The State of Study Preregistration - How Science Benefits From It and How to Apply It

Preregistration 1 The State of Study Preregistration - How Science Benefits From It and How to Apply It Hilmar Brohmer Department of Psychology, Graz Open Science Initiative, Arqus Open Science Ambassador, University of Graz, Psychology of Digitalisation, University of Bern, [email protected], https://orcid.org/0000-0001-7763-4229 Gabriela Hofer Department of Psychology, Graz Open Science Initiative, Arqus Open Science Ambassador, University of Graz, [email protected], https://orcid.org/0000-0003-4407-1487 Sarah von Götz Department of Psychology, Graz Open Science Initiative, University of Graz, Department of Biology, University of Konstanz, International Max Planck Research School for Quantitative Behaviour, Ecology and Evolution, [email protected], https://orcid.org/0009-0006-9430-8867 Abstract Study preregistration – a documented plan on how to conduct a future study – is an important, yet underappreciated Open Science practice. In this article, we describe the emergence of preregistration in medicine and psychology and how science as a whole may benefit from it. We also show that the uptake of preregistration is thus far very slow among researchers in the social and behavioral sciences. There may be several reservations and misconceptions for this, which we address in this article. We further provide a short and accessible tutorial on preregistration that may enable and motivate researchers to start using preregistration in their workflow. Keywords: preregistration, registered report, Open Science, research ethics This manuscript (2nd version) is currently under peer review. Cite at your own risk Preregistration 2 Introduction The concept of a study preregistration is simple and straight forward: It is a plan, usually committed in writing and documented in a publicly accessible digital form, describing why and how a study will be conducted and analyzed in the future. It is done before data is collected or analyzed and contains information on research questions and hypotheses, research design and planned data analysis (Parsons et al. 2022). With a preregistration a researcher thus provides a high level of transparency for the study in preparation. Although this seems like a trivial task – and even one that laypersons may believe to be common in science – preregistrations in a systematic and publicly available form have not been around for long. In this article, we want to briefly review how and under which circumstances preregistration was introduced to research on human subjects, how several preregistration forms developed based on the original idea, and how effective preregistrations may tackle widespread problems of publication bias. Next, we will present how preregistration is adopted by researchers, particularly PhD students, in psychology – a field where preregistration has recently become more common – over time. Then, we will provide a tutorial on how to implement preregistration in one’s research using the popular Open Science Framework repository (OSF). Finally, we will address some common critiques of preregistration. This article aims to give a brief overview on preregistration and to remove barriers researchers might have towards implementing them by providing a straightforward tutorial on how to get started. Given that preregistration is applicable in their research, this may motivate them to integrate preregistration in their future studies. Emergence of Preregistration Although most researchers spend a lot of time working out the details on how to set up their scientific studies, the idea of a preregistered plan that lays out the specifics of a future study and that is publicly accessible is not older than a quarter century. In medical research, preregistration was first implemented around the year 2000 in response to an emerging credibility crisis: many clinical studies probing new medications (usually conducted in randomized controlled trials) seemed to show positive effects (i.e., patients became healthy quickly following a new medication compared to placebo or an older medication). However, followup studies of the same medical treatments sometimes did not show the same health benefits – despite seemingly carefully conducted research. This called into question the results of the initial studies that largely reported positive findings and indicated a publication bias towards positive findings (Simes 1986, Easterbrook et al. 1991). As a response, the US National Institute for Health launched the first registry for studies – ClinicalTrials.gov – where researchers had to indicate the aims and set-up of their planned studies. It was received positively and just five years later the International Committee of Medical Journal Editors of major scientific journals made registration of studies a mandatory requirement for later publication (McCray & Ide 2000; CinicalTrials.gov 2025). The effectiveness of this early form of a preregistration became evident in 2010s: compared to Preregistration 3 before, new studies in medical research rarely reported exaggerated health benefits. Instead, findings became more nuanced and oftentimes even showed null effects (Kaplan & Irvin 2015). The public registration of the research plan resulted in fewer researchers trying to circumvent their initial idea to publish “positive” results that turned out to be fluke findings later. The social and behavioral sciences had their own simmering credibility and replication crisis, which flared up in the early 2010s. 1 First, concerns arose that some findings in the social and cognitive-psychological domain seemed to be incompatible with physical understandings of the world or just absurd to be taken for granted (Lakens 2025). Most prominently, one paper in a prestigious psychology journal demonstrated that some people could “feel the future” by seemingly correctly guessing the position of erotic pictures on an empty computer screen. Not only did this finding cause some media controversy (French 2012), but also proved to be unreliable in a followup study (Ritchie et al. 2012). More often than not did these findings show positive – statistically significant – effects that appeared to confirm the researchers’ hypotheses, implying that they are almost always right with their predictions. However, a group of methodologists convincingly argued that many findings could simply be fluke results stemming from undisclosed analyses steps. They are known as “researcher degrees of freedom”, where researchers flexibly conduct several analyses, but only report the one’s that culminate in a positive finding. When carefully conducting replication studies, these results would likely vanish (Simmons et al. 2011). Indeed, a large-scale replication project by the Open Science Collaboration (2015) demonstrated that of 100 published psychological studies from major journals, more than 60 % could not show the same result when the study was independently replicated – a major upset for researchers in the field. Simmons and colleagues (2011) argued that the issues of replicability and transparency need to be addressed far beyond researchers only sharing their data and methods, which constitute necessary, but not sufficient Open Science (OS) practices for more transparency. Instead, researchers would need to commit to and uphold important parts of their research idea beforehand to decrease the chance that they would later deviate too much from their initial plan through hindsight and confirmation bias in their analysis decisions. In other words, they needed to explicitly commit to which part of their research is confirmatory (hypotheses testing) and which part is exploratory (hypotheses generating; see also Nosek et al. 2018). Hence, the idea of preregistration started to spread in psychology. 1 Almost at the same time, but independently of this crisis, researchers in the fields of economics and political science started to use pre-analysis plans. In a similar spirit as preregistrations, pre-analysis plans also aim at specifying analysis steps beforehand to avoid false-positive results particularly in intervention studies (see McKenzie 2012, Humphreys et al. 2013, Olken 2015). Preregistration 4 Key Points of a Preregistration Simmons and colleagues (Data Colada 2015) as well as Nosek and his colleagues from the Center for Open Science (n. d.) became vanguards of preregistration by providing early online platforms – the Wharton Credibility Lab (https://aspredicted.org/) and the Open Science Framework (OSF, https://osf.io/) – and popularized the concept in psychology and other social and behavioral sciences. If one looks at their popular preregistration forms on these repositories (i. e., the popular “AsPredicted” form or the “OSF Preregistration” form), principles of what a preregistration should entail to be informative become evident (see also McPhetres 2020; Simmons et al. 2021). These principles can be roughly subsumed under the following key points of a preregistration: a) Research question and hypotheses: What question do the researchers want to answer and what specific prediction, if any, is being made for the confirmatory part? b) Research design and variables: How is the study conceptualized and set up and which variables are measured or experimentally manipulated? c) Sample size justification: What is the population and the sample size drawn from it? Is this sample size sufficient to find a hypothesized effect given that it exists? Is there a known/estimated effect size, which can be basis for a statistical power analysis? d) Analysis plan and preparation steps: How is the data being prepared to become ready for the analysis? Do these steps reduce the sample size through data exclusion? What are the specific assumptions and analysis strategies? These points should be fixed beforehand to avoid hidden flexibility and questionable research practices when analyzing the data later, which quickly lead to false-positive results (Simmons et al., 2011). Note that these points resemble core specifications that have also been discussed in other publications (e. g., Wicherts et al. 2016, Hahn et al. 2025) Development of Preregistration and Registered Reports While the initial preregistration forms focused on experimental studies in psychology (see Bowman et al., 2016), several groups of Open Science proponents from different disciplines have since updated and refined the idea of study preregistration, making it suitable for several kinds of research, including qualitative studies (Haven & van Grootel 2019), replication studies (Brandt et al. 2014), systematic reviews (van den Akker et al. 2023), or secondary analyses of existing data (van den Akker et al. 2021). Moreover, in a collaborative effort of task forces of several psychological associations, a more comprehensive preregistration form was developed for the whole field of psychology (the “PRP-Quant” form, see Bosnjak et al., 2022). 2 2 In this comprehensive form, researchers can provide highly detailed information on study specifics and data handling. Thus, it resembles an extended data management plan. The most recent version of the template (version 3) is available as a web application (https://prereg-psych.org/create/). Preregistration 5 These efforts reflect that researchers increasingly recognized the non-replicability of findings not only as an issue of social and cognitive psychology (in which the replication crisis came to light), but as a fundamental issue to many scientific disciplines that apply some kind of hypothesis testing. More precisely, the issue concerns foremost disciplines that deduce ideas to be tested (ideally formulated in hypotheses) in future studies from theory and/or previous knowledge. The studies serve as a test to verify or falsify these ideas (Fidler et al. 2018, Scheel et al. 2020). 3 Excessive positive results are present in research disciplines that apply hypothesis testing (see Fanelli 2010), and many of these deal with own replication issues (e. g., experimental economics, Camerer et al. 2016; social sciences, Camerer et al. 2018; and pre-clinical cancer research, Errington et al. 2021). Several journals even brought the preregistration concept one step further: While preregistering a study before data collection provides a level of transparency, there is no formal review process in place. The journal format of Registered Reports (Chambers 2013; Chambers and Tzavella 2022) addresses this problem, which includes an a priori peer-review. This way, researchers write a journal-style preregistration and submit it to a journal before the begin of the data collection or data analysis. The peer-review then critiques the theory and methods (rather than the results) and can provide constructive feedback. This effectively allows the researchers to collect feedback at a timepoint where it can still be implemented in their study. A second round of peerreview for the final article (i. e., after data collection, analysis and writing) merely verifies that the researchers followed their initial preregistered plan. Registered Reports come with the premise that the research is evaluated based on its actual quality and contribution to the field, rather than on whether its results turn out to be positive. Thus, Registered Reports provide the means to prevent publication bias towards positive results (Chambers 2017). On the online platform “Peer-Community In Registered Reports” (https://rr.peercommunityin.org), the research community even aims at standardizing the peer-review process for Registered Reports, unifying it across all participating psychology journals. Similar to medical research, preregistration in the social and behavioral sciences, proved to be effective: effect sizes in published studies that utilized preregistration are often much smaller than in regular studies (Schäfer and Schwarz 2019), implying that that the literature is less swamped with fluke findings. This is particularly true for Registered Reports, which also seem to be of higher quality in terms of their theorizing, methods, and implications (Scheel et al. 2021; Soderberg et al. 2021). Although registries like the OSF provide researchers with a highly streamlined and low-effort way of preregistering their studies, the question remains, how many researchers actually chose to apply it in their research. 3 As a reviewer noted, we want to mention that this so called Hypothetico-Deductive Method does not apply to all scientific disciplines alike. For instance, in disciplines that uncover information about the past, such as history or archeology, hypothesis testing in a form similar to psychology is simply not applicable. Hence, preregistration in the way presented in this article cannot be applied to this research either. Nonetheless, other forms of a priori planning (e.g., for the later interpretation of findings) may also help researchers avoid traps of confirmation and hindsight bias. Preregistration 6 Uptake of Preregistration Contrary to randomized controlled studies in medicine, studies in the social and behavioral sciences do not include mandatory preregistration before data collection. However, OS communities and initiatives have emerged across universities to promote the voluntary uptake of OS practices – including preregistration. These initiatives often employ various approaches such as the organization of journal clubs and larger events, or giving workshops and courses. They seek to show researchers why OS practices matter for improving research in relevant hypothesis-testing disciplines, and to help them develop the skills needed to put these practices into action successfully (e. g., Orben 2019; Armeni et al. 2021; Brohmer et al. 2025 in this issue). Nonetheless, in the end, the success of such endeavors may be measured in how popular OS practices have become in research output, that is, in scientific publications. For preregistrations, an increase throughout the last decade may indicate that researchers started recognizing these practices being important to improve transparency in general. Some recent data from Hardwicke and colleagues (2024) suggests that there is indeed an uptake in applying preregistration: Whereas between 2016 to 2018, about 7 % of research articles in psychology reported a functional link to a preregistration form, this number had increased to 20 % in 2022. In major psychology journals, this rate could even be as high as 40 % (Pfadt et al. 2025; although the actual rate of completeness maybe lower, see Hahn et al., 2024). Brohmer and Hoffmann (2025) did something similar, but focused on PhD students, because they are assumed to just having acquired their primary research skills, which they will likely use until the end of their career. In a sample of studies (N = 379) from 91 dissertations from psychology departments at two universities, they found that 20 % (n = 80) contained a preregistration, corroborating the results by Hardwicke. From all studies that used preregistration, most were based on the beginner-friendly “AsPredicted” form (16 %, n = 60), few used the more comprehensive “OSF Preregistration” form (3.7 %, n = 14), and n = 3 (0.8 %) conducted a Registered Report (see Figure 1). Hence, early-career researchers started using preregistration, but there is certainly room for improvement. If one looks at the prevalence of Registered Reports in journals, they do not appear to be very popular yet: we compared the proportion of Registered Reports in two journals from psychology: Collabra: Psychology emerged as a small journal dedicated to transparency and reproducibility throughout the last couple of years, whereas Nature Human Behavior quickly became a journal focusing on more broadly interesting and high-impact results. Both journals introduced Registered Reports in 2017. Collabra’s rate of Registered Reports went up from zero to 16 % in 2024 (14 out of 88 published research articles). For Nature Human Behavior the rate did not increase much: only about 1 % of published studies in 2024 were in a Registered Report format (two out of 153 published research articles). Preregistration 7 Figure 1. Use of preregistration in studies of psychology dissertations 2018-22. Assuming that the types of submitted studies are otherwise similar, this may indicate two things: I) The few researchers, who aim at conducting a Registered Report may rather “play it safe” by submitting it to a journal (or the “Peer Community” platform, see above) that values transparency and reproducibility explicitly. A potential reason might be that they struggle with evaluating the importance and impact of their research before knowing their results. 4 II) For a similar reason, the editors of high-impact journals may reject Registered Reports more often as they fear potential null findings (which are more common in Registered Reports), which they think are less citable. Unfortunately, there is little data on the adoption of preregistration in research fields beyond psychology. A recent survey (Ferguson et al., 2023) among social scientists from sociology, economics and political science suggests that 10 to 25 % of researchers have used a preregistration before (although it was not specified for how many studies exactly). But as the awareness of the importance of OS practices only recently gained traction in fields like consumer research (Simmons et al. 2021), or education research (Krammer & Svecnik 2021, Reich 2021), a slow and delayed uptake of preregistration is rather plausible. With our following tutorial, we hope to stimulate a further uptake of preregistration across fields. Four Easy Steps to Preregistration – A Brief Tutorial Certainly, science would benefit from a quicker uptake of OS practices and particularly from the uptake of study preregistration. However, for individual researchers it can be difficult to know where to start as this methodological skill is rarely taught in university courses. Moreover, 4 For researchers, who value OS a lot, another reason might be that the smaller, transparency-oriented journal is more in line with their values and that the open-access publication charges in that journal are not as high. Preregistration 8 researchers at later career stages might have received their training before the emergence of preregistration. In this section, we will provide a four-step tutorial for preregistration in the hopes that this will make it easy for interested researchers to get started. We have chosen the OSF, as this is a very accessible registry and researchers might already be familiar with it for other OS-related purposes (e. g., as a repository for data sharing). Moreover, it is compliant with the General Data Protection Regulation of the European Union and also allows working in private projects. The only prerequisite is that interested readers create an account on the OSF (https://osf.io/). It is also important that readers already have a relatively concrete study idea in mind, including the research questions, hypotheses and methodology. The OSF provides many available preregistration templates, which differ in level of detail and type of research they can be applied to (see https://help.osf.io/article/229-select-a-registration-template). As this is a beginner’s tutorial, we will focus on the “AsPredicted” format which is relatively short and can be applied to most experimental studies and beyond. The following four steps are illustrated in Figure 2. 5 Step 1: Create a New Project “Projects” on the OSF are superordinate folders, which can be created for single studies or whole research projects (e.g., a dissertation or a post-doc grant project). Upon clicking on the button “Create New Project” (Figure 2a), one should select a short, but clearly descriptive title (e.g., “Study 1: Cooperative Behavior”). When selecting a server, it is advisable to keep local data protection regulations in mind (e.g., European researchers may select the Frankfurt server). Step 2: Navigate to the Preregistration Form The landing project page is now created and should look like as in Figure 2b. Note that this project is “private”, which means it is only visible to the author themselves and will remain private until the authors chose to share the project (e.g., during publication of the research paper). Following the tabs on the left, one could later add information on the project by clicking on the Wiki tab, add supplemental documents, methods, and data files via the Files tab, or add co-authors and collaborators to the project via the Contributors tab. For adding a preregistration, one should simply click on the Registrations tab and then on “add a registration”. Step 3: Choose a Preregistration Form On the next page, select that you have content for registration and an existing project (i.e., the one you just created). As we mentioned above, there are many preregistration forms available, but 5 A video tutorial in German language is provided on the Unitube portal of the University of Graz: https://unitube.unigraz.at/portal/aufzeichnungen.html?id=e1afd6a9-c7cf-4faa-a77d-4953c501c67f Preregistration 9 Figure 2. Four Steps to a Preregistration via the Open Science Framework Note: red ellipses and arrows with annotation are added for clarification; form is available on https://osf.io/; screenshots were taken in October 2025 we select the “AsPredicted” form from the drop-down list (Figure 2c). 6 Researchers with more preregistration experience may choose the “OSF Preregistration” form, which is more informative in regard to some details (see Bakker et al. 2020), or another form that closely aligns with their study design (e. g., Replication Recipe). Experienced researchers from psychology and related disciplines are further advised to try out the accessible app of the comprehensive “PRP-Quant” form (https://prereg-psych.org/create/). After selection, click on “create draft”. Step 4: Fill Out the Form and Submit It This final step requires some time, depending on the study’s complexity and how well thoughtthrough all study aspects already are. 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