Correlation matrices (along with their unstandardized counterparts, covariance matrices) underlie the majority the statistical methods that researchers use today. A correlation matrix is more than a matrix filled with correlation coefficients. The value of one correlation in the matrix puts constraints on the values of the others, and the multivariate implications of this statement is a major theme of the volume. Alexandria Hadd and Joseph Lee Rodgers cover many features of correlations matrices including statistical hypothesis tests, their role in factor analysis and structural equation modeling, and graphical approaches. Understanding Correlation Matrices illustrates the discussion with a wide range of lively examples including correlations between intelligence measured at different ages through adolescence; correlations between country characteristics such as public health expenditures, health life expectancy, and adult mortality; correlations between well-being 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.

Resources from the book are available below:

R files for chapters 1 and 3:

› R Files.zip

A downloadable Appendix which demonstrates the use of the functions:  

› Online Appendix.doc

Acknowledgments

We gratefully acknowledge Alexandria Hadd and Joseph Lee Rodgers for writing an excellent text and providing these materials.