapter 13).
- A new appendix on the analysis of correlated observations (Chapter 6) including excerpts from an important article by Dr. Larry Hedges.
- Expanded sections on obtaining non-orthogonal comparisons with SPSS (Chapter 13) and the labeling of each factor to make it easier to identify the cells for 4 or 5 way designs (Chapter 14).
- Updated versions of SPSS (15.0) and SAS (8.0) are used throughout the text and introduced in chapter 1.
Ideal for courses on multivariate statistics found in psychology, education, and business departments, the book also appeals to practicing researchers with little or no training in multivariate methods. Prerequisites include a course on factorial ANOVA and covariance. It does not assume a working knowledge of matrix algebra.