Reading and Understanding Multivariate Statistics helps researchers, students, and other readers of research to understand the purpose and presentation of multivariate techniques. The editors focus on providing a conceptual understanding of the meaning of the statistics in the context of the research questions and results; they leave the subject of how to perform multivariate analysis to other texts.

The book presents an overview of multivariate statistics and their place in research. It describes the appropriate context for—and the types of empirical questions that can best be addressed by—each technique or family of techniques, as well as the distribution assumptions that must be met for the analysis to be meaningful.

The most commonly used multivariate techniques are examined in detail:

  • multiple regression and correlation
  • path analysis
  • principal-components analysis
  • exploratory and confirmatory factor analysis
  • multidimensional scaling
  • analysis of cross-classified data
  • logistic regression
  • multivariate analysis of variance (MANOVA)
  • discriminant analysis
  • meta-analysis

Statistical notations are explained, underlying assumptions are described, and terms are defined clearly and understandably. Concepts and symbols are presented with minimal use of formulas and a generous use of real-world research examples. Each chapter also includes suggestions for additional reading and a glossary of statistical and related terms.

Reading and Understanding Multivariate Statistics is an ideal companion to any multivariate research text for performing these analyses, so in addition to research consumers it will be helpful to students and investigators learning to use a particular analysis for the first time.

Table of Contents

List of Contributors


  1. Introduction to Multivariate Statistics
    —Laurence G. Grimm and Paul R. Yarnold
  2. Multiple Regression and Correlation
    —Mark H. Licht
  3. Path Analysis
    —Laura Klem
  4. Principal-Components Analysis and Exploratory and Confirmatory Factor Analysis
    —Fred B. Bryant and Paul R. Yarnold
  5. Multidimensional Scaling
    —Loretta J. Stalans
  6. Analysis of Cross-Classified Data
    —Willard Rodgers
  7. Logistic Regression
    —Raymond E. Wright
  8. Multivariate Analysis of Variance
    —Kevin P. Weinfurt
  9. Discriminant Analysis
    —A. Pedro Duarte Silva and Antonie Stam
  10. Understanding Meta-Analysis
    —Joseph A. Durlak


About the Editors