Examining the generalizability of research findings from archival data

dc.contributor.authorDelios, A.
dc.contributor.authorClemente, E. G.
dc.contributor.authorWu, T.
dc.contributor.authorTan, H.
dc.contributor.authorWang, Y
dc.contributor.authorGordon, M.
dc.contributor.authorViganola, D.
dc.contributor.authorChen, Z.
dc.contributor.authorDreber, A.
dc.contributor.authorJohannesson, M.
dc.contributor.authorPfeiffer, T.
dc.contributor.authorUhlmann, E. L.
dc.contributor.authorAmaral-Baptista, M.
dc.contributor.authorWright, J. D.
dc.contributor.authorXia, Q.
dc.contributor.authorXie, Z.
dc.contributor.authorYoon, S.
dc.contributor.authorYuan, W.
dc.contributor.authorYuan, L.
dc.contributor.authorGeneralizability Tests Forecasting Collaboration
dc.date.accessioned2023-05-17T08:58:10Z
dc.date.available2023-05-17T08:58:10Z
dc.date.issued2022
dc.date.updated2023-05-17T09:57:52Z
dc.description.abstractThis 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.eng
dc.description.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.citationDelios, A., Clemente, E. G., Wu, T., Tan, H., Wang, Y., Gordon, M., Viganola, D., Chen, Z., Dreber, A., Johannesson, M., Pfeiffer, T., Uhlmann, E. L., Amaral-Baptista, M., Wright, J. D., Xia, Q., Xie, Z., Yoon, S., Yuan, W., Yuan, L., & Generalizability Tests Forecasting Collaboration (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), e2120377119. http://dx.doi.org/10.1073/pnas.2120377119
dc.identifier.doi10.1073/pnas.2120377119
dc.identifier.issn0027-8424
dc.identifier.urihttp://hdl.handle.net/10071/28603
dc.language.isoeng
dc.number30
dc.peerreviewedyes
dc.publisherNational Academy of Sciences
dc.relation72002158
dc.relationR-313-000-131-115
dc.relationSFB F63
dc.rightsopen access
dc.subject.fosDomínio/Área Científica::Ciências Sociais::Economia e Gestãopor
dc.titleExamining the generalizability of research findings from archival dataeng
dc.typearticle
dc.volume119
dspace.entity.typePublicationen
iscte.alternateIdentifiers.scopus2-s2.0-85134491120
iscte.alternateIdentifiers.wosWOS:000957256900001
iscte.identifier.cienciahttps://ciencia.iscte-iul.pt/id/ci-pub-89971
iscte.journalProceedings of the National Academy of Sciences of the United States of America
iscte.subject.odsEducação de qualidadepor

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