Conventional methods for missing data, like listwise deletion or regression imputation, are prone to three serious problems:
These new methods for handling missing data have been around for at least a decade, but have only become practical in the last few years with the introduction of widely available and user friendly software. Maximum likelihood and multiple imputation have very similar statistical properties. If the assumptions are met, they are approximately unbiased and efficient--that is, they have minimum sampling variance.
What's remarkable is that these newer methods depend on less demanding assumptions than those required for conventional methods for handling missing data. Maximum likelihood is available for linear models, logistic regression and Cox regression. Multiple imputation can be used for virtually any statistical problem.
This course will cover the theory and practice of both maximum likelihood and multiple imputation. Maximum likelihood for linear and logistic models will be demonstrated with Mplus, a software package designed for estimating structural equation models with latent variables. Multiple imputation will be demonstrated with SAS and with Stata.
Virtually anyone who does statistical analysis can benefit from new methods for handling missing data. To take this course, you should have a good working knowledge of the principles and practice of multiple regression, as well as elementary statistical inference. But you do not need to know matrix algebra, calculus, or likelihood theory.
The class will meet from 9 to 4 each day with a 1-hour lunch break.
In addition to Professor Allison's text Missing Data, participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking.
Registration and Lodging
fee of $795 includes all course materials.
must make their own arrangements for lodging. Special rates have been
arranged at a nearby hotel.
1. Assumptions for missing data methods
2. Problems with conventional methods
3. Maximum likelihood (ML)
4. ML with EM algorithm
5. Direct ML with Mplus
6. ML for contingency tables
7. Multiple Imputation (MI)
8. MI under multivariate normal model
9. MI with SAS
10. MI with categorical and nonnormal data
11. Interactions and nonlinearities
12. Using auxiliary variables
13. Other parametric approaches to MI
14. Linear hypotheses and likelihood ratio tests
15. Nonparametric and partially parametric methods
16. Sequential generalized regression models
17. MI and ML for nonignorable missing data
The fee of $795 covers all course materials.
Our Tax ID number is 26-4576270.