Please note: These class lecture notes are from 2005 and do not reflect some of the newer enhancements to Stata. These notes will be updated as time permits.

Part 1 - Peliminary Topics
1. Introduction
2. Information in Contingency Tables
3. Review of OLS Regession
4. Collinearity Issues
5. Loglinear Regression Models

   Part 2 - Binary Response Models

6. Odds & Ends
7. Logistic Regression Models
8. More Logistic Regression
9. Model Fit
10. Logistic Diagnostics
11. Interactions in Logistic Regression
12. Perfect Prediction
13. Polynomial Logistic Regression
14. OLS versus Logistic
15. Probit Models
16. Interpreting Probit Coefficients
17. Complementary Log-Log Models
18. Conditional Logit Models
19. Bivariate Probit Models
20. Multivariate Probit Models
21. Binary Panel Data
22. Survey Logistic Regression

    Part 3 - Beyond Binary: Multinomial Response Models

23. Ordered Logit & Probit Models
24. Cut Points & Constants (Stata FAQ)
25. Multinomial Logit Models
26. Left/Right Equivalency
27. Ordinal Predictor Variables
28. Interpreting Logistic Regression in all its Forms(PDF) by William Gould
29. Discriminant Function Analysis

    Part 4 - Count Models

30. Poisson Models
31. Negative Binomial Models
32. Zero-inflated Count Models
33. Zero-truncated Count Models
34. Hurdle Models

    Part 5 - Survival Models

35. Introduction to Survival Analysis
36. Discrete-Time Survival Analysis
37. Proportional Hazards (Semiparametric) Model

    Part 6 - Other Topics

38. Generalized Linear Models
39. A Matter of Proportion
40. Relative Risk
41. Generalized Estimating Equations - Gausian
42. Generalized Estimating Equations - Binary & Count
43. Regression Models with Censored Data or Truncated Data
44. Selection Models
45. Quantile Regression
46. A Rasch Model Example
47. Latent Profile & Latent Class Models
48. Latent Class Analysis Stata Example
49. Instrumental Variables Regression
50. Regression with Measurement Error
51. Correspondence Analysis
52. The Process of Data Analysis

Categorical Data Analysis Course