Indiana University Bloomington

Political Science Y577
Political Data Analysis Ii (contextual Analysis)

Contact: Armando Razo (arazo@indiana.edu)
Offered: Spring, 2016
Class Time: 1-3 pm
Class Days: W
Capacity: 10 (spring 2016)
Sequence: n/a
Pre-Requisites: Y575 or equivalent (e.g., S501) is required.
Algebra Required: Yes (for notation); No (for proofs) No (for homework)
Calculus Required: Yes--occasionally (for concepts); No (for derivations); No (for homework)
Contact Person for Authorization: Departmental authorization from POLS graduate assistant (Amanda Campbell, acperry@indiana.edu)
Instructor: Armando Razo
Days Per Week Offered: two-hour weekly meeting will include mini-lectures and statistical exercises in a computer lab setting
Website: http://canvas.iu.edu
Recommended follow-up classes: S503; S651 (Social Network Analysis); S681 (Spatial Statistics)
Syllabus: No Syllabus Avaliable
Keywords: contextual, hierarchical, multilevel, HLM, grouped, spatial, multilevel, networks, SNA, interdependence
Description: This course builds upon the linear regression model with various empirical approaches that are useful for the study of contextual political factors. The emphasis will be on developing practical proficiency with the estimation and interpretation of statistical models, but students will also learn how "context" has been conceptualized across different subfields of political science. We will start with a few selected multilevel analysis methods to analyze contextual factors embedded in grouped data. We will then study spatial regression models that incorporate geographical proximity as a way to analyze the contextual importance of physical space. The second half of the semester will focus on the study of networks to understand recent network-analytic research in political science.
Books: There are not required books. Our main textbook will be an unpublished book manuscript. Articles and other readings will be available online.
Substantive Orientation: social sciences
Statistical Orientation: observational data analysis, statistical computing
Applied/Theoretical: This is a mostly an applied course. Y577 gives a conceptual overview of relevant statistical models, but the focus is on estimation and interpretation.
Formal Computing Lab: Yes
Software: R, Stata
How Software is Used: Software will be mostly used for data analysis with some minimal programming tasks (scripts for data analysis).
Problem Sets: Yes
Data Analysis: Yes
Presentation: Yes, students will be required to either formulate a research design for their own projects or otherwise attempt to replicate a published article
Exams: Two take-home exams
Comments: Syllabus will be available in Canvas site the week before classes start.