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Components of a preregistration

Recap: In the introduction we covered the context and rationale of preregistration. Here we will learn what the practical steps involved are and what information is required to complete a study preregistration.

Components of a preregistration

There are a number of essential elements within a registration document (with a subset of recommended things to also include Stefan et al. (2023)):

  1. Hypotheses
  2. Design
  3. Planned sample
  4. Exclusion criteria
  5. Analysis plan
Note

These mirror some of the stages in the research life cycle we have seen earlier. There are also additional recommendations that can be added to registrations.

We will go through each element in turn. Note that the five preregistration components detailed below are more focused for hypothesis-testing research. For hypothesis-generating or exploratory research approaches there is guidance on what to include (e.g. Dirnagl (2020)).

Part 1: Hypotheses

Essential:

  • State your research questions clearly

  • Describe the (numbered) hypotheses in the context of your variables

  • For interaction effects, describe the expected shape of the interactions.

  • If you are manipulating a variable, make predictions for successful check variables or explain why no manipulation check is included.

Recommendations:

  • Adding a table/figure to describe complex interaction

  • For studies testing hypotheses, link stated hypotheses to theoretical frameworks

Part 2: Design

Essential:

List of variables based on your hypotheses:

  • Independent variables (including levels, within or between participants, relationship between them (e.g. nested))

  • dependent variables or variables in a correlational design

  • third variables (e.g. covariates, moderators, control variables)

Part 3: Planned sample

Essential:

  • If applicable, state any pre-selection rules (e.g. age limits)

  • Indicate where, from whom and how the data will be collected

  • Justify planned sample size (e.g. power analysis)

  • Describe data collection termination rule

Part 4: Exclusion criteria

Essential:

Describe anticipated specific data exclusion criteria such as:

  • missing, erroneous, or overly consistent responses

  • failing check-tests or suspicion probes

  • demographic exclusions

  • data-based outlier criteria (e.g expected variable ranges)

  • method-based outlier criteria (e.g. too short or long response times)

Recommendations:

  • Set fail-safe levels of exclusion at which the whole study needs to be stopped, altered, and restarted.

Part 5: Analysis plan

Essential:

Describe the analyses that will test each main prediction from the hypotheses section. For each one, include:

  • relevant variables and how they are calculated

  • statistical technique

  • each variable’s role in the technique (e.g., independent variable, dependent variable and covariates)

  • rationale for each covariate used, if any

  • if using techniques other than null hypothesis testing (e.g. Bayesian statistics), describe your criteria calculation of prior values or distributions

Recommendations:

  • Specify contingencies and assumptions (e.g. method of correcting for multiple testing, missing data handling, data transformations (e.g. Shapiro-Wilk’s test for normal/Gaussian distribution), assumptions and assumption checks for analyses and alternative plans for data analysis if assumptions are not met).

Additional considerations

Depending on your study type, there may be additional things to keep in mind as you plan your study. You’ll need to determine if you need to: plan for multi-stage contingent experiments, anonymize your registration (e.g. place under embargo) or apply for ethical approval.

Preregistration vs registered reports

So far content has focused on preregistration, but there is another related Open Science practice known as a registered report. Registered reports are a publishing format where a study undergoes two stages of peer review: stage 1: introduction and methods in advance of data collection/analysis and stage 2: which includes the full study findings including results/discussion Parsons et al. (2022). Registered reports can help safeguard against finding potential study design issues after data collection.

Caution

You may come across registered reports being described in the literature as “two-stage review with in-principle acceptance” (Mayo-Wilson et al. 2025). Some meta-science researchers are working calling for the Open Science community to harmonize terms to reduce ambiguity.

Preregistration templates

Now that you have an overview of what things are needed in a preregistration, we can explore the table below of some preregistration templates.

Adapted from Pennington (n.d.) and Stewart et al. (n.d.)

Description

  • Standardized template
  • Multiple templates for many study types
  • Study protocols for systematic reviews
  • Guidelines
  • Online peer-reviewed protocol journal
  • Guidelines

Discipline/study type

  • Quantitative/experimental studies
  • Wide-ranging
  • Health related
  • Quantitative/experimental social psychology studies
  • Biological sciences
  • Research syntheses
NoteReflection point

Thinking about your research area and any institutional policies or community standards, which template(s) do you think fit closer to your type of research study?

Quick Quiz!

Test yourself on what you have learned in this section on the components of preregistration.

  1. True or false? When describing your design plan you only need to describe your independent and dependent variables.

False (any covariates should be listed too!)

Refer back to Section 1.2 for more information

  1. True or false? Preregistration involves stating how you will handle missing data

True

Refer back to Section 1.4 for more information

  1. Which repository has a more generalist preregistration template: PROSPERO, Open Science Foundation (OSF) or Bio-protocol?

Open Science Foundation (OSF)

Refer back to Section 1.8 table for more information

  1. Registered reports have two peer review stages that occur:
  1. After developing an idea and after writing up study findings
  2. After study plan and after collecting and analyzing data
  3. After study plan and after writing up study findings
  1. After study plan and after writing up study findings

Refer back to Section 1.7 for more information

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References

Dirnagl, Ulrich. 2020. “Preregistration of Exploratory Research: Learning from the Golden Age of Discovery.” PLOS Biology 18 (3): e3000690. https://doi.org/10.1371/journal.pbio.3000690.
Mayo-Wilson, Evan, Sean Grant, Katherine S. Corker, and David Moher. 2025. “Consistent and Precise Description of Research Outputs Could Improve Implementation of Open Science.” Advances in Methods and Practices in Psychological Science 8 (4): 25152459251375445. https://doi.org/10.1177/25152459251375445.
Parsons, Sam, Flávio Azevedo, Mahmoud M. Elsherif, et al. 2022. “A Community-Sourced Glossary of Open Scholarship Terms.” Nature Human Behaviour 6 (3): 312–18. https://doi.org/10.1038/s41562-021-01269-4.
Pennington, Charlotte Rebecca. n.d. Study Preregistration: A How-to Guide. https://doi.org/10.31234/osf.io/6g5zv.
Stefan, Angelika, M., Felix Schönbrodt, and Lena Schiestel. 2023. Preregistration, Open Science Workshop. https://osf.io/5krf3/files/jpbgy.
Stewart, Suzanne, Eike Mark Rinke, Ronan McGarrigle, et al. n.d. Pre-Registration and Registered Reports: A Primer from UKRN. https://doi.org/10.31219/osf.io/8v2n7.