© 2001 – Psychology Press
Drawing upon more than 30 years of experience in working with statistics, Dr. Richard J. Harris has updated A Primer of Multivariate Statistics to provide a model of balance between how-to and why. This classic text covers multivariate techniques with a taste of latent variable approaches. Throughout the book there is a focus on the importance of describing and testing one's interpretations of the emergent variables that are produced by multivariate analysis.
This edition retains its conversational writing style while focusing on classical techniques. The book gives the reader a feel for why one should consider diving into more detailed treatments of computer-modeling and latent-variable techniques, such as non-recursive path analysis, confirmatory factor analysis, and hierarchical linear modeling. Throughout the book there is a focus on the importance of describing and testing one's interpretations of the emergent variables that are produced by multivariate analysis.
"Multivariate statistical techniques are not easy, nor are they meant to be, but I cannot imagine a more genial, if sometimes demanding, guide for an introduction to the topic. There is no 'bad voice' here."
"The coverage of the book is essentially classical multivariate statistics, based on the normal distribution, plus a discussion of factor analysis. This, I think, is a good choice, and fits well with Harris' goal to make the book a primer that presents the concepts a researcher needs to make good use of the computer packages."
—Thomas D. Wickens
University of California at Los Angeles
Contents: Preface. The Forest Before the Trees. Multiple Regression: Predicting One Variable From Many. Hotelling's T2: Tests on One or Two Mean Vectors. Multivariate Analysis of Variance: Differences Among Several Groups on Several Measures. Canonical Correlation: Relationships Between Two Sets of Variables. Principal Component Analysis: Relationships Within a Single Set of Variables. Factor Analysis: The Search for Structure. The Forest Revisited. Digression 1: Finding Maxima and Minima of Polynomials. Digression 2: Matrix Algebra. Digression 3: Solution of Cubic Equations. Appendices: Statistical Tables. Computer Programs Available From the Author. Derivations.