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It is with great pleasure to announce a MEAFA research training workshop on Multilevel/mixed Models Using Stata, 10-12 February 2010.

Mixed models contain both fixed effects and random effects and are useful in the physical, biological and economic disciplines. When mixed models contain more than one level of nested effects, they are referred to as multilevel or hierarchical models. Multilevel/mixed models are particularly useful in panel data analysis where measurements on a statistical units are repeated over time, or where measurements are made on clusters of related statistical units.

Day 1 (10 Feb) of the workshop provides an overall introduction to Stata 11 and demonstrates how to work efficiently with reproducible and tractable routines. This day is of interest to those who want to learn how to use Stata or want become more efficient in their work and are not interested in mixed models, or to those who want to attend the days on Multilevel/mixed models and have no or little experience with Stata.
Day 1 will be presented by Demetris Christodoulou, MEAFA General Convenor.

Days 2 & 3 (11-12 Feb) of the workshop engage with the theory and application of multilevel/mixed models. These days assume basic knowledge of Stata and of standard linear regression. Those interested only in mixed models and already have basic knowledge of Stata may attend only these days. Those interested in mixed models but do not have experience with Stata should attend all three days.
Days 2 & 3 will be presented by Robert Gutierrez, StataCorp Director of Statistics.

The course will be interactive, use real data, and offer ample opportunity for specific research questions and for working exercises to enforce what is learned. Detailed notes will be provided outlining all theory and applications.

Numbers are limited and places are reserved on a first-come first-served basis. To avoid disappointement and secure a place you must fill-in the online Reservation Form at:


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MEAFA is a cross-disciplinary research group that promotes methodical analytical work with empirical applications in financial analysis.