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Statistical Assessment of Peer Opinions in Higher Education Rankings: The Case of U.S. Engineering Graduate Programs

Ghaisi, Amir; Fountas, Grigoious; Anastasopoulos, Panagiotis; Mannering, Fred

Authors

Amir Ghaisi

Panagiotis Anastasopoulos

Fred Mannering



Abstract

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 U.S. colleges of engineering as reported annually in US News and World Report (USNews) rankings, this paper seeks to provide some insight into peer assessments by statistically identifying factors that influence them. With highly detailed data, a random parameters linear regression is estimated to statistically identify factors determining a college of engineering’s average USNews peer assessment score. 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. Our estimation results show 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 our statistical modeling 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.

Journal Article Type Article
Acceptance Date Dec 8, 2018
Publication Date Jul 1, 2019
Deposit Date Jan 23, 2019
Publicly Available Date Jul 1, 2019
Journal Journal of Applied Research in Higher Education
Print ISSN 1758-1184
Publisher Emerald
Peer Reviewed Peer Reviewed
Volume 11
Issue 3
Pages 481-492
DOI https://doi.org/10.1108/JARHE-09-2018-0196
Keywords University rankings; peer assessments; random parameters; linear regression
Public URL http://researchrepository.napier.ac.uk/Output/1526550
Contract Date Jan 23, 2019

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