A Dynamic Dyadic Systems Perspective on Communication of Real-Time Support Between Graduate Women in STEM and Their Mentor

Women of Color (WoC) in science, technology, engineering, and math (STEM) leave doctoral programs at disproportionately high rates. Supportive mentorship is key to increasing belonging and rates of retention, yet little is known about how conversations between mentees and their mentors on academic and personal stress topics unfold in real-time. Applying the lens of Social Cognitive Career Theory to communication dynamics between mentees and mentors, the present study utilized a dynamic dyadic systems (DDS) perspective to examine observationally coded data from six mentee-mentor dyads. First, hierarchical clustering analysis was applied to identify speaking turn types. Then, sequence analysis was used to identify common multi-turn patterns or conversation motifs (CM). Results showed five predominant CMs: (CM1) support provision through listening; (CM2) focus on mentor’s experience; (CM3) support provision through advice; (CM4) mentee’s making a bid for support; and (CM5) mentor dominated conversations. This study demonstrates methods for identifying potentially meaningful patterns of support in stress conversations between mentees and mentors. The application of such methods with larger samples may aid in understanding ways to increase retention among WoC in STEM through mentor support provision.