Event Phone: 1-610-715-0115

Students can get the discounted rate by emailing info@statisticalhorizons.com using your university email address.

Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 3-Day Livestream Seminar Taught by Paul Allison, Ph.D.

This seminar is intended to give students the opportunity to learn Missing Data from an expert instructor, Dr. Paul Allison, at a special price of $295 (email info@statisticalhorizons.com for the student discount code). Non-students are welcome to register at the regular rate of $995.

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

If you are 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.

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.

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 linear regression, 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. These methods will be demonstrated using four packages in R: norm2, lavaan, jomo and mice. Slides and exercises using SAS and Stata are also available to participants on request.