Correlation matrices (along with their unstandardized counterparts, covariance matrices) underlie much of the statistical machinery in common use today. A correlation matrix is more than a matrix filled with correlation coefficients. The value of one coefficient in the matrix puts constraints on the values of the others, and this is a major theme of the volume. In Understanding Correlation Matrices authors Alexandria Hadd and Joseph Lee Rodgers illustrate their key points with a wide range of lively examples, including correlations between intelligence measured at different ages through adolescence; correlations between public health expenditures, health life expectancy, adult mortality, and other country characteristics; correlations between wellbeing and state-level vital statistics; correlations between the racial composition of cities and professional sports teams; and correlations between childbearing intentions and childbearing outcomes over the reproductive life course.
Chapter 1: Introduction
Chapter 2: The Mathematics of Correlation Matrices
Chapter 3: Statistical Hypothesis Testing on Correlation Matrices
Chapter 4: Methods for Correlation/Covariance Matrices as the Input Data
Chapter 5: Graphing Correlation Matrices
Chapter 6: The Geometry of Correlation Matrices
Chapter 7: Conclusion