Quality Assessment of the Academic Freedom Index: Strengths, Weaknesses, and How Best to Use It - Lott, Lars; Spannagel, Janika (Working paper series, 2023)
This paper reviews the data quality of the first systematic global measurement of academic freedom, namely the Academic Freedom Index (AFI) by using a data quality assessment approach proposed by McMann et al. (2022). By analyzing three distinct components of data quality (content validity, the data generation process, and convergent validity), this article examines the specific strengths and potential shortcomings of the AFI. The findings indicate that the AFI does well in terms of its theoretical embeddedness (within some conceptual limits), of the transparent data generation process and the handling of expert assessments, as well as of its temporal and spatial coverage. A critical assessment of the level of disagreement between expert coders further shows that there are few systematic predictors, providing no evidence for problematic biases among AFI coders. Overall, we conclude that the data quality of the AFI is comparatively high but that it could be further increased by recruiting even more experts and thereby enhancing the Bayesian IRT model’s performance
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments June 2023 erstellt