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Examining the generalizability of research findings from archival data

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posted on 2023-04-14, 14:58 authored by Andrew Delios, Elena Giulia Clemente, Tao Wu, Hongbin Tan, Yong Wang, Michael Gordon, Domenico Viganola, Zhaowei Chen, Anna Dreber, Magnus Johannesson, Thomas Pfeiffer, Generalizability Tests Forecasting Collaboration, Eric Luis Uhlmann
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.

Funding

Ministry of Education (Singapore) Tier 1 Grant R-313-000-131-115 (to A. Delios), National Science Foundation of China Grants 72002158 (to H.T.) and 71810107002 (to H.T.), grants from the Knut and Alice Wallenberg Foundation (to A. Dreber) and the Marianne and Marcus Wallenberg Foundation (through a Wallenberg Scholar grant; to A. Dreber), Austrian Science Fund (FWF) Grant SFB F63 (to A. Dreber), grants from the Jan Wallander and Tom Hedelius Foundation (Svenska Handelsbankens Forskningsstiftelser;to A. Dreber), and an Research & Development (R&D) research grant from Institut European d’Administration des Affaires (INSEAD) (to E.L.U.). Dmitrii Dubrov, of the G.T.F.C., was supported by the National Research University Higher School of Economics (HSE University) Basic Research Program

History

Citation

Delios, Andrew, Elena Giulia Clemente, Tao Wu, Hongbin Tan, Yong Wang, Michael Gordon, Domenico Viganola et al. "Examining the generalizability of research findings from archival data." Proceedings of the National Academy of Sciences 119, no. 30 (2022): e2120377119.

Author affiliation

School of Psychology and Vision Sciences

Version

  • VoR (Version of Record)

Published in

Proceedings of the National Academy of Sciences of the United States of America

Volume

119

Issue

30

Pagination

e2120377119

Publisher

Proceedings of the National Academy of Sciences

issn

0027-8424

eissn

1091-6490

Acceptance date

2022-06-08

Copyright date

2022

Available date

2023-04-14

Language

eng

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