Anyone familiar with Andrew Gelman's excellent blog, Statistical Modeling, Causal Inference, and Social Science, would be keen to see his new book, aco-authored with Jennifer Hill. The book is titled Data Analysis Using Regression and Multilevel/Hierarchical Models, and has just been published by Cambridge University Press.
Judging by its table of contents it covers plenty of ground, including several that have been neglected by other authors:
1. Why?; 2. Concepts and methods from basic probability and statistics;
Part IA. Single-level Regression: 3. Linear regression: the basics; 4. Linear regression: before and after fitting the model; 5. Logistic regression; 6. Generalized linear models;
Part IB. Working with Regression Inferences: 7. Simulation of probability models and statistical inferences; 8. Simulation for checking statistical procedures and model fits; 9. Causal inference using regression on the treatment variable; 10. Causal inference using more advanced models;
Part IIA. Multilevel Regression: 11. Multilevel structures; 12. Multilevel linear models: the basics; 13. Multilevel linear models: varying slopes, non-nested models and other complexities; 14. Multilevel logistic regression; 15. Multilevel generalized linear models;
Part IIB. Fitting Multilevel Models: 16. Multilevel modeling in bugs and R: the basics; 17. Fitting multilevel linear and generalized linear models in bugs and R; 18. Likelihood and Bayesian inference and computation; 19. Debugging and speeding convergence;
Part III. From Data Collection to Model Understanding to Model Checking: 20. Sample size and power calculations; 21. Understanding and summarizing the fitted models; 22. Analysis of variance; 23. Causal inference using multilevel models; 24. Model checking and comparison; 25. Missing data imputation;
Appendixes: A. Six quick tips to improve your regression modeling; B. Statistical graphics for research and presentation; C. Software; References.
Caveat: I've ordered it but have not yet seen a copy. Comments welcome from those who have.