It is no exaggeration to say that virtually all quantitative research in the social sciences is done with correlation and regression analysis (CRA) and their siblings and offspring. CRA are fundamental analytic tools in fields like sociology, economics and political science as well as applied disciplines such as marketing, nursing, education and social work. The subject is of great substantive importance; therefore, distinguished editors, W. Paul Vogt and R. Burke Johnson, have ordered the growing research literature on the use of CRA according to its natural steps. Each step in this logical progression constitutes a part in this collection: Part I. Regression and Its Correlational Foundations and Concomitants Part II. Linear Regression Designs and Model Building Part III. Inherently Nonlinear Models: Log-Linear Models And Probit And Logistic Regression Part IV. Multi-Level Regression Modeling (MLM) Part V. Exploratory and Confirmatory Factor Analysis and Latent Class Modeling Part VI. Structural Equation Modeling (SEM)