Elena Erosheva
C 14C Padelford Hall (206) 6850166 elena@stat.washington.edu 

A great deal of data analyzed by social scientists are organized according to some sort of clustered or hierarchical structure. This course will focus on methodology for the analysis of data with complex patterns of variability such as those arising from longitudinal and nested designs: e.g., measurements on subjects over time, or records on students within classes within schools. The goal of this course is to provide students with knowledge and confidence to use hierarchical modeling in their discipline through understanding of statistical theory behind hierarchical models set up and estimation. We will spend a fair amount of time during the quarter through the underlying mathematics of hierarchical models. Nonetheless, the main emphasis of the class will be on applications. 
It is assumed that the students have completed a statistical sequence (such as SOC 424426), and a regression or an applied regression course (such as CS&SS 504). It is also recommended that the students have some familiarity with basic calculus (differentiation and integration), matrix algebra (matrix addition, multiplication, and inversion), and probability theory. Some familiarity with computing packages SAS and SPlus or R would be helpful as well. 
We will use R/Splus software for creating plots and for exploratory data analysis. We will mostly use SAS Proc Mixed for fitting the models. 
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Students with Disabilities: If you would like to request academic accommodations due to a disability, please contact Disabled Student Services, 448 Schmitz, 5438924 (V/TTY). If you have a letter from Disabled Student Services indicating you have a disability that requires academic accommodations, please present the letter to me so we can discuss the accommodations you might need for this class. 