A Model for an Undergraduate Research Experience Program in Quantitative Sciences

Kay See Tan, Elena B. Elkin, Jaya M. Satagopan

Research output: Contribution to journalArticlepeer-review


We developed a summer research experience program within a freestanding comprehensive cancer center to cultivate undergraduate students with an interest in and an aptitude for quantitative sciences focused on oncology. This unconventional location for an undergraduate program is an ideal setting for interdisciplinary training in the intersection of oncology, statistics, and epidemiology. This article describes the development and implementation of a hands-on research experience program in this unique environment. Core components of the program include faculty-mentored projects, instructional programs to improve research skills and domain knowledge, and professional development activities. We discuss key considerations such as fostering effective partnership between research and administrative units, recruiting students, and identifying faculty mentors with quantitative projects. We describe evaluation approaches and discuss post-program outcomes and lessons learned. In its initial two years, the program successfully improved the students’ perception of competence gained in research skills and statistical knowledge across several knowledge domains. The majority of students also went on to pursue graduate degrees in a quantitative field or work in oncology-centric academic research roles. Our research-based training model can be adapted by a variety of organizations motivated to develop a summer research experience program in quantitative sciences for undergraduate students. Supplemental files for this article are available online.

Original languageAmerican English
Pages (from-to)65-74
Number of pages10
JournalJournal of Statistics and Data Science Education
Issue number1
StatePublished - 2022

ASJC Scopus subject areas

  • Statistics and Probability
  • Management Science and Operations Research
  • Education


  • Analysis of biomedical data
  • Applied statistics internship
  • Computational biology
  • Experiential learning
  • Mentoring
  • Statistical training


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