Missing Data

A 2-Day Seminar taught by Paul D. Allison, Ph.D. 

Read reviews of this seminar. 

If you’re using conventional methods for handling missing data, you may be missing out. Conventional methods for missing data, like listwise deletion or regression imputation, are prone to three serious problems:

  • Inefficient use of the available information, leading to low power and Type II errors.
  • Biased estimates of standard errors, leading to incorrect p-values.
  • Biased parameter estimates, due to failure to adjust for selectivity in missing data.

More accurate and reliable results can be obtained with maximum likelihood or multiple imputation.

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 models will be demonstrated with SAS, Stata, and Mplus. Mplus will also be used for maximum likelihood with logistic regression. Multiple imputation will be demonstrated with both SAS and 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. 

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.  


The fee of $895.00 includes all seminar materials. 

Lodging Reservation Instructions

A block of rooms has been reserved at the Courtyard Washington Embassy Row, 1600 Rhode Island Avenue, NW, Washington, DC  20036 at a special rate of $119 per night.  In order to  make your reservations, call Marriott at 1-888-236-2427 or 1-202-448-8004 and identify yourself with Statistical Horizons. You may also click here to book online. The room block will expire once the block is full or on Tuesday, September 30 at 5 PM (Eastern Time). 


  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, Stata and SAS
  6. ML for contingency tables
  7. Multiple Imputation (MI)
  8. MI under multivariate normal model
  9. MI with SAS and Stata
  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. Fully conditional models
  17. MI and ML for nonignorable missing data


Comments by recent participants

“Clear, concise, well organized information for those beginner to intermediate users of Missing Value procedures. The class is well organized, the lectures are well paced, and the material is well thought out. Dr. Allison is receptive to questions and to individual problem discussion. Highly recommended.”
 Carl Peiper, Duke University Medical Center 

“This is a great course for missing values. You don’t need to spend a lot of time to understand the mathematical formulas. Overall definitely worth taking.” 
  Huifeng Yun, University of Alabama at Birmingham 

“I read about a dozen articles on missing data techniques before taking this course – including two excellent articles by the course instructor, Paul Allison. That reading was helpful but not necessary to understand the course lectures. Most importantly, the course helped me understand what I had read and introduced numerous ideas/facts not contained in the readings. This course was definitely worth the time and money – I walk away with it much more knowledgeable about missing data techniques and  more  confident in my ability to implement them properly.”
  Wm. Michael Lynn, Cornell University