3D Hierarchical Models
Jeff Gill, Washington University in St. Louis
6 - 17 August (two week course / 35 hrs)
Detailed Course Outline [PDF]
Course Content
This course covers statistical model development with explicitly defined hierarchies. Such multilevel specifications allow researchers to account for different structures in the data and provide for the modelling of variation between defined groups. The course begins with simple nested linear models and proceeds on to non-nested models, multilevel models with dichotomous outcomes, and multilevel generalized linear models. In each case, a Bayesian perspective on inference and computation is featured. The focus on the course will be practical steps for specifying, fitting, and checking multilevel models with much time spent on the details of computation in the R and bugs environments.
Course Objectives
This course emphasizes the practical aspects of model production: specification of hierarchies based on the theorized data-generation process, development of estimation processes using free, high quality software (R and WinBUGS), careful model comparison and assessment. Students will get hands-on experience producing modern statistical solutions to their practical problems. This will especially helpful to those with current datasets related to their research agenda.
Course Prerequisites
Basic, but not advanced, knowledge of calculus, matrix algebra, and probability. We will review the basic linear regression model as a starting point before proceeding on to multilevel forms.
Reading
Gill, J. 1999.The Insignificance of Null Hypothesis Significance Testing. Political Research Quarterly 52, 647-674.
Cohen, J. 1994. The Earth is Round ($p<.05$). American Psychologist. December, 12, 997-1003.
King, G. 1986. How Not to Lie with Statistics: Avoiding Common Mistakes in Quantitative Political Science. American Journal of Political Science 30 (3) 666-87.
Leamer, E. E. 1983. Lets Take the Con Out of Econometrics. American Economic Review 73, No. 1 (March), 31-43.