Simon JackmanDepartment of Political ScienceEncina Hall West, Room 315Stanford Universityjackman@stanford.eduSpring 2015

PS 350C: Political Methodology 3

We consider a variety of statistical models in this class, presuming that students have asound knowledge of the linear regression model for continuous outcomes.

We will begin with a consideration of maximum likelihood estimation, and then turn tomodels for discrete responses: binary (e.g., turning out to vote) ordinal (responses to Likertitems on surveys, rating schemes) and unordered or multinomial outcomes (e.g., voting in amulti-party system). We will also briefly look at models for counts (e.g., number of terroristattacks).

We will then cover the following topics:

• an introduction to Bayesian inference

• hierarchical or multi-level models

• topics in multivariate analysis, i.e., models and methods for measurement and/ordata reduction, as typically arise when combining various indicators to create scalemeasures of concepts (e.g., a country’s level of democracy, a legislator’s ideology, asurvey respondent’s level of racial prejudiuce). We will examine principal components,factor analysis, and item-response models.

• how to handle missing data

Teaching Assistants

Our TAs for this class are

• Mathilde Emariau memeriau@stanford.edu

• Bradley Spahn bspahn@stanford.edu

The TAs will hold section for the class at 9.30-11.30 on Friday, in the ANES seminar room, inthe Center for the Study of American Politics, in the basement of Encina Hall West.

Texts

There is no prescribed text for this class. I’ll supply my own notes for much of the class. Istrongly recommend the book by Gelman and Hill:

Gelman, Andrew and Jennifer Hill. 2006. Data Analysis Using Regression and Multi-level/Hierarchical Models. Cambridge University Press. New York.

For the material on models for discrete dependent variables, older but easily digested textsinclude:

Long, J. Scott. 1997. Regression Models for Categorical and Limited DependentVariables, Sage Publications, Thousand Oaks, California.

Hosmer, David W. and Stanley Lemeshow. 2000. Applied Logistic Regression. Wiley:New York.

Slightly more advanced, but a classic work on the subject is

Agresti, Alan. 2002. Categorical Data Analysis. Wiley: New York.

For a slightly higher level, econometric perspective on models for discrete outcomes, see

Cameron, A. Colin and Pravin K. Trivedi. 2005. Microeconometrics: Methods andApplications. Cambridge University Press: New York.

The following books provide detailed treatments of multinomial choice models:

Hensher, David A, John M. Rose and William H. Greene. Applied Choice Analysis: APrimer. 2005. Cambridge University Press: New York.

Train, Kenneth E. 2009. Discrete Choice Methods with Simulation. 2009. CambridgeUniversity Press: New York.

For our treatment of multivariate analysis, I will rely on

Everitt, Brian and Torsten Hothorn. 2011. An Introduction to Applied MultivariateAnalysis with R. Springer: New York.

My lecture notes will have references to applications and other source materials.

Contact

I’m available by appointment (please either just stop by my office or send an email).

We have a Piazza site for the class (signup). Please use the Q&A section of the Piazza classsite.

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Scheduling Issues

We are scheduled to have class Tuesday and Thursdays at 11am.

Tuesday classes: we have been assigned GESB (Green Earth Sciences Building) 134. Thisroom holds 24 but is a long way from Encina and I expect it will be a very crowded room. I’mlooking for another option closer to Encina, but for now, this is it.

Thursday classes: Graham Stuart Lounge, 4th floor, Encina West.

We will not have class on

• Thursday April 30

• Thursday May 14

I will propose make-up lectures for those sessions.

Assessment

We will have

1. five or six homeworks, comprising data analysis and write-ups and some more analyticalquestions testing your understanding of the models we will encounter

2. a take-home, open-book final exam

with roughly a 60-40 weighting given to the homeworks and the exam, respectively.

Software

As in other methodology classes in the Department of Political Science, we will use R.

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