image of interns posing in Perry building atrium

Summer Undergraduate Internship Program

The ICPSR summer internship program provides undergraduate students with a unique and expansive research experience that introduces all aspects of social science research and includes supported exploration of a research query from start to finish, data management training, and focused methodological education in quantitative research. This prepares interns for capstone or senior thesis projects, graduate school, and/or research-based employment opportunities. The students, under the supervision of faculty mentors, develop a research question, perform a literature search and review, complete data analysis, and report findings in a poster; learn good data management processes and research practices with a research process mentor; and attend classes at the ICPSR Summer Program in Quantitative Methods.

Additionally, regularly planned luncheon meetings focus on research projects within ICPSR/ISR, ethics in research and data management, and life in graduate school or in a research career obtainable with a Bachelor's degree in the social sciences. In the last week of the internship, the students display their work in a poster session for all faculty and staff. They leave ICPSR with a poster and abstract suitable for submission into a local/regional social science professional organization meeting of their choosing.

The internship program is excellent preparation for advanced studies and careers in data science and social science research.

Internship Structure

Interns spend 10 weeks from mid-June through mid-August at ICPSR (Ann Arbor, Michigan), during which they will:

  • Work in small groups and with faculty mentors to complete research projects resulting in conference-ready posters
  • Gain experience using statistical programs such as SAS, SPSS, and Stata to check data, working in both UNIX and Windows environments
  • Select and attend graduate-level courses in the Summer Program in Quantitative Methods of Social Research
  • Participate in a weekly Lunch and Lecture series that covers topics related to social science

How to Apply

The following documents are required for a complete application:

  • A cover letter or letter of interest
  • Resume or CV
  • Two letters of recommendation
  • List of relevant courses
  • Contact information for the required two professional or faculty references:

LETTERS OF REFERENCE ARE REQUIRED

Two (2) Letters of Reference with your name and/or applicant identification number from this system must be submitted through the Recommendation Portal

Deadline

Applications due by January 31, 2014. Please note that late or incomplete applications will not be considered for this opportunity.

Eligibility

  • Expected graduation of Dec. 2014 or later
  • United States citizenship or permanent residency
  • Undergraduate standing and completion of sophomore year in a social science or mathematics major, with interests related to one of ICPSR's Thematic Collections

Qualifications

Strong academic credentials

  • Knowledge of a statistical software package such as SPSS, SAS, or Stata
  • Previous experience with social science research via work or class project
  • Demonstrated leadership, problem-solving, and strong verbal and written communication skills
  • Ability to prioritize tasks, work on multiple assignments at once, and manage ambiguity
  • Ability to work both independently and as part of a team with professionals at all levels

Compensation

$4,000 stipend, room and partial-board in university housing, and a scholarship covering the cost of fees, texts, and materials for coursework in the ICPSR Summer Program

Questions and More Information

If you have any questions about the program, please contact Program Manager Abay Israel

NSF Logo The Quantitative Social Science Research at the University of Michigan is a National Science Foundation REU site, and receives major funding from the National Science Foundation under Grant No. 1062317. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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