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Examining the generalizability of research findings from archival data
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;...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9335312/ https://www.ncbi.nlm.nih.gov/pubmed/35858443 http://dx.doi.org/10.1073/pnas.2120377119 |
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author | Delios, Andrew Clemente, Elena Giulia Wu, Tao Tan, Hongbin Wang, Yong Gordon, Michael Viganola, Domenico Chen, Zhaowei Dreber, Anna Johannesson, Magnus Pfeiffer, Thomas Uhlmann, Eric Luis |
author_facet | Delios, Andrew Clemente, Elena Giulia Wu, Tao Tan, Hongbin Wang, Yong Gordon, Michael Viganola, Domenico Chen, Zhaowei Dreber, Anna Johannesson, Magnus Pfeiffer, Thomas Uhlmann, Eric Luis |
author_sort | Delios, Andrew |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9335312 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-93353122022-07-30 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 Uhlmann, Eric Luis Proc Natl Acad Sci U S A Social Sciences 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. National Academy of Sciences 2022-07-19 2022-07-26 /pmc/articles/PMC9335312/ /pubmed/35858443 http://dx.doi.org/10.1073/pnas.2120377119 Text en Copyright © 2022 the Author(s). Published by PNAS https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Social Sciences Delios, Andrew Clemente, Elena Giulia Wu, Tao Tan, Hongbin Wang, Yong Gordon, Michael Viganola, Domenico Chen, Zhaowei Dreber, Anna Johannesson, Magnus Pfeiffer, Thomas Uhlmann, Eric Luis Examining the generalizability of research findings from archival data |
title | Examining the generalizability of research findings from archival data |
title_full | Examining the generalizability of research findings from archival data |
title_fullStr | Examining the generalizability of research findings from archival data |
title_full_unstemmed | Examining the generalizability of research findings from archival data |
title_short | Examining the generalizability of research findings from archival data |
title_sort | examining the generalizability of research findings from archival data |
topic | Social Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9335312/ https://www.ncbi.nlm.nih.gov/pubmed/35858443 http://dx.doi.org/10.1073/pnas.2120377119 |
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