Program for Quantitative and Analytical Political Science

Welcome to QAPS

The Program for Quantitative and Analytical Political Science (QAPS) was established in 2009 to support theoretical and quantitative research in political science and its dissemination. We support graduate students through QAPS fellowships, host post-doctoral research fellows, offer statistical and formal theory consulting, hold quantitative skills workshops, throw conferences, and organize the Quantitative Social Science colloquium


End of an Era
Dec. 16, 2019

After four and a half years at QAPS, Will Lowe is leaving the university to become Senior Research Scientist at the Hertie School of Governance in Berlin. This position at QAPS will not be refilled, so until further notice this means that:

There will be no more QAPS computational skills…
QAPS Workshop Change
April 15, 2019

This Wednesday's workshop will now be about cleaning, structuring, and visualizing data with R (not about R package construction, as previously advertised*).

It will take place in Fisher Hall, Room 200 starting at 10:30am on Wednesday, April 17th. 

More details can be found here:

QAPS workshop canceled
March 20, 2019

Reminder: This morning's QAPS workshop (April 3, 2019) has been canceled.

Future Events

No upcoming events found.

Previous Events

Colloquium: Bruce Desmarais
Fri, Apr 12, 2019, 12:00 pm12:00 pm

Bruce Desmarais
Department of Political Science, Penn State University

Title: Network Event History Analysis for Modeling Public Policy Adoption with Latent Diffusion Networks

Research on the diffusion of public policies across…

Corwin 127
Colloquium: Tom Griffiths (CANCELED)
Fri, Apr 5, 2019, 12:00 pm12:00 pm

Canceled due to a conflict with politics department events.

Corwin 127
Workshop: Parallel Processing in R on the Clusters (CANCELED)
Wed, Apr 3, 2019, 10:30 am10:30 am

This workshop introduces the university's cluster computers and shows how to run your R code on them. This is helpful when your code takes a long time run or is too large to run efficiently on your own machine.

We will start by running ordinary R code on just a single node of the cluster, effectively using it as an extra computer,  We…

Wallace Hall 333