A 2-Day Seminar on Modern Methods for Handling Missing Data,Taught by Paul D. Allison, Ph.D.
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.
These new methods for handling missing data have been around for at least a decade, but have only become practical in the last few 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 and Cox regression. 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. Maximum likelihood for linear models will be demonstrated with SAS, Stata, and Mplus. Mplus will also be used for maximum likelihood with logistic regression. Multiple imputation will be demonstrated with both SAS and Stata.
Who should attend?
Virtually anyone who does statistical analysis can benefit from new methods for handling missing data. To take this course, you should have a good working knowledge of the principles and practice of multiple regression, as well as elementary statistical inference. But you do not need to know matrix algebra, calculus, or likelihood theory.
Schedule and materials
The class will meet from 9 to 4 each day with a 1-hour lunch break.
Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking.
Registration and Lodging
The fee of $895 includes all course materials.
Lodging Reservation Instructions
A block of rooms has been reserved at the seminar location, the Jamaica Bay Inn, 4175 Admiralty Way, Marina del Rey CA at a rate of $169 – 189 per night. To make a reservation, please call 888-823-5333 and identify yourself with the Group Code 50714 Statistical Horizons. You may also click here for the hotel’s registration page Jamaica Bay. In order to guarantee rate and availability, reservations must be made by Tuesday, April 16, 2013.
- Assumptions for missing data methods
- Problems with conventional methods
- Maximum likelihood (ML)
- ML with EM algorithm
- Direct ML with Mplus, Stata and SAS
- ML for contingency tables
- Multiple Imputation (MI)
- MI under multivariate normal model
- MI with SAS and Stata
- MI with categorical and nonnormal data
- Interactions and nonlinearities
- Using auxiliary variables
- Other parametric approaches to MI
- Linear hypotheses and likelihood ratio tests
- Nonparametric and partially parametric methods
- Fully conditional models
- MI and ML for nonignorable missing data
“This course was an excellent overview of diverse methods for handling the complexities of missing data. I enjoyed the demonstrations, the discussion of different software capacities, and the honest, practical approach to the topic.”
Jerel Calzo, Children’s Hospital Boston/Harvard Medical School
“This course was very easy to follow and doesn’t require advanced statistical knowledge, however Paul is very knowledgeable and can answer very advanced questions if that is your level of statistical expertise. The program code and output are great and will help me implement what I’ve learned back home.”
Elizabeth Baker, RAND Corp.
“”I thought this was a great overview of missing data methods including conventional methods as well as more novel approaches. Lots of good examples and code to use for implementation. I would highly recommend this course!”
Heather Baer, Brigham and Women’s Hospital
“The individuals in my area of study and work – epidemiology and biostatistics and analysis – need to develop new skills, sharpen and refresh old knowledge and keep up to date. I have found courses I have taken from Statistical Horizons to be an excellent, efficient and economical way of achieving these goals. Thank you!”
Martin Tammemagi, Brock University
“For researchers who have tons of burning questions from practice but no experts to consult with, this two-day intensive workshop is a great place to comb through your knowledge and skills on this topic. Paul answered my questions briefly but conceptually and to the point. It is also a great opportunity to connect with colleagues of similar interests.”
Qiong Fu, Lehigh University
“I really appreciated the structure of this course which walked us through the original intuitive ways of dealing with missing data and then took us through more sophisticated ways of handling it. There was a good balance of giving us the “spirit” of what is happening “behind the scenes” in these analyses and practical steps and tips on how to implement it in several software packages.”
Laura Gibson, University of Pennsylvania
“Excellent overview of applied missing data. Dr. Allison is knowledgeable and stays current with new developments. I would highly recommend this course.”