Research Associate (Field) Nurse-Family Partnership
J-PAL North America, based at the Massachusetts Institute of Technology, seeks a highly motivated and skilled Research Associate to support an evaluation of the Nurse Family Partnership (NFP) program, and to contribute to the development of research resources for public use. The primary focus of this position is to lead study fielding efforts for the NFP evaluation by providing technical and communicative support between the research team, NFP staff, and governmental agencies. Responsibilities will include planning and hosting workshops both in-person and online to train survey enumerators in ethical study procedures, identifying and correcting errors in the survey instrument, updating the institutional review board application, among others. This role has the opportunity to work with Principal Investigators Margaret McConnell (Harvard School of Public Health) and Katherine Baicker (University of Chicago) on long-term tasks including researching and drafting literature reviews, contributing to the analysis plan, and securing additional data use agreements.
This RA will also support J-PAL’s research and training team in the development of public good resources that will help researchers conduct new randomized evaluations, and to support states and cities that are launching new evaluations of social programs.
This position is based at J-PAL North America at MIT, in Cambridge, MA. The Department of Economics at MIT is an Equal Opportunity Employer. We encourage applicants from diverse backgrounds and welcome candidates with experience working on randomized evaluations in the United States or abroad.
Responsibilities
Study fielding:
- Train survey enumerators (in-person and via webinar) and provide ongoing technical assistance to program staff in the field.
- Program updates to survey instrument using SurveyCTO
- Identify and program data corrections using Stata for any discrepancies in survey transfer.
- Assist faculty with literature reviews and academic analysis plans.
- Liaise with staff at non-profit organizations implementing the intervention, state policy makers, officials who authorize the evaluation and data sharing agreements, and relevant institutional review boards.
- Contribute to project reports, policy memos, and conference submissions.
- Assist with negotiating, interpreting, and/or writing documents required for research, including data use agreements (DUAs), IRB applications, and MOUs.
- Contribute to public goods promoting best practices, new methods, or resources for conducting high-quality randomized evaluations, such as guides to evaluation design and data security procedures, and creating and presenting onboarding materials for new research staff.
- Contribute to resources that centralize information and best practices on accessing administrative data for use in randomized evaluations. This includes working with external parties to understand the processes for accessing administrative data.
Benefits
This position is eligible for MIT benefits, including comprehensive health insurance, a 401(k) match and pension-based retirement plan, tuition assistance, commuter benefits, MIT community discounts, a generous vacation policy, and more. J-PAL supports a culture of learning, and provides opportunities to attend weekly seminars, engage in small-group discussions with other J-PAL staff, and otherwise facilitates professional growth and engagement for its staff.
Qualifications
Education & experience
- Bachelor’s degree in economics, social sciences, public policy, public health, or a related field, is required.
- Previous experience working on impact evaluations or other quantitative research is strongly preferred; formal post-undergraduate professional experience is recommended, but not required.
- Familiarity with and training in randomized impact evaluations is preferred.
- •Experience in stakeholder interaction and project management is preferred.
Skills
- Advanced writing and presentation skills
- Ability to manage high-level relationships with partner organizations
- Strong quantitative background
- Basic programming skills (e.g. Stata, R)
- Ability to work independently