Using Multiple Generator Random Interpreters (MGRIs) for Studying Undergraduate Student Support Networks (CCWT, 2023)

Jang-Tucci, K., Benbow, R. J., & Bañuelos, N., (2023). Using Multiple Generator Random Interpreters (MGRIs) for Studying Undergraduate Student Support Networks. Networks & Cultural Assets Project. Center for Research on College-Workforce Transitions. University of Wisconsin–Madison, Division of Continuing Studies.

Abstract: Researchers in higher education who study social support networks—groups of interpersonal relationships through which individuals exchange help, advice, and guidance (Wasserman & Faust, 1994)—widely use name generators and interpreters in surveys. “Name generators” are questions that elicit the names of people with whom survey respondents exchange information or discuss certain topics. After collecting these names, surveys often include “name interpreters” that ask respondents to provide information on the people who have been listed, including, for example, each person’s role in the respondent’s life, their education level, how close the respondent feels affectively to each person, etc. This research brief introduces the Multiple Generator Random Interpreter (MGRI; Marin & Hampton, 2007), a method for collecting personal or “ego” network data, as an alternative to traditional name generators and interpreters in social network research. Specifically, we focus on: (1) How MGRIs are different from Traditional Name Generators and Interpreters (TNGIs), and (2) What new insights can be yielded from using MGRIs when assessing college students’ support networks. We answer  with a review of social network literature, and then focus on  describing research methods and empirical evidence from two studies we have conducted of Latino/a/x/e (hereinafter “Latine”) college students in two U.S. states. We conclude with insights from our analyses and links to resources for implementing MGRIs in online surveys.