Modern Missing Data Analysis: Fundamentals

December 5-7, 2022
Replay Access: 1 year from end of workshop
Instructor: Craig Enders

Student: $475
Professional: $595

Category: Livestream, Workshops

Missing data are a ubiquitous feature of nearly all research applications, arising through participant non-response, attrition, and sometimes even by design. Failure to account appropriately for missing values when conducting statistical analyses can result in badly biased estimates and incorrect inferences about the relationships under study. Modern Missing Data Analysis: Fundamentals is a three-day workshop focused on three optimal approaches for addressing missing data that can be applied across a variety of research settings: maximum likelihood, Bayesian estimation, and multiple imputation. Participants will learn details about the classic versions of these methods as well as state-of-the-art extensions that have been developed in the last five years. These procedures are advantageous because they use all available data and make realistic assumptions about the cause of missingness; estimates and significance tests are therefore valid in a broader range of situations than historical methods such as deleting incomplete data records. The purpose of this course is to provide participants with foundational knowledge about maximum likelihood and Bayesian estimation, including how to save and analyze multiple imputations. Equally important, participants will learn how to select an appropriate missing data handling method and apply it to complex, real-world data sets. To this end, presentations will include a mix of theoretical information, practical tips, and in-depth, recorded computer applications.

Note: This workshop is a shorter version of the three-day workshop Modern Missing Data Analysis that was offered in June of 2022 and is currently available for asynchronous access. The longer workshop includes more extensive coverage of multiple imputation as well as additional material on selection and pattern mixture models for missing not at random processes, missing data handling for scale scores with incomplete items, nonnormal missing data, and multilevel missing data. Participants seeking illustrations of these more advanced topics may prefer to enroll in the longer workshop, with asynchronous access available now and a new Livestream planned for Spring 2023.


Craig K. Enders, PhD

Craig Enders is Professor and Area Chair of Quantitative Psychology in the Department of Psychology at the University of California, Los Angeles. His primary research focus is on analytic issues related to missing data analyses, and he leads the research team responsible for developing the Blimp statistical software application. Read More

Workshop Details


In an effort to continually improve our instruction we obtain student evaluations with each course offering. Here is a sample of reviews from our prior online offering of Introduction to Missing Data Analysis by Dr. Enders in June, 2022:

Wow this has to be among the best courses I have ever taken!  I came in feeling less than confident in my knowledge regarding missing data analysis.  I've come out feeling I can handle just about anything that I have had to deal with in my practice by referencing course notes, the recordings, the examples, and dr. enders' book and website.  Kudos!

Craig is clearly an expert on the topic and his materials were very organized and easy to follow. I appreciate the examples in multiple software formats as well as the various types of data examples. This was really great.

Craig is an excellent instructor and very helpful with questions. I appreciated his style; not too slow and not too fast.

Craig mentioned a lot of little details that might not make it into a text paper or tutorial. These details were helpful for putting things in context and explaining some of the nuances of applied missing data analysis.

Dr. Enders is among the best lecturers, ever.  He really understands what you need to know to feel confident with the technique.  He literally builds your confidence as he goes along.

This was fantastic, thank you!

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