Now in its 6th edition, the authoritative textbook Applied Multivariate Statistics for the Social Sciences, continues to provide advanced students with a practical and conceptual understanding of statistical procedures through examples and data-sets from actual research studies. With the added expertise of co-author Keenan Pituch (University of Texas-Austin), this 6th edition retains many key features of the previous editions, including its breadth and depth of coverage, a review chapter on matrix algebra, applied coverage of MANOVA, and emphasis on statistical power. In this new edition, the authors continue to provide practical guidelines for checking the data, assessing assumptions, interpreting, and reporting the results to help students analyze data from their own research confidently and professionally. Features new to this edition include: * NEW chapter on Logistic Regression (Ch. 11) that helps readers understand and use this very flexible and widely used procedure * NEW chapter on Multivariate Multilevel Modeling (Ch. 14) that helps readers understand the benefits of this "newer" procedure and how it can be used in conventional and multilevel settings * NEW Example Results Section write-ups that illustrate how results should be presented in research papers and journal articles * NEW coverage of missing data (Ch. 1) to help students understand and address problems associated with incomplete data * Completely re-written chapters on Exploratory Factor Analysis (Ch. 9), Hierarchical Linear Modeling (Ch. 13), and Structural Equation Modeling (Ch. 16) with increased focus on understanding models and interpreting results * NEW analysis summaries, inclusion of more syntax explanations, and reduction in the number of SPSS/SAS dialogue boxes to guide students through data analysis in a more streamlined and direct approach * Updated syntax to reflect newest versions of IBM SPSS (21) /SAS (9.3) * A free companion website www.routledge.com/9780415836661 with data sets and instructor's resources. Ideal for advanced graduate-level courses in education, psychology, and other social sciences in which multivariate statistics, advanced statistics, or quantitative techniques courses are taught, this book also appeals to practicing researchers as a valuable reference. Pre-requisites include a course on factorial ANOVA and covariance; however, a working knowledge of matrix algebra is not assumed.
Contents Preface 1. Introduction 2. Matrix Algebra 3. Multiple Regression for Prediction 4. Two-Group Multivariate Analysis of Variance 5. K-Group MANOVA: A Priori and Post-Hoc Procedures 6. Assumptions in MANOVA 7. Factorial ANOVA and MANOVA 8. Analysis of Covariance 9. Exploratory Factor Analysis 10. Discriminant Analysis 11. Binary Logistic Regression 12. Repeated-Measures Analysis 13. Hierarchical Linear Modeling 14. Multivariate Multilevel Modeling 15. Canonical Correlation 16. Structural Equation Modeling Appendix A: Statistical Tables Appendix B: Obtaining Nonorthogonal Contrasts in Repeated Measures Design Answers Index