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A Distinguished Speaker Series Seminar by Paul Allison, Ph.D.

Statistical Horizons is celebrating 20 years of advancing research methods—and we couldn’t have done it without you.

Since 2005, we’ve helped thousands of researchers across academia, government, and industry sharpen their skills and apply cutting-edge tools to real-world data. To mark our 20th anniversary, we’re offering a special thank you to our community: a free, 3-hour seminar led by our founder and CEO, Paul Allison.

Widely regarded as a leading authority on missing data, Dr. Allison will revisit one of the most important topics in applied research as part of our Distinguished Speaker Series. Whether you’ve been with us since the beginning or are just discovering Statistical Horizons, we invite you to join us for this free event.

ABSTRACT

In 2001, Paul Allison published Missing Data as part of Sage’s acclaimed “little green book” series. Cited over 10,000 times, the book focused primarily on two emerging methods for handling missing data: maximum likelihood (ML) and multiple imputation (MI). Much has changed since then—and this seminar is designed to bring you up to date.

In the first hour, Professor Allison will revisit the state of the field as it stood in 2001. After a brief historical overview, he will introduce the core, enduring principles behind ML and MI. Particular attention will be given to the foundational assumptions of that era: missing at random and multivariate normality—what they mean and why they matter.

The second hour will provide an accessible survey of the most important developments from the past 24 years, including:

• MICE methods for categorical and other non-normal data.
• New insights and methods for determining the number of imputations needed.
• Factorization techniques for handling categorical variables with ML.
• Extensions of MI and ML to multilevel data.
• Substantive model compatibility in MI.
• The EMB algorithm for MI.
• Improved handling of categorical variables under multivariate normality.

In the third hour, Professor Allison will turn to the fully Bayesian approach to missing data—a topic not covered in his original book. While Bayesian methods for missing data have long existed, recent advances in software have made them far more accessible to applied researchers. Today, fully Bayesian tools are not only easy to use but also remarkably flexible and powerful.

Even if your preference is for multiple imputation, Bayesian methods offer a nearly ideal way to generate high-quality imputations across a wide range of models.

Join us for a comprehensive update on missing data methods—what has changed, what has endured, and what you need to know now.