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Applied Multivariate Statistics for the Social Sciences, Fifth Edition

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This best-selling text is written for those who use, rather than develop statistical methods. Dr. Stevens focuses on a conceptual understanding of the material rather than on proving results. Helpful narrative and numerous examples enhance understanding and a chapter on matrix algebra serves as a review. Annotated printouts from SPSS and SAS indicate what the numbers mean and encourage interpretation of the results. In addition to demonstrating how to use these packages, 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 exercises including answers to half.

The new edition features:

  • New chapters on Hierarchical Linear Modeling (Ch. 15) and Structural Equation Modeling (Ch. 16)
  • New exercises that feature recent journal articles to demonstrate the actual use of multiple regression (Ch. 3), MANOVA (Ch. 5), and repeated measures (Ch. 13)
  • A new appendix on the analysis of correlated observations (Ch. 6)
  • Expanded discussions on obtaining non-orthogonal contrasts in repeated measures designs with SPSS and how to make the identification of cell ID easier in log linear analysis in 4 or 5 way designs
  • Updated versions of SPSS (15.0) and SAS (8.0) are used throughout the text and introduced in chapter 1
  • A book website with data sets and more.

Ideal for courses on multivariate statistics found in psychology, education, sociology, 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. Working knowledge of matrix algebra is not assumed.

Table of Contents

1. Introduction. 2. Matrix Algebra. 3. Multiple Regression. 4. Two-Group Multivariate Analysis of Variance. 5. K-Group MANOVA: A Priori and Post Hoc Procedures. 6. Assumptions in MANOVA. 7. Discriminant Analysis. 8. Factorial Analysis of Variance. 9. Analysis of Covariance. 10. Stepdown Analysis. 11. Exploratory and Confirmatory Factor Analysis. 12. Canonical Correlation. 13. Repeated Measures Analysis. 14. Categorical Data Analysis: The Log Linear Model. 15. N. Beretvas, Hierarchical Linear Modeling. 16. L.R. Fabrigar & D.T. Wegener Structural Equation Modeling. Appendix A. Statistical Tables. Appendix B. Obtaining Nonorthogonal Contrasts in Repeated Measures Designs. Answer Section.

Reviews

"Of all the texts I have ever used, this is one of the very best… Students find the book to be extremely understandable … [and] nearly all keep [it] for reference purposes…It really is a great applied treatment of the topics… the examples are general enough to appeal to students across disciplines … The … computer examples are very helpful…an extraordinarily balanced text by a highly respected author." - Dale R. Fuqua, Oklahoma State University

"It is the best text I have found on Multivariate stats… Including examples in journals is a great addition… the book's … greatest strengths [include] comprehensive coverage of the analyses, thorough description and discussion of the assumptions for the analyses, and annotated SPSS print-outs." - Philip Schatz, Saint Joseph's University

"The book's primary strength is its exceptionally clear, even engaging, prose." - Louis M Kyriakoudes, University of Southern Mississippi