TY - JOUR
T1 - Use of Latent Profile Analysis to Model the Translation of University Research into Health Practice and Policy
T2 - Exploration of Proposed Metrics
AU - Vernon, Marlo M.
AU - Yang, Frances Margaret
N1 - Funding Information:
The authors would like to acknowledge Dr. Andrew Balas for his contributions to the early development of evaluation metrics. We received no funding for this work.
Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Nature B.V.
PY - 2023/11
Y1 - 2023/11
N2 - The aim of this study is to profile academic institutions (n = 127) based on publications, citations in the top 10% of journals, patent citations in Food and Drug Administration (FDA) approvals, clinical trials with uploaded results, contributions to clinical practice guidelines, awarded patents, start-ups, and licenses generating income in response to the Association of University Technology Managers (AUTM) Licensing Activity Survey: Fiscal Years 2011–2015. Latent variable modeling (LVM) was conducted in Mplus v.8.1, specifically latent profile analysis (LPA) was utilized to predict institutional profiles of research, which were compared with the 2015 Carnegie Classification System ranks. Multivariate regression of profile assignment on research expenditure and income generated by licensure was used to show concurrent validity. The LPA resulted in three profiles as the most parsimonious model. Mantel-Haenszel test of trend to the Carnegie Classification found a positive and significant association among institution rankings (r = 0.492, χ2(1) = 26.69, p < 0.001). Profile assignment significantly predicted differences in research expenditure and income generated by licensure. By classifying academic institutions into improving, mobilizing and thriving translational research profiles allows for a universal metric of translation of science from basic or bench to practice or policy.
AB - The aim of this study is to profile academic institutions (n = 127) based on publications, citations in the top 10% of journals, patent citations in Food and Drug Administration (FDA) approvals, clinical trials with uploaded results, contributions to clinical practice guidelines, awarded patents, start-ups, and licenses generating income in response to the Association of University Technology Managers (AUTM) Licensing Activity Survey: Fiscal Years 2011–2015. Latent variable modeling (LVM) was conducted in Mplus v.8.1, specifically latent profile analysis (LPA) was utilized to predict institutional profiles of research, which were compared with the 2015 Carnegie Classification System ranks. Multivariate regression of profile assignment on research expenditure and income generated by licensure was used to show concurrent validity. The LPA resulted in three profiles as the most parsimonious model. Mantel-Haenszel test of trend to the Carnegie Classification found a positive and significant association among institution rankings (r = 0.492, χ2(1) = 26.69, p < 0.001). Profile assignment significantly predicted differences in research expenditure and income generated by licensure. By classifying academic institutions into improving, mobilizing and thriving translational research profiles allows for a universal metric of translation of science from basic or bench to practice or policy.
KW - Benefit of research
KW - Factor analysis
KW - Institutional evaluation
KW - Latent variable modeling
KW - Translational research
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U2 - 10.1007/s11162-023-09735-w
DO - 10.1007/s11162-023-09735-w
M3 - Article
AN - SCOPUS:85151720528
SN - 0361-0365
VL - 64
SP - 1058
EP - 1070
JO - Research in Higher Education
JF - Research in Higher Education
IS - 7
ER -