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

This research brief introduces the Multiple Generator Random Interpreter (MGRI) as an innovative approach to collecting egocentric social network data in higher education research. Contrasting MGRI with Traditional Name Generator and Interpreter (TNGI) methods that cap the number of listed contacts, the brief explains how MGRI allows respondents to list all relevant network members and then randomly samples a subset for follow-up questions, reducing respondent burden while improving representativeness. Drawing on comparative evidence from two studies of Latine college students, one using TNGI and the other using MGRI, the analysis demonstrates that MGRI captures larger and less kin-centered networks, yields lower network density estimates, and better reflects the diversity of students’ academic and career support ties. The findings suggest that MGRI provides a more accurate portrait of undergraduate support networks and enables researchers to examine multiple types of relational contexts within a single survey. The brief concludes with practical guidance and open-access resources for implementing MGRI in online survey platforms.