How do mentoring relationships grow over time? New study has answers!

Spiekermann, L., Lyons, M., & Deutsch, N. (2021). A mixed-methods approach to understanding trajectories of mentoring relationship growth. Journal of Community Psychology. https://doi.org/10.1002/jcop.22648

Summarized by Ariel Ervin

Notes of Interest: 

• Although mentors play a vital role in mentoring relationships, there isn’t a consensus on how mentors should develop and maintain quality relationships with their mentees over time.
• Because of this, mentoring program guidelines tend to not go into detail about mentor behaviors.
• Many mentoring scholars tend to evaluate single time-point measurements of relationship quality (RQ).
• This study explores how mentoring relationships develop over time.
  • More specifically, it examines developmental patterns of mentoring dyads and assesses how these patterns may be related to various relationship traits (e.g. implementation of mentoring practices and relationship challenges).
• Despite the fact that most mentors stated that they had stable and high levels of mentor-reported quality, there was notable variability between mentors in terms of how relationship quality shifted over time.
• Five patterns  were identified:
  • Progressive
  • Stable-high
  • Dip and Recovery
  • Stable-low
  • Regressive
• Because some mentors tend to have unrealistic expectations that can lead to early mentorship termination, it’s important for programs to inform them that “dips” in relationship quality are normal and don’t indicate that the relationship is doing poorly.
• Findings indicate that it’s plausible for programs to assess how mentoring relationships are doing from time to time without burdening mentors and/programmers.

Introduction (Reprinted from the Abstract)

The proposed study integrates quantitative and qualitative approaches to examine mentors with different relationship trajectories reflect on their relationships. Using quantitative and qualitative methods, mentor reports of relationship quality are plotted over time and different growth patterns identified: (1) progressive, (2) stable-high, (3) dip and recovery, (4) stable-low, and (5) regressive. Qualitative coding was used to identify patterns in mentors’ descriptions of their relationship experiences—including both what mentors wrote about and how they wrote about it.

Implications (Reprinted from the Discussion)

Although mentoring programs are one of the most popular forms of prevention programs (Bruce & Bigdgeland, 2014), these programs are often associated with, on average, small positive effects (DuBois et al., 2011). New research also suggests that average effects may mask heterogeneity of treatment effects within mentoring relationships—with some mentoring relationships yielding larger positive effects and others null or negative effects (Lyons & McQuillin, 2018, 2021). To understand interindividual differences in the formation of mentoring relationships, this study used quantitative data on mentor-reported RQ and qualitatively coded different patterns in relationship growth over the course of a school year. Then, we examined patterns in the relationship trajectories and strategies mentors used to support a positive mentor–mentee relationship. In doing so, we hoped to provide new insight into the ways in which mentoring relationships differentiate support depending on the trajectories of mentoring relationships.

Overall, most mentors in this sample reported generally high, stable levels of mentor reported quality across the relationship. However, there was significant variability between mentors in how RQ changed over time. These findings are consistent with Pryce and Keller’s (2012) framework, which identified four unique relationship growth patterns. These four patterns were also identified in this study as well as a novel fifth pattern: stable-high, progressive, dip and recovery, stable-low, and regressive. In this sample, stable-high relationships were the most common type, followed by dip and recovery, progressive, stable-low, and regressive. These results are also consistent with the notion that interindividual differences (i.e., differences across mentor–mentor dyads) exist in mentoring programs and that these differences necessitate differentiation of mentoring practices based on the particular needs and competencies of the mentor–mentee (Grossman & Rhodes, 2002; Lyons & McQuillin, 2021).

In addition, results provide new insight into specific mentoring practices that programs might use to guide how to train and support mentors facing difficulty building and maintaining a positive mentoring relationship. Comparisons of how mentors reflected on their relationships across the different relationship types suggest that mentors in more successful relationships (e.g., stable high and progressive, recovery) write in ways that reflect taking more responsibility for building the relationship and overcoming barriers. Results also suggested that these mentors also overcame initial expectations of the relationship by allowing their mentee to guide their expectations. On the other hand, results suggested that mentors in less successful relationships (regressive and stable low) tended to report a more passive approach to problem solving that places more responsibility on their mentee. Furthermore, while they often acknowledge their unrealistic expectations in their reflections, they did so in a way that suggested more difficulty overcoming them. Together, these results are consistent with prior work suggesting that mentoring relationships characterized by mutual support and collaboration tend to be associated with positive trajectories over time (Pryce, 2012; Spencer, 2007).

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