Statistical assessment of peer opinions in higher education rankings: The case of US engineering graduate programs

Purpose: Unlike many other quantitative characteristics used to determine higher education rankings, opinion-based peer assessment scores and the factors that may influence them are not well understood. Using peer scores of US colleges of engineering as reported annually in US News and World Report (USNews) rankings, the purpose of this paper is to provide some insights into peer assessments by statistically identifying factors that influence them. Design/methodology/approach: With highly detailed data, a random parameters linear regression is estimated to statistically identify the factors determining a college of engineering’s average USNews peer assessment score. Findings: The findings show that a wide variety of college- and university-specific attributes influence average peer impressions of a university’s college of engineering including the size of the faculty, the quality of admitted students and the quality of the faculty measured by their citation data and other factors. Originality/value: The paper demonstrates that average peer assessment scores can be readily and accurately predicted with observable data on the college of engineering and the university as a whole. In addition, the individual parameter estimates from the statistical modeling in this paper provide insights as to how specific college and university attributes can help guide policies to improve an individual college’s average peer assessment scores and its overall ranking.