IDEAS home Printed from https://ideas.repec.org/p/zbw/i4rdps/225.html
   My bibliography  Save this paper

The Origins of Reporting Bias: Selective but Unbiased Reporting by Early-Career Researchers?

Author

Listed:
  • Asanov, Anastasiya-Mariya
  • Asanov, Igor
  • Buenstorf, Guido
  • Kadriu, Valon
  • Schoch, Pia

Abstract

Doctoral dissertations provide evidence about research practices in early career-stage research. We examine reporting bias by manually collecting over 94,000 test statistics from a random sample of German dissertations and their follow-up papers worldwide. We observe selective reporting, as only a fraction of the tests in the dissertations is reported in follow-up papers. Unexpectedly, we find no increase in reporting bias in follow-up papers compared to dissertations nor, generally, reporting bias in dissertations or papers. Self-selection into higher-impact journals based on statistical significance may reconcile our finding of selective yet "unbiased" reporting with prior evidence suggesting pervasive reporting bias.

Suggested Citation

  • Asanov, Anastasiya-Mariya & Asanov, Igor & Buenstorf, Guido & Kadriu, Valon & Schoch, Pia, 2025. "The Origins of Reporting Bias: Selective but Unbiased Reporting by Early-Career Researchers?," I4R Discussion Paper Series 225, The Institute for Replication (I4R).
    • Handle: RePEc:zbw:i4rdps:225
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/316397/1/I4R-DP225.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Abel Brodeur & Nikolai M. Cook & Jonathan S. Hartley & Anthony Heyes, 2024. "Do Preregistration and Preanalysis Plans Reduce p-Hacking and Publication Bias? Evidence from 15,992 Test Statistics and Suggestions for Improvement," Journal of Political Economy Microeconomics, University of Chicago Press, vol. 2(3), pages 527-561.
    2. Guido W. Imbens, 2021. "Statistical Significance, p-Values, and the Reporting of Uncertainty," Journal of Economic Perspectives, American Economic Association, vol. 35(3), pages 157-174, Summer.
    3. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "Inference on Treatment Effects after Selection among High-Dimensional Controlsâ€," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(2), pages 608-650.
    4. Eva Vivalt, 2019. "Specification Searching and Significance Inflation Across Time, Methods and Disciplines," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(4), pages 797-816, August.
    5. Anastasiya-Mariya Asanov & Igor Asanov & Guido Buenstorf & Valon Kadriu & Pia Schoch, 2024. "Patterns of dissertation dissemination: publication-based outcomes of doctoral theses in the social sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(4), pages 2389-2405, April.
    6. Cilliers,Jacobus & Nour Elashmawy & David McKenzie, 2024. "Using Post-Double Selection Lasso in Field Experiments," Policy Research Working Paper Series 10931, The World Bank.
    7. Doucouliagos, Hristos & Hinz, Thomas & Zigova, Katarina, 2022. "Bias and careers: Evidence from the aid effectiveness literature," European Journal of Political Economy, Elsevier, vol. 71(C).
    8. Bruns, Stephan B. & Kalthaus, Martin, 2020. "Flexibility in the selection of patent counts: Implications for p-hacking and evidence-based policymaking," Research Policy, Elsevier, vol. 49(1).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anastasiya-Mariya Asanov & Igor Asanov & Guido Buenstorf & Valon Kadriu & Pia Schoch, 2025. "The Origins of Reporting Bias: Selective but Unbiased Reporting by Early-Career Researchers?," MAGKS Papers on Economics 202504, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    2. Graham Elliott & Nikolay Kudrin & Kaspar Wuthrich, 2022. "The Power of Tests for Detecting $p$-Hacking," Papers 2205.07950, arXiv.org, revised Apr 2024.
    3. Costanza Naguib, 2024. "P-hacking and Significance Stars," Diskussionsschriften dp2409, Universitaet Bern, Departement Volkswirtschaft.
    4. Alexandre Belloni & Victor Chernozhukov & Kengo Kato, 2019. "Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 749-758, April.
    5. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
    6. Khanh Duong, 2024. "Is meritocracy just? New evidence from Boolean analysis and Machine learning," Journal of Computational Social Science, Springer, vol. 7(2), pages 1795-1821, October.
    7. Chenchuan (Mark) Li & Ulrich K. Müller, 2021. "Linear regression with many controls of limited explanatory power," Quantitative Economics, Econometric Society, vol. 12(2), pages 405-442, May.
    8. Freitas-Monteiro, Teresa & Ludolph, Lars, 2025. "Barriers to humanitarian migration, victimization and integration outcomes: Evidence from Germany," World Development, Elsevier, vol. 186(C).
    9. Hector Espinoza & Stefan Speckesser, 2019. "A Comparison of Earnings Related to Higher Level Vocational/Technical and Academic Education," National Institute of Economic and Social Research (NIESR) Discussion Papers 502, National Institute of Economic and Social Research.
    10. Guo, Jiaqi & Wang, Qiang & Li, Rongrong, 2024. "Can official development assistance promote renewable energy in sub-Saharan Africa countries? A matter of institutional transparency of recipient countries," Energy Policy, Elsevier, vol. 186(C).
    11. Munday, Tim & Brookes, James, 2021. "Mark my words: the transmission of central bank communication to the general public via the print media," Bank of England working papers 944, Bank of England.
    12. Ando, Michihito & Mori, Hiroaki & Yamaguchi, Shintaro, 2022. "Universal early childhood education and adolescent risky behavior," SocArXiv rnkgs, Center for Open Science.
    13. Grafström, Jonas & Poudineh, Rahmat, 2023. "No evidence of counteracting policy effects on European solar power invention and diffusion," Energy Policy, Elsevier, vol. 172(C).
    14. Michael C. Knaus, 2021. "A double machine learning approach to estimate the effects of musical practice on student’s skills," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 282-300, January.
    15. Montiel Olea, José Luis & Nesbit, James, 2021. "(Machine) learning parameter regions," Journal of Econometrics, Elsevier, vol. 222(1), pages 716-744.
    16. Englander,Aaron Gabriel Ratliffe & Karp,Larry & Simon,Leo, 2023. "The Value of Information in a Congested Fishery," Policy Research Working Paper Series 10543, The World Bank.
    17. Masaki,Takaaki & Newhouse,David Locke & Silwal,Ani Rudra & Bedada,Adane & Engstrom,Ryan, 2020. "Small Area Estimation of Non-Monetary Poverty with Geospatial Data," Policy Research Working Paper Series 9383, The World Bank.
    18. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    19. Guo, Xu & Li, Runze & Liu, Jingyuan & Zeng, Mudong, 2023. "Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic," Journal of Econometrics, Elsevier, vol. 235(1), pages 166-179.
    20. Luca Barbaglia & Sergio Consoli & Sebastiano Manzan, 2024. "Forecasting GDP in Europe with textual data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(2), pages 338-355, March.

    More about this item

    Keywords

    research transparency; reporting bias; higher education; young researchers;
    All these keywords.

    JEL classification:

    • A14 - General Economics and Teaching - - General Economics - - - Sociology of Economics
    • A23 - General Economics and Teaching - - Economic Education and Teaching of Economics - - - Graduate
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:i4rdps:225. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://www.i4replication.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.