Providing a practical, thorough understanding of how factor analysis works, this text discusses the assumptions underlying the equations and procedures of this method. This long-awaited second edition includes a new chapter on the multivariate normal distribution, its general properties, and the concept of maximum-likelihood estimation. It also contains a rewritten chapter on analytic oblique rotation that focuses on the gradient projection algorithm and its applications as well as a revised chapter on confirmatory factor analysis. This edition offers more complete coverage of descriptive factor analysis and doublet factor analysis and explores the developments of factor score indeterminacy.
