Preregistration in economic research

A guide to greater transparency

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In science, transparency is essential to ensure the credibility and reproducibility of research results. Pre-registration, i.e. the documentation of key aspects of a study prior to data collection, is also becoming increasingly important in economic research.

This short guide explains the benefits of preregistration, describes the key steps and offers practical tips for implementation.

Why is pre-registration important?

  • Avoidance of HARKing (Hypothesising After the Results are Known): The subsequent adjustment of hypotheses to the results found is prevented.
  • Reduction of p-hacking: Pre-defined analysis methods minimise selective data analysis.
  • Increasing replicability: Other researchers can reproduce the methods used and replicate studies.
  • Strengthening scientific integrity: Transparency increases trust in research results.

Steps to successful preregistration

  1. Definition of the research question and hypotheses
    • Formulation of clear, testable hypotheses.
    • Differentiation between exploratory and confirmatory questions.
  2. Detailed documentation of the study design
    • Description of the methodology (experiment, survey, secondary data analysis, etc.).
    • Specification of sample size and recruitment method.
  3. Definition of the variables
    • Specification of independent and dependent variables.
    • Description of possible moderators or mediators.
  4. Data collection procedure
    • Collection and storage of data.
    • Definition of exclusion criteria and handling of missing values.
  5. Determine analysis plan
    • Selection of statistical tests.
    • Procedure for correcting multiple comparisons.
    • Procedure for data cleansing.
  6. Publication of the preregistration
    • Utilisation of established platforms such as Open Science Framework (OSF) or AsPredicted.
    • Possibility of an embargo agreement, if necessary.

Challenges and solutions

  • Flexibility vs. rigour: In exploratory studies, not all aspects can be defined in advance. Flexible preregistration can help here.
  • Effort vs. benefit: Initial preregistration takes time, but saves resources in the long term through structured planning.
  • Acceptance in the specialist community: The increasing demand for preregistration by specialist journals and institutions increases the relevance of this practice.

Here are some reading and link tips on preregistration in economic research:

General introductions to preregistration

  1. Centre for Open Science (COS) – https://www.cos.io/: Information on Open Science and preregistration platforms such as the Open Science Framework (OSF).
  2. APA (American Psychological Association) on preregistration – https://www.apa.org/science/leadership/bsa/preregistration: Overview of the benefits of preregistration, especially for empirical research.
  3. Preregistration in Business Research – https://osf.io/z8k3c/ A guide to the application of preregistration in business research.

Platforms for pre-registration

  1. Open Science Framework (OSF) – https://osf.io/: One of the most important platforms for the preregistration of studies. Here you will find templates and step-by-step instructions.
  2. AsPredicted – https://aspredicted.org/: A user-friendly platform for quick and easy preregistrations.
  3. Registered Reports – https://www.cos.io/initiatives/registered-reports: Information on the peer review process prior to data collection.

Reading tips:

Chambers, C. D., & Tzavella, L. (2022). The past, present and future of Registered Reports. Nature human behaviour, 6(1), 29-42. https://doi.org/10.1038/s41562-021-01193-7

Hardwicke, T. E., & Ioannidis, J. P. (2018). Mapping the universe of registered reports. Nature Human Behaviour, 2(11), 793-796. https://doi.org/10.1038/s41562-018-0444-y

Hardwicke, T. E., & Wagenmakers, E. J. (2023). Reducing bias, increasing transparency and calibrating confidence with preregistration. Nature Human Behaviour, 7(1), 15-26. https://doi.org/10.1038/s41562-022-01497-2

Henderson, E. L., & Chambers, C. D. (2022). Ten simple rules for writing a Registered Report. PLoS computational biology, 18(10), e1010571. https://doi.org/10.1371/journal.pcbi.1010571

Mellor, D. T., & Nosek, B. A. (2018). Easy preregistration will benefit any research. Nature Human Behaviour, 2(2), 98-98. https://doi.org/10.1038/s41562-018-0294-7

Nosek, B. A., & Lakens, D. (2016). Registered Reports: A method to increase the credibility of published reports. https://doi.org/10.1027/1864-9335/a000192

Nosek, B. A., Beck, E. D., Campbell, L., Flake, J. K., Hardwicke, T. E., Mellor, D. T., … & Vazire, S. (2019). Preregistration is hard, and worthwhile. Trends in cognitive sciences, 23(10), 815-818. https://doi.org/10.1016/j.tics.2019.07.009

Nosek, B. A., Ebersole, C. R., DeHaven, A. C., & Mellor, D. T. (2018). The preregistration revolution. Proceedings of the National Academy of Sciences, 115(11), 2600-2606. https://doi.org/10.1073/pnas.1708274114

*The practical guide was written on 17 March 2025.
This text was translated on 24 March 2025 using DeeplPro.



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