Tenko Raykov is a Professor of Measurement and Quantitative Methods at Michigan State University. He received his Ph.D. in Mathematical Psychology from Humboldt University in Berlin. He is an editorial board member of the British Journal of Mathematical and Statistical Psychology, Multivariate Behavioral Research, Psychological Methods, and Structural Equation Modeling.George A. Marcoulides is a Professor of Research Methods and Statistics at the University of California, Riverside. He is the Editor of Structural Equation Modeling, the Quantitative Methodology Series, and an editorial board member of numerous other measurement and statistics journals.

• Preface. 1. Introduction to Multivariate Statistics. 2. Elements of Matrix Theory. 3. Data Screening and Preliminary Analyses. 4. Multivariate Analysis of Group Differences. 5. Repeated Measure Analysis of Variance. 6. Analysis of Covariance. 7. Principal Component Analysis. 8. Exploratory Factor Analysis. 9. Confirmatory Factor Analysis. 10. Discriminant Function Analysis. 11. Canonical Correlation Analysis. 12. An Introduction to the Analysis of Missing Data. 13. Multivariate Analyses of Change Processes. References. Appendix.

"This text is very well written and makes important connections between univariate and multivariate procedures..[it] allows readers to understand progressive developments that build on previously established foundations... [and] provides a good conceptual understanding of multivariate procedures." Tim Konold, University of Virginia, USA "The writing style...is characterized by simplicity and clarity in explaining complex concepts... the main readers will be … postgraduate students in quantitative sciences...[and] researchers in engineering, commerce, medicine, or applied science who... desperately want to get meaningful answers to statistical inference questions using their own data sets." Spiridon Penev, University of New South Wales, Australia"I particularly enjoyed the examples...[they] clearly illustrate the points the authors were trying to convey...the chapter on ANCOVA was particularly well-written and covered the logic (which is confusing in some texts) of ANCOVA in a clear manner that was easy to follow." Douglas Steinley, University of Missouri, USA

Tenko Raykov is a Professor of Measurement and Quantitative Methods at Michigan State University. He received his Ph.D. in Mathematical Psychology from Humboldt University in Berlin. He is an editorial board member of the British Journal of Mathematical and Statistical Psychology, Multivariate Behavioral Research, Psychological Methods, and Structural Equation Modeling.George A. Marcoulides is a Professor of Research Methods and Statistics at the University of California, Riverside. He is the Editor of Structural Equation Modeling, the Quantitative Methodology Series, and an editorial board member of numerous other measurement and statistics journals.

• Preface. 1. Introduction to Multivariate Statistics. 2. Elements of Matrix Theory. 3. Data Screening and Preliminary Analyses. 4. Multivariate Analysis of Group Differences. 5. Repeated Measure Analysis of Variance. 6. Analysis of Covariance. 7. Principal Component Analysis. 8. Exploratory Factor Analysis. 9. Confirmatory Factor Analysis. 10. Discriminant Function Analysis. 11. Canonical Correlation Analysis. 12. An Introduction to the Analysis of Missing Data. 13. Multivariate Analyses of Change Processes. References. Appendix.

"This text is very well written and makes important connections between univariate and multivariate procedures..[it] allows readers to understand progressive developments that build on previously established foundations... [and] provides a good conceptual understanding of multivariate procedures." Tim Konold, University of Virginia, USA "The writing style...is characterized by simplicity and clarity in explaining complex concepts... the main readers will be … postgraduate students in quantitative sciences...[and] researchers in engineering, commerce, medicine, or applied science who... desperately want to get meaningful answers to statistical inference questions using their own data sets." Spiridon Penev, University of New South Wales, Australia"I particularly enjoyed the examples...[they] clearly illustrate the points the authors were trying to convey...the chapter on ANCOVA was particularly well-written and covered the logic (which is confusing in some texts) of ANCOVA in a clear manner that was easy to follow." Douglas Steinley, University of Missouri, USA

Tenko Raykov is a Professor of Measurement and Quantitative Methods at Michigan State University. He received his Ph.D. in Mathematical Psychology from Humboldt University in Berlin. He is an editorial board member of the British Journal of Mathematical and Statistical Psychology, Multivariate Behavioral Research, Psychological Methods, and Structural Equation Modeling.George A. Marcoulides is a Professor of Research Methods and Statistics at the University of California, Riverside. He is the Editor of Structural Equation Modeling, the Quantitative Methodology Series, and an editorial board member of numerous other measurement and statistics journals.

• Preface. 1. Introduction to Multivariate Statistics. 2. Elements of Matrix Theory. 3. Data Screening and Preliminary Analyses. 4. Multivariate Analysis of Group Differences. 5. Repeated Measure Analysis of Variance. 6. Analysis of Covariance. 7. Principal Component Analysis. 8. Exploratory Factor Analysis. 9. Confirmatory Factor Analysis. 10. Discriminant Function Analysis. 11. Canonical Correlation Analysis. 12. An Introduction to the Analysis of Missing Data. 13. Multivariate Analyses of Change Processes. References. Appendix.

"This text is very well written and makes important connections between univariate and multivariate procedures..[it] allows readers to understand progressive developments that build on previously established foundations... [and] provides a good conceptual understanding of multivariate procedures." Tim Konold, University of Virginia, USA "The writing style...is characterized by simplicity and clarity in explaining complex concepts... the main readers will be … postgraduate students in quantitative sciences...[and] researchers in engineering, commerce, medicine, or applied science who... desperately want to get meaningful answers to statistical inference questions using their own data sets." Spiridon Penev, University of New South Wales, Australia"I particularly enjoyed the examples...[they] clearly illustrate the points the authors were trying to convey...the chapter on ANCOVA was particularly well-written and covered the logic (which is confusing in some texts) of ANCOVA in a clear manner that was easy to follow." Douglas Steinley, University of Missouri, USA