Missing Data

A 4-Week On-Demand Seminar Taught by
Paul D. Allison, Ph.D.

Read reviews of this seminar

To see a sample of the course slides, click here.

For more than 15 years, Dr. Paul Allison has been presenting a 2-day, in-person seminar on Missing Data at various locations around the U.S. Based on his book Missing Data, this seminar covers both the theory and practice of two modern methods for handling missing data: multiple imputation and maximum likelihood.

Many researchers have told us that they would love to take the course but just can’t manage the time or the money to attend the live sessions. Developed over three years, this web-based version is a popular alternative for anyone looking for a more flexible option to learn missing data techniques. It is designed to closely match the in-person version, but with substantial additional material. 

The course takes place online in a series of four weekly installments of videos, quizzes, readings, and assignments, and requires about 10 hours/week. You may participate at your own convenience; there are no set times when you are required to be online.

This four-week course can be accessed with any recent web browser on almost any platform, including iPhone, iPad, and Android devices. It consists of 12 modules:

  1. Basic principles and assumptions.
  2. Conventional methods for missing data.
  3. Maximum likelihood (ML) for categorical variables.
  4. ML and the EM algorithm.
  5. Direct ML with SEM software and with mixed models.
  6. Basic principles of multiple imputation (MI).
  7. MI for non-monotone data using MCMC.
  8. MCMC options and complications.
  9. Fully conditional specification.
  10. Multivariate inference, interactions, and nonlinearities.
  11. Other methods, panel data, clustered data.
  12. Non-ignorable missing data.

Each module begins with an introductory video, followed by a narrated PowerPoint presentation. The modules contain all the slides in the live, 2-day version of the course. But there are also many additional slides that wouldn’t fit into the live course, including several slides on imputation with clustered data.

Each module is followed by a short multiple-choice quiz to test your knowledge. There are also weekly exercises that ask you to apply what you’ve learned to a real data set. You may submit your exercises for grading by Dr. Allison.  

There is also an online discussion forum where you can post questions or comments about any aspect of the course. All questions will be promptly answered by Dr. Allison.  


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.

Although these new methods for handling missing data have been around for more than two decades, they have only become practical in recent 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, Cox regression and regression for count data. 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.


In the videos, SAS will be the main software package used to demonstrate the methods. Mplus and LEM will also be used for portions of the modules on maximum likelihood estimation. For those who prefer to use Stata or R, slides and exercises using these packages can be downloaded from the course site.

WHO SHOULD Register?

Virtually anyone who does statistical analysis can benefit from new methods for handling missing data. To optimally benefit from this course, you should have a good working knowledge of the principles and practice of linear regression, as well as elementary statistical inference. But you do not need to know matrix algebra, calculus, or likelihood theory. You should be at least moderately proficient at using one of these packages: SAS, Stata or R. 

Comments from Recent participants

“The online Missing Data course taught by Professor Paul Allison is an excellent course and provides great opportunity to learn some of the modern techniques to tackle missing data. Practical examples are included in each module, and the weekly exercises in the course enable students to apply modern missing data techniques in different applications.”
  Bilal Mirza, University of California, Los Angeles

“Statistical Horizons’ online Missing Data course is a much-needed interactive presentation of the material described in Paul Allison’s ‘little green book’. Beyond that, it includes descriptions of more recent methods, updated recommendations, and coding examples in R. As someone who had read many texts on missing data and had understood the theory behind robust methods for handling it, I really benefited from the extra examples and practice exercises Professor Allison presented in the online course.”
  Simon G. Brauer, Duke University

“The course has provided me with a deeper theoretical understanding of missing data. I also appreciate the interactiveness of the course, that I could clarify with Prof. Allison questions that I have.”
  Yvonne Yock, National Institute of Education, Singapore

“What I liked most about the online Missing Data course is 1) the instructor was extremely knowledgeable; 2) he provided prompt and helpful replies to all questions from participants; and 3) the course material was adapted to different statistical software.”
  Nicolas Van der Linden, Free University of Brussels