Every data analyst must deal with missing data problems. However, conventional methods for handling missing data, like listwise deletion (complete case analysis) or single imputation, often make things worse. Much better solutions are now readily available. This seminar covers two of them, maximum likelihood and multiple imputation, which have very good statistical properties. Remarkably, they require less stringent assumptions than conventional methods. This seminar delves deeply into the conceptual foundations of these methods but, more importantly, teaches you the practical details of how to implement them. Maximum likelihood is demonstrated with two software packages, LEM and Mplus. Multiple imputation is demonstrated with SAS and Stata.
The fee of 495 euros includes all course materials and a continental breakfast. Information on lodging is available at the course web site.
Dr. Allison is Professor of Sociology at the University of Pennsylvania. He is the author of Missing Data (Sage 2001) and Survival Analysis Using SAS (SAS Institute 1995).
To get more information and to register, go to www.StatisticalHorizons.com
http://www.regonline.com/builder/site/Default.aspx?eventid=802739
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