Data Analysis (Undergraduate)
This course reviews and expands upon core topics in probability and statistics through the study and practice of basic data analysis. Topics include data, hypothesis testing, confidence intervals, counts and tables, tests for independence of categorical variables, and linear regression analysis. Upon completion of this course, students should be able to think critically about data and apply standard statistical inference procedures to draw conclusions from such analyses. This course will be computationally, not mathematically, intensive and will use the R and STATA language and environment for statistical computing and graphics.
Advanced Statistical Analysis (Undergraduate)
This course builds on basic understanding of linear models to address in detail more advanced issues. Include interactions and their visual representation, dealing with heterogeneity in errors. The course focuses on obtain robust statistical results. The end of the course addresses more advanced topics based on the interests and needs of the students.
Public Opinion (Masters)
This course introduces students to basic concepts in public opinion surveys preparing them to property read and understand results. The course then discusses sampling issues and the construction of surveys.
Scope and Methods / Research Design (PhD)
The course covers the whole range from Little, KKV, to Brady and Collier, Goertz and Mahoney. It discusses case studies and process tracing in depth and introduces students to natural experiments.
Regression Analysis (PhD)
This is an introductory course to regression analysis based on LME.
Categorical Data Analysis (PhD)
This course introduces students to non-linear modes for categorical and limited dependent variables. The course covers ogit through hurdle models.
Longitudinal and Panel Analysis (PhD)
An introduction to continuous and categorical analysis of longitudinal and panel data.