# Applied Multivariate Statistics for the Social Sciences, Fifth Edition

Routledge, Nov 12, 2012 - Education - 664 pages

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.

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### Contents

 1 Introduction 1 2 Matrix Algebra 43 3 Multiple Regression 63 4 TwoGroup Multivariate Analysis of Variance 145 A Priori and Post Hoc Procedures 177 6 Assumptions in MANOVA 217 7 Discriminant Analysis 245 8 Factorial Analysis of Variance 271
 12 Canonical Correlation 395 13 RepeatedMeasures Analysis 413 The Log Linear Model 463 15 Hierarchical Linear Modeling 505 16 Structural Equation Modeling 537 References 583 Statistical Tables 597 Obtaining Nonorthogonal Contrasts in Repeated Measures Designs 617

 9 Analysis of Covariance 287 10 Stepdown Analysis 315 11 Exploratory and Confirmatory Factor Analysis 325