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 2007 Participants Participants in the 2007 seminars were asked to rate the course on a scale of 1 (worst) to 10 (best).  The average score was 9.1. They were also asked if they wished to make an attributed statement regarding the course. Here are all the comments that were received:    "Dr. Allison's missing data course was extremely informative, covering introductory and advanced topics.  He was able to cover a tremendous amount of material in the 2-day period, adhering to a good structure and tracking our comprehension.  I can't wait to start applying some of the learned methodology." Maria Shiyko, CUNY "Thorough, clear explication of the problem of missing data and how to deal with it.  I'll never be complacent about missing data again." Palmer Bessey, Cornell University School of Medicine "I highly recommend this course for anyone interested in research since it is difficult not to find problems with missing data in your own research or in the literature.  This is an advanced course that teaches some of the most recent ways of dealing with missing data in a serious and rigorous way." Alfonso Serrano-Maillo, Universidad Nacional de Educacion a Distancia, Spain "Allison shows you how to think critically in order to solve your missing data problem." Tim Cheney, University of Pennsylvania "This was a comprehensive, but very comprehensible, introduction to missing data procedures."  Brad Cox, Pennsylvania State University   "Excellent overview of the methods available and wonderful discussion of the advantages and disadvantages of  using them in real data situations." Moomita Sinha, Boehringer Ingelheim Pharmaceuticals, Inc.   "I found Missing Data very well organized, extremely useful in my work, and full of good, well thought out examples." Kirk Miller, Franklin and Marshall College "