JISARA

Abstract: This study introduces a data-driven method to select peer institutions in higher education for faculty salary comparison. Given a target institution, the goal is to form a peer group of similar colleges using stakeholder-identified variables like enrollment, finances, and student outcomes, but excluding salary data. An effective peer group places the target near the median salary level. Previous work raised equity concerns because the methodology generated separate peer groups, including one for base salaries and several for high-demand accredited disciplines. Concerns about objectivity and fairness emerged due to the use of subjective filters and post-hoc adjustments, such as including aspirational institutions. We seek a more consistent, data-driven approach that uses principal component analysis and nearest neighbor search to create a single unified peer group that can be used for all salary comparisons. By employing a more transparent, analytics-based method, we aim to enhance trust in the process to promote acceptance of the peer group among faculty and administrative stakeholders.

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Recommended Citation: Breimer, E.A., Kim, S., Wang, S., (2026). Data-Driven Peer Group Selection for Salary Comparison in Higher Education: An Applied Analytics Approach to Building Trust. Journal of Information Systems Applied Research and Analytics 19(4) pp 70-82. https://doi.org/10.62273/JSBD5673