2017 PORES Summer Fellowship Program

Program Date: May 15- July 21, 2017

Application Deadline: February 24

The Penn Program on Opinion Research and Election Studies (PORES) is soliciting applications for its 2017 Summer Research Fellowship. The fellowship runs from mid May through the end of July, pairing rising Penn sophomores, juniors, and seniors with faculty mentors who are conducting public opinion and election research. PORES is a relatively new research program within the Department of Political Science that aims to train students in the science of polling and analysis of election data. PORES has strategic relationships with NBC, SurveyMonkey and TargetSmart, among others.

This is a fulltime position for ten weeks. Fellows are expected to be in residence for a bulk of May 15- July 21, although there is some flexibility in the exact weeks a fellow would work. Students will earn $11/hr and are expected to work approximately 35 hours/week.  In addition to assisting PORES faculty on research, students attend weekly labs that teach them about the statistical programs that their faculty mentors utilize. Fellows may also have the opportunity to visit NBC in New York City.

Project possibilities may include but are not limited to:

  • Assisting in the drafting of survey questions and administration of polls
  • Compiling large datasets of historic public opinion and election returns
  • Analyzing the effect of various elements of election administration of election outcomes
  • Constructing information about the demographics of electoral districts
  • Writing memos that summarize previous academic research

Candidate Qualifications:

Applicants should have an interest in public opinion research, politics and voting habits. Students should be detail-oriented and have a strong writing background. Many projects require fellows to analyze data using statistics. Thus, we also seek candidates with a desire to learn new statistical computing skills. Please highlight in your statement of interest any previous experiences with:

  • Data-visualization
  • Microsoft Excel
  • Python
  • Statistical Software (such as R, STATA, SPSS)