If you are studying social sciences, you might be especially interested in reading about how to preregister your research.
What is a preregistration?
Preregistering a study involves writing a plan of how you intend to carry out your research, and then uploading that plan to a preregistration archive (e.g., OSF) before you begin data collection. Your plan should include as much detail as possible, such as:
- Your hypotheses
- Study design
- Inclusion and exclusion criteria for participants
- Intended sample size (and how you chose that number)
- Study materials
- Dependent variables (and how you measure/calculate them)
- Randomization approach
- How you’ll clean up your dataset so it will be ready for analysis
- How you’ll define outliers in your dataset
- Analysis plan
- Data management plan
This might seem like a lot! But not every aspect of the research project has to be detailed beforehand. Deviations from the initial plan are acceptable as long as you can justify them and transparently communicate them. For example, you may want to use non-parametric (rather than parametric) statistical tests because you might find out after data collection that your data are not normally distributed.
Once your plan is written, you are ready to begin your preregistration! On the OSF, you can create a project record for your upcoming study, invite collaborators and upload your protocol. When you are happy and everything is in place, you can create a preregistration - a timestamped, frozen version of your project. If you are worried about your ideas being stolen, you can embargo the registration for up to four years, but if not, you can make your project public right away.
Fundamentally, preregistration lends credibility to your scientific findings. When you make a prediction and find results to match it, your conclusions have more weight. Preregistration lets you prove that your hypotheses are ‘predictions’ and not ‘postdictions’. It lets you prove that you haven’t p-hacked: Since your analysis plan, sample size and exclusion criteria were specified in advance, you can’t fudge your analysis to achieve statistically significant results. Accordingly, preregistration helps fight publication bias because your research article is more likely to be accepted based on its methodology, and not because you found statistically significant results. On top of that, writing a thorough plan before you begin collecting data helps you to spot flaws in your methodology, and makes writing up the paper easier once the data is all in. Perhaps most importantly, it demonstrates openness, honesty, and transparency - three key characteristics of any good scientist.
Even if you are doing exploratory research, preregistration is still worth it. It indicates to others that you are interested in producing credible scientific results that you (and others) can have confidence in.
In psychology, some have called these steps towards improved science Psychology’s Renaissance. Exciting times, right? :)
And what are Registered Reports?
There is a second approach to preregistration called the registered report (or reviewed preregistration). In registered reports, your research question and methodology are peer-reviewed prior to your data collection. Based on this review, journals agree to accept articles for publication if the authors follow the methodology specified in the preregistration (this is called ‘in principle acceptance’). Registered reports provide you with expert input on your methodology before you begin data collection. It might sting, but your study will be much better off for it! And, let’s be honest: It’s even more upsetting if you put all your effort in conducting a study and writing a paper that then gets rejected because of a small detail in the design that you overlooked, right?
Still have questions? You can find more answers to questions about preregistration here.