MISSING DATA A  2-Day Course on Modern Methods for Handling Missing Data 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.  At present, maximum likelihood is best suited for linear models or log-linear models for contingency tables.  Multiple imputation, on the other hand, 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 Amos, a software package designed for estimating structural equation models with latent variables.  Multiple imputation will be demonstrated with two new SAS procedures (PROC MI and PROC MIANALYZE) and two Stata commands (ICE and MICOMBINE).  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.   Materials In addition to Professor Allison's text Missing Data, 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. Course outline 1. Assumptions for missing data methods 2. Problems with conventional methods 3. Maximum likelihood (ML) 4. ML with EM algorithm 5. Direct ML with Amos 6. ML for contingency tables 7. Multiple Imputation (MI) 8. MI under multivariate normal model 9. MI with SAS 10. MI with categorical and nonnormal data 11. Interactions and nonlinearities 12. Using auxiliary variables 13. Other parametric approaches to MI 14. Linear hypotheses and likelihood ratio tests 15. Nonparametric and partially parametric methods 16. Sequential generalized regression models 17. MI and ML for nonignorable missing data   Comments by Recent Participants Participants in the 2008 seminar were asked to rate the course on a scale of 1 (worst) to 10 (best).  Of the 23 who responded, the average score was 8.4. They were also asked if they wished to make an attributed statement regarding the course. Here are the comments that were received:  "I found it a very useful overview of missing data analytic methods and will strongly recommend it." Chyke Doubeni, University of Massachusetts Medical School "Dr. Allison has cutting edge expertise across a broad set of topics, and seems to be able to think through missing data procedures in all of them. Come prepared with what you think are tough questions, and you will probably leave with the most current answers available." John Hitchcock, Vienna, Virginia "A very comprehensive and beneficial class!  Would recommend to other colleagues!" Xiatong Han, University of Arkansas for Medical Sciences "This course provides a rapid, thorough coverage of approaches to solving real problems with missing data.  It gives both a survey of the range of approaches, along with detailed, practical instruction on implementation, and guidance on choosing among them.  One is ready to get to work immediately on completion." Theodore J. Iwashyna, Hospital of the University of Pennsylvania "This course fills in the void you have about missing data.  Highly recommended." Shanti Tripathi, University of Arkansas for Medical Sciences