Examining the generalizability of research findings from archival data

Andrew Delios; Elena Guilia 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

doi:10.1073/pnas.2120377119

Examining the generalizability of research findings from archival data

Andrew Delios^*, Elena Guilia 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^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	e2120377119
Number of pages	9
Journal	Proceedings of the National Academy of Sciences of the United States of America
Volume	119
Issue number	30
Early online date	19 Jul 2022
DOIs	https://doi.org/10.1073/pnas.2120377119
Publication status	Published - 26 Jul 2022

Keywords

Research reliability
Generalizability
Archival data
Reproducibility
Context sensitivity

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Copyright © 2022 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
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Delios, A., Clemente, E. G., Wu, T., Tan, H., Wang, Y., Gordon, M., Viganola, D., Chen, Z., Dreber, A., Johannesson, M., Pfeiffer, T., Generalizability Tests Forecasting Collaboration, & Uhlmann, E. L. (2022). Examining the generalizability of research findings from archival data. Proceedings of the National Academy of Sciences of the United States of America, 119(30), Article e2120377119. https://doi.org/10.1073/pnas.2120377119

@article{b0806f37c85f4880ba03af706e3e367d,

title = "Examining the generalizability of research findings from archival data",

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.",

keywords = "Research reliability, Generalizability, Archival data, Reproducibility, Context sensitivity",

author = "Andrew Delios and Clemente, {Elena Guilia} and Tao Wu and Hongbin Tan and Yong Wang and Michael Gordon and Domenico Viganola and Zhaowei Chen and Anna Dreber and Magnus Johannesson and Thomas Pfeiffer and {Generalizability Tests Forecasting Collaboration} and Uhlmann, {Eric Luis} and Conny Wollbrant and Cinla Akinci",

note = "This research project benefitted from 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 Europ{\'e}en 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.",

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Delios, A, Clemente, EG, Wu, T, Tan, H, Wang, Y, Gordon, M, Viganola, D, Chen, Z, Dreber, A, Johannesson, M, Pfeiffer, T, Generalizability Tests Forecasting Collaboration & Uhlmann, EL 2022, 'Examining the generalizability of research findings from archival data', Proceedings of the National Academy of Sciences of the United States of America, vol. 119, no. 30, e2120377119. https://doi.org/10.1073/pnas.2120377119

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AU - Delios, Andrew

AU - Clemente, Elena Guilia

AU - Wu, Tao

AU - Tan, Hongbin

AU - Wang, Yong

AU - Gordon, Michael

AU - Viganola, Domenico

AU - Chen, Zhaowei

AU - Dreber, Anna

AU - Johannesson, Magnus

AU - Pfeiffer, Thomas

AU - Generalizability Tests Forecasting Collaboration

AU - Uhlmann, Eric Luis

AU - Wollbrant, Conny

AU - Akinci, Cinla

N1 - This research project benefitted from 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 Européen 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.

PY - 2022/7/26

Y1 - 2022/7/26

N2 - 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.

AB - 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.

KW - Research reliability

KW - Generalizability

KW - Archival data

KW - Reproducibility

KW - Context sensitivity

U2 - 10.1073/pnas.2120377119

DO - 10.1073/pnas.2120377119

M3 - Article

SN - 0027-8424

VL - 119

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

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

IS - 30

M1 - e2120377119

ER -

Examining the generalizability of research findings from archival data

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Keywords

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