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.
Four major components: data processing, secondary research, graduate-level courses, and professional development. Interns spend 10 weeks from June to August in Ann Arbor where they will:
- Prepare study documentation and apply data management techniques to recode, label, transform, and manipulate data for ICPSR studies to be disseminated for secondary research and analysis
- Use data management skills to work in small groups and with research mentors to complete a research project resulting in a conference-ready poster
- Participate in graduate-level courses in the ICPSR Summer Program in Quantitative Methods of Social Research
- Participate in weekly workshops that cover topics related to soclal science research, graduate school, and professional development
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
Applications are due by January 31, 2015. Please note that late or incomplete applications will not be considered for this opportunity.
- Expected graduation of December 2015 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
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
$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
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.