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Missing Data - Online Course

A 4-Week On-Demand Seminar Taught by

Paul Allison
Course Dates: Ask about upcoming dates

Each Monday you will receive an email with instructions for the following week.
All course materials are available 24 hours a day. Materials will be accessible for an additional 2 weeks after the official close on November 14.

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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.

Maximum likelihood and multiple imputation have very similar statistical properties. If the assumptions are met, they are approximately unbiased and efficient. What’s remarkable is that these newer methods depend on less demanding assumptions than those required for older methods for handling missing data.

Maximum likelihood is available for logistic regression, Cox regression, and regression for count data. Multiple imputation can be used for virtually any statistical problem.

Although these newer methods for handling missing data have been around for more than two decades, they have only become practical with the introduction of widely available and user friendly software.

Based on Paul Allison’s book Missing Data, this seminar covers both the theory and practice of multiple imputation and maximum likelihood.

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

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.


Who should register?

Seminar outline

Registration instructions

"This course is a great introduction..."

“I have only recently become aware of the importance of missing data. This course is a great introduction to a topic I knew very little about. I feel like it has opened up a whole new frontier in how I handle data.”

Bob Reed

University of Canterbury

"... I could not get a clear understanding until I attended this course"

“Although I have struggled to understand how to handle missing data for several years, I could not get a clear understanding until I attended this course. The depth and breadth of the ways to deal with missing data taught by Professor Allison are beyond rival!”

Yunhwan Lee

Ajou University School of Medicine

"Practical examples are included in each module..."

“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

"... I really benefited from the extra examples and practice exercises..."

“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."

“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

"... the instructor was extremely knowledgeable..."

“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