Education 231C

Applied Categorical & Nonnormal Data Analysis

Course Topics

    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. Using Categorical Predictors (Courtesy of ATS)
    Note: Unit 11 is rough draft for an unfinished project.
  12. Interactions in Logistic Regression
  13. Perfect Prediction
  14. Polynomial Logistic Regression
  15. OLS versus Logistic
  16. Probit Models
  17. Interpreting Probit Coefficients
  18. Complementary Log-Log Models
  19. Conditional Logit Models
  20. Bivariate Probit Models
  21. Multivariate Probit Models
  22. Binary Panel Data
  23. Survey Logistic Regression

    Part 3 - Beyond Binary: Multinomial Response Models

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

    Part 4 - Count Models

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

    Part 5 - Survival Models

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

    Part 6 - Other Topics

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

Categorical Data Analysis Course

phil ender 6dec05