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How to Measure Field‐of‐Study Mismatch? A Comparative Analysis of the Different Methods
Author(s) -
Sellami Sana,
Verhaest Dieter,
Van Trier Walter
Publication year - 2018
Publication title -
labour
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.403
H-Index - 34
eISSN - 1467-9914
pISSN - 1121-7081
DOI - 10.1111/labr.12129
Subject(s) - measure (data warehouse) , field (mathematics) , reliability (semiconductor) , econometrics , empirical research , computer science , statistics , data mining , mathematics , power (physics) , physics , quantum mechanics , pure mathematics
We compare the different methods used to measure field‐of‐study mismatch. A first part reviews the literature, detailing, and discussing the different approaches. A second part uses a dataset allowing one to investigate whether these different approaches result in differences with respect to the incidence and determinants of field‐of‐study mismatch. As substantial differences do indeed exist, even among variants of similar approaches, we conclude that empirical results should be interpreted with caution. While making several recommendations concerning the measurement of field‐of‐study mismatch, we also call for more focused research on the validity and reliability of field‐of‐study mismatch measures.