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 journalArticlepeer-review

    15 Citations (Scopus)
    10 Downloads (Pure)


    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 languageEnglish
    Article numbere2120377119
    Number of pages9
    JournalProceedings of the National Academy of Sciences of the United States of America
    Issue number30
    Early online date19 Jul 2022
    Publication statusPublished - 26 Jul 2022


    • Research reliability
    • Generalizability
    • Archival data
    • Reproducibility
    • Context sensitivity


    Dive into the research topics of 'Examining the generalizability of research findings from archival data'. Together they form a unique fingerprint.

    Cite this