Examining the generalizability of research findings from archival data

Delios, Andrew; Clemente, Elena Giulia; Wu, Tao; Tan, Hongbin; Wang, Yong; Gordon, Michael; Viganola, Domenico; Chen, Zhaowei; Dreber, Anna; Johannesson, Magnus; Pfeiffer, Thomas; Generalizability Tests Forecasting Collaboration; Uhlmann, Eric Luis

Abstract

This initiative examined systematically the extent to which a large set of archival research findings generalizes across contexts. We repeated the key analyses for 29 original strategic management effects in the same context (direct reproduction) as well as in 52 novel time periods and geographies; 45% of the reproductions returned results matching the original reports together with 55% of tests in different spans of years and 40% of tests in novel geographies. Some original findings were associated with multiple new tests. Reproducibility was the best predictor of generalizability-for the findings that proved directly reproducible, 84% emerged in other available time periods and 57% emerged in other geographies. Overall, only limited empirical evidence emerged for context sensitivity. In a forecasting survey, independent scientists were able to anticipate which effects would find support in tests in new samples.

Citation

Delios, A., Clemente, E. G., Wu, T., Tan, H., Wang, Y., Gordon, M., …Uhlmann, E. L. (2022). Examining the generalizability of research findings from archival data. Proceedings of the National Academy of Sciences, 119(30), Article e2120377119. https://doi.org/10.1073/pnas.2120377119

• Journal Article Type: Article
• Acceptance Date: Jun 8, 2022
• Online Publication Date: Jul 19, 2022
• Publication Date: Jul 26, 2022
• Deposit Date: Jul 20, 2022
• Publicly Available Date: Jul 21, 2022
• Journal: Proceedings of the National Academy of Sciences of the United States of America
• Print ISSN: 0027-8424
• Electronic ISSN: 1091-6490
• Publisher: National Academy of Sciences
• Peer Reviewed: Peer Reviewed
• Volume: 119
• Issue: 30
• Article Number: e2120377119
• DOI: https://doi.org/10.1073/pnas.2120377119
• Keywords: Multidisciplinary
• Public URL: https://nottingham-repository.worktribe.com/output/9089236
• Publisher URL: https://www.pnas.org/doi/full/10.1073/pnas.2120377119
• Additional Information: Gerardus Lucas was one of the co-authors who lent their time and expertise as contributors to the forecasting study and credited as part of the Generalized Tests Forecasting Collaboration.