Applied Multivariate Analysis

Like most academic authors, my views are a joint product of my teaching and my research. Needless to say, my views reflect the biases that I have acquired. One way to articulate the rationale (and limitations) of my biases is through the preface of a truly great text of a previous era, Cooley and Lohnes (1971, p. v). They draw a distinction between mathematical statisticians whose intel­ lect gave birth to the field of multivariate analysis, such as Hotelling, Bartlett, and Wilks, and those who chose to "concentrate much of their attention on methods of analyzing data in the sciences and of interpreting the results of statistical analysis . . . . (and) . . . who are more interested in the sciences than in mathematics, among other characteristics. " I find the distinction between individuals who are temperamentally "mathe­ maticians" (whom philosophy students might call "Platonists") and "scientists" ("Aristotelians") useful as long as it is not pushed to the point where one assumes "mathematicians" completely disdain data and "scientists" are never interested in contributing to the mathematical foundations of their discipline. I certainly feel more comfortable attempting to contribute in the "scientist" rather than the "mathematician" role. As a consequence, this book is primarily written for individuals concerned with data analysis. However, as noted in Chapter 1, true expertise demands familiarity with both traditions. The Book Is A Basic Graduate Level Textbook In Multivariate Analysis. It Is Designed To Emphasize The Problems Of Analyzed Data As Opposed To Testing Formal Models. One Of The Most Important Is A Discussion Of The Connection Between Mathematical Techniques And Substantial Issues. Simulation Is Given A Prominent Role. Topical Content Is Standard Except For A Chapter Devoted To The Analysis Of Scales, An Important Issue For Clinical And Social Psychologists. Students Can Learn How To Evaluate Issues Of Interest To Them. Emphasis Is Also Placed On How Not To Become Overwhelmed By The Complexities Of Computer Printouts. The Single Most Important Part Of The Book Is That The Author Attempts To Address The Reader In Clear Language, Not Mathematics. Considerable Care Was Devoted To Presenting Examples That Readers Will Find Meaningful. Contents: Introduction And Preview -- Some Basic Statistical Concepts -- Some Matrix Concepts -- Multiple Regression And Multiple Correlation - Basic Concepts -- Multiple Regression And Correlation - Advanced Applications -- Exploratory Factor Analysis -- Confirmatory Factor Analysis -- Classification Methods: Forming Discriminant Axes -- Classification Methods: Methods Of Assignment -- Classification Methods - Inferential Considerations In The Manova -- Profile And Canonical Analysis -- Analysis Of Scales -- Appendices -- Problems -- References -- Author Index -- Subject Index. By Ira H. Bernstein, Calvin P. Garbin, Gary K. Teng. Like most academic authors, my views are a joint product of my teaching and my research. Needless to say, my views reflect the biases that I have acquired. One way to articulate the rationale (and limitations) of my biases is through the preface of a truly great text of a previous era, Cooley and Lohnes (1971, p. v). They draw a distinction between mathematical statisticians whose intelƯ lect gave birth to the field of multivariate analysis, such as Hotelling, Bartlett, and Wilks, and those who chose to "concentrate much of their attention on methods of analyzing data in the sciences and of interpreting the results of statistical analysis ... (and) ... who are more interested in the sciences than in mathematics, among other characteristics." I find the distinction between individuals who are temperamentally "matheƯ maticians" (whom philosophy students might call "Platonists") and "scientists" ("Aristotelians") useful as long as it is not pushed to the point where one assumes "mathematicians" completely disdain data and "scientists" are never interested in contributing to the mathematical foundations of their discipline. I certainly feel more comfortable attempting to contribute in the "scientist" rather than the "mathematician" role. As a consequence, this book is primarily written for individuals concerned with data analysis. However, as noted in Chapter 1, true expertise demands familiarity with both traditions Front Matter....Pages i-xix Introduction and Previe....Pages 1-21 Some Basic Statistical Concepts....Pages 22-56 Some Matrix Concepts....Pages 57-88 Multiple Regression and Correlation—Part 1. Basic Concepts....Pages 89-120 Multiple Regression and Correlation—Part 2. Advanced Applications....Pages 121-156 Exploratory Factor Analysis....Pages 157-197 Confirmatory Factor Analysis....Pages 198-245 Classification Methods—Part 1. Forming Discriminant Axes....Pages 246-275 Classification Methods—Part 2. Methods of Assignment....Pages 276-314 Classification Methods—Part 3. Inferential Considerations in the MANOVA....Pages 315-344 Profile and Canonical Analysis....Pages 345-375 Analysis of Scales....Pages 376-409 Back Matter....Pages 410-508