Hierarchical Bayesian model to estimate and compare research productivity of Italian academic statisticians

Maura Mezzetti () and Ilia Negri
Additional contact information
Maura Mezzetti: Università degli Studi di Roma Tor Vergata
Ilia Negri: Università della Calabria

Scientometrics, 2024, vol. 129, issue 12, No 1, 7443-7474

Abstract: Abstract A new method for measuring scientific productivity is proposed. Each researcher is initially associated with a cumulative score over time, reflecting the quality of the papers based on the journals in which they have published throughout their career. The second measure, an average speed over time from varying production speeds, is derived through the estimation of a two-level hierarchical Bayesian model for piecewise linear regression. These productivity indicators are validated and compared to other commonly used bibliometric indexes. The proposed method is applied to compare the productivity of females and males at different career levels in Italian academia, with a focus on statisticians. The study also contributes to the literature on the gender gap, showing that among those who remain at the lower levels of the university career hierarchy, women tend to have higher and more consistent scientific production over time compared to their male colleagues.

Keywords: Research evaluation; Bayesian hierarchical model; Bibliometrics; Gender gap (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11192-024-05154-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:129:y:2024:i:12:d:10.1007_s11192-024-05154-5

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-024-05154-5

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().