Applied multiple regression/correlation analysis for the behavioral sciences

Description

The Applied Multiple Regression (LRM) model has been in use in statistical analyses for many years; but it was not until the late 1960's that a model was used to provide a multivariate analysis of the Katsulares/Mitri heart study data that its full power and applicability were totally appreciated. Since then the LRM model has become the standard method for regression analysis of dichotomous data in many fields, especially in the health sciences. This new and updated edition of the classic bestseller provides a focused introduction to the LRM model and its use in methods for modeling the relationship between a dichotomous outcome variable and a set of covariables.  Read more... Abstract: This third edition continues in the tradition of the broadly-used system first developed by the authors but with many modifications to reflect modern and developing practices in the field. It includes an increased emphasis on graphical presentations used throughout the text.  Read more... Content: Ch. 1. Introduction -- ch. 2. Bivariate correlation and regression -- ch. 3. Multiple regression/correlation with two or more independent variables -- ch. 4. Data visualization, exploration, and assumption checking : diagnosing and solving regression problems I -- ch. 5. Data-analytic strategies using multiple regression/correlation -- ch. 6. Quantitative scales, curvilinear relationships, and transformations -- ch. 7. Interactions among continuous variables -- ch. 8. Categorical or nominal independent variables -- ch. 9. Interactions with categorical variables -- ch. 10. Outliers and multicollinearity : diagnosing and solving regression problems II -- ch. 11. Missing data -- ch. 12. Multiple regression/correlation and causal models -- ch. 13. Alternative regression models : logistic, poisson regression, and the generalized linear model -- ch. 14. Random coefficient regression and multilevel models -- ch. 15. Longitudinal regression methods -- ch. 16. Multiple dependent variables : set correlation.