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More than fun and games? Understanding the role of school-based mentor-mentee match activity profiles in relationship processes and outcomes

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  • Kanchewa, Stella
  • Christensen, Kirsten M.
  • Poon, Cyanea Y.S.
  • Parnes, McKenna
  • Schwartz, Sarah

Abstract

This study investigated associations between mentor-mentee activities and relationship outcomes of school-based mentoring. Multigroup three-step latent profile analysis (LPA) assessed whether youth could be classified into distinct profiles based on their mentoring relationship activities, and whether these profiles were similar when taking into account youth academic success. Furthermore, regression analyses examined whether activity profile membership of mentored youth predicted youth relationship process outcomes. Participants (N = 1110) were drawn from a national, randomized longitudinal study of youth in Big Brothers Big Sisters School-Based Mentoring Programs. Multigroup LPA results indicated a two-profile model, labeled Balanced and Instructional based on varying engagement in mentoring activities. Descriptive analyses revealed no differences in youth, mentor, and program characteristics across groups. Controlling for these characteristics, mentees in the Balanced profile demonstrated more relational benefits than youth in the Instructional profile. Research and practice implications are discussed.

Suggested Citation

  • Kanchewa, Stella & Christensen, Kirsten M. & Poon, Cyanea Y.S. & Parnes, McKenna & Schwartz, Sarah, 2021. "More than fun and games? Understanding the role of school-based mentor-mentee match activity profiles in relationship processes and outcomes," Children and Youth Services Review, Elsevier, vol. 120(C).
    • Handle: RePEc:eee:cysrev:v:120:y:2021:i:c:s0190740920321794
    DOI: 10.1016/j.childyouth.2020.105757
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    References listed on IDEAS

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    1. Vermunt, Jeroen K., 2010. "Latent Class Modeling with Covariates: Two Improved Three-Step Approaches," Political Analysis, Cambridge University Press, vol. 18(4), pages 450-469.
    2. Bayer, Amanda & Grossman, Jean & DuBois, David, 2015. "Using Volunteer Mentors to Improve the Academic Outcomes of Underserved Students: The Role of Relationships," MPRA Paper 85106, University Library of Munich, Germany.
    3. G. J. McLachlan, 1987. "On Bootstrapping the Likelihood Ratio Test Statistic for the Number of Components in a Normal Mixture," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 318-324, November.
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