To increase transparency in research, the International Committee of Medical Journal Editors required, in 2005, prospective registration of clinical trials as a condition to publication. However, many trials remain unregistered or retrospectively registered. We aimed to assess the association between trial prospective registration and treatment effect estimates. Methods This is a meta-epidemiological study based on all Cochrane reviews published between March 2011 and September 2014 with meta-analyses of a binary outcome including three or more randomised controlled trials published after 2006. We extracted trial general characteristics and results from the Cochrane reviews. For each trial, we searched for registration in the report’s full text, contacted the corresponding author if not reported and searched ClinicalTrials.gov and the International Clinical Trials Registry Platform in case of no response. We classified each trial as prospectively registered (i.e. registered before the start date); retrospectively registered, distinguishing trials registered before and after the primary completion date; and not registered. Treatment effect estimates of prospectively registered and other trials were compared by the ratio of odds ratio (ROR) (ROR <1 indicates larger effects in trials not prospectively registered). Results We identified 67 meta-analyses (322 trials). Overall, 225/322 trials (70 %) were registered, 74 (33 %) prospectively and 142 (63 %) retrospectively; 88 were registered before the primary completion date and 54 after. Unregistered or retrospectively registered trials tended to show larger treatment effect estimates than prospectively registered trials (combined ROR = 0.81, 95 % CI 0.65–1.02, based on 32 contributing meta-analyses). Trials unregistered or registered after the primary completion date tended to show larger treatment effect estimates than those registered before this date (combined ROR = 0.84, 95 % CI 0.71–1.01, based on 43 contributing meta-analyses). Conclusions Lack of trial prospective registration may be associated with larger treatment effect estimates.

Discrepancies between pre-specified and reported outcomes are an important source of bias in trials. Despite legislation, guidelines and public commitments on correct reporting from journals, outcome misreporting continues to be prevalent. We aimed to document the extent of misreporting, establish whether it was possible to publish correction letters on all misreported trials as they were published, and monitor responses from editors and trialists to understand why outcome misreporting persists despite public commitments to address it. Methods We identified five high-impact journals endorsing Consolidated Standards of Reporting Trials (CONSORT) (New England Journal of Medicine, The Lancet, Journal of the American Medical Association, British Medical Journal, and Annals of Internal Medicine) and assessed all trials over a six-week period to identify every correctly and incorrectly reported outcome, comparing published reports against published protocols or registry entries, using CONSORT as the gold standard. A correction letter describing all discrepancies was submitted to the journal for all misreported trials, and detailed coding sheets were shared publicly. The proportion of letters published and delay to publication were assessed over 12 months of follow-up. Correspondence received from journals and authors was documented and themes were extracted. Results Sixty-seven trials were assessed in total. Outcome reporting was poor overall and there was wide variation between journals on pre-specified primary outcomes (mean 76% correctly reported, journal range 25–96%), secondary outcomes (mean 55%, range 31–72%), and number of undeclared additional outcomes per trial (mean 5.4, range 2.9–8.3). Fifty-eight trials had discrepancies requiring a correction letter (87%, journal range 67–100%). Twenty-three letters were published (40%) with extensive variation between journals (range 0–100%). Where letters were published, there were delays (median 99 days, range 0–257 days). Twenty-nine studies had a pre-trial protocol publicly available (43%, range 0–86%). Qualitative analysis demonstrated extensive misunderstandings among journal editors about correct outcome reporting and CONSORT. Some journals did not engage positively when provided correspondence that identified misreporting; we identified possible breaches of ethics and publishing guidelines. Conclusions All five journals were listed as endorsing CONSORT, but all exhibited extensive breaches of this guidance, and most rejected correction letters documenting shortcomings. Readers are likely to be misled by this discrepancy. We discuss the advantages of prospective methodology research sharing all data openly and pro-actively in real time as feedback on critiqued studies. This is the first empirical study of major academic journals’ willingness to publish a cohort of comparable and objective correction letters on misreported high-impact studies. Suggested improvements include changes to correspondence processes at journals, alternatives for indexed post-publication peer review, changes to CONSORT’s mechanisms for enforcement, and novel strategies for research on methods and reporting.

Registered Reports: Peer review before results are known to align scientific values and practices.

Registered Reports is a publishing format used by over 250 journals that emphasizes the importance of the research question and the quality of methodology by conducting peer review prior to data collection. High quality protocols are then provisionally accepted for publication if the authors follow through with the registered methodology.

This format is designed to reward best practices in adhering to the hypothetico-deductive model of the scientific method. It eliminates a variety of questionable research practices, including low statistical power, selective reporting of results, and publication bias, while allowing complete flexibility to report serendipitous findings.

This page includes information on Registered Reports including readings on Registered Reports, Participating Journals, Details & Workflow, Resources for Editors, Resources For Funders, FAQs, and Allied Initiatives.

In this deep dive session, Amanda Montoya (UCLA) and Karen Rambo-Hernandez (Texas A&M University) introduce the basics of preregistration and Registered Reports: two methods for creating a permanent record of a research plan prior to conducting data collection. They discuss the conceptual similarities and practical differences between pre-registration and registered reports. They provide practical advice from their own experiences using these practices in research labs and resources available for researchers interested in using these approaches. The session concludes with questions and discussion about adopting these practices and unique considerations for implementing these practices in education research.

In this deep dive session, we introduce the basics of pre-registration: a method for creating a permanent record of a research plan prior to conducting data collection and/or data analysis. We discuss the conceptual similarities and practical differences between pre-registration and registered reports and traditional approaches to educational research. We provide some practical advice from our own experiences using this practice in our own research and resources available for researchers interested in pre-registering their work. Finally, we end with questions and discussion about adopting pre-registration practices and unique considerations for implementing pre-registration in education research.

This cross-sectional study examines discrepancies between registered protocols and subsequent publications for drug and diet trials whose findings were published in prominent clinical journals in the last decade. ClinicalTrials.gov was established in 2000 in response to the Food and Drug Administration Modernization Act of 1997, which called for registration of trials of investigational new drugs for serious diseases. Subsequently, the scope of ClinicalTrials.gov expanded to all interventional studies, including diet trials. Presently, prospective trial registration is required by the National Institutes of Health for grant funding and many clinical journals for publication.1 Registration may reduce risk of bias from selective reporting and post hoc changes in design and analysis.1,2 Although a study3 of trials with ethics approval in Finland in 2007 identified numerous discrepancies between registered protocols and subsequent publications, the consistency of diet trial registration and reporting has not been well explored.

Objectives To identify and appraise empirical studies on publication and related biases published since 1998; to assess methods to deal with publication and related biases; and to examine, in a random sample of published systematic reviews, measures taken to prevent, reduce and detect dissemination bias. Data sources The main literature search, in August 2008, covered the Cochrane Methodology Register Database, MEDLINE, EMBASE, AMED and CINAHL. In May 2009, PubMed, PsycINFO and OpenSIGLE were also searched. Reference lists of retrieved studies were also examined. Review methods In Part I, studies were classified as evidence or method studies and data were extracted according to types of dissemination bias or methods for dealing with it. Evidence from empirical studies was summarised narratively. In Part II, 300 systematic reviews were randomly selected from MEDLINE and the methods used to deal with publication and related biases were assessed. Results Studies with significant or positive results were more likely to be published than those with non-significant or negative results, thereby confirming findings from a previous HTA report. There was convincing evidence that outcome reporting bias exists and has an impact on the pooled summary in systematic reviews. Studies with significant results tended to be published earlier than studies with non-significant results, and empirical evidence suggests that published studies tended to report a greater treatment effect than those from the grey literature. Exclusion of non-English-language studies appeared to result in a high risk of bias in some areas of research such as complementary and alternative medicine. In a few cases, publication and related biases had a potentially detrimental impact on patients or resource use. Publication bias can be prevented before a literature review (e.g. by prospective registration of trials), or detected during a literature review (e.g. by locating unpublished studies, funnel plot and related tests, sensitivity analysis modelling), or its impact can be minimised after a literature review (e.g. by confirmatory large-scale trials, updating the systematic review). The interpretation of funnel plot and related statistical tests, often used to assess publication bias, was often too simplistic and likely misleading. More sophisticated modelling methods have not been widely used. Compared with systematic reviews published in 1996, recent reviews of health-care interventions were more likely to locate and include non-English-language studies and grey literature or unpublished studies, and to test for publication bias. Conclusions Dissemination of research findings is likely to be a biased process, although the actual impact of such bias depends on specific circumstances. The prospective registration of clinical trials and the endorsement of reporting guidelines may reduce research dissemination bias in clinical research. In systematic reviews, measures can be taken to minimise the impact of dissemination bias by systematically searching for and including relevant studies that are difficult to access. Statistical methods can be useful for sensitivity analyses. Further research is needed to develop methods for qualitatively assessing the risk of publication bias in systematic reviews, and to evaluate the effect of prospective registration of studies, open access policy and improved publication guidelines.

This article provides a roadmap to assist graduate students and their advisors to engage in open science practices. We suggest eight open science practices that novice graduate students could begin adopting today. The topics we cover include journal clubs, project workflow, preprints, reproducible code, data sharing, transparent writing, preregistration, and registered reports. To address concerns about not knowing how to engage in open science practices, we provide a difficulty rating of each behavior (easy, medium, difficult), present them in order of suggested adoption, and follow the format of what, why, how, and worries. We give graduate students ideas on how to approach conversations with their advisors/collaborators, ideas on how to integrate open science practices within the graduate school framework, and specific resources on how to engage with each behavior. We emphasize that engaging in open science behaviors need not be an all or nothing approach, but rather graduate students can engage with any number of the behaviors outlined.

Registered Reports (RRs) is a publishing model in which initial peer review is conducted prior to knowing the outcomes of the research. In-principle acceptance of papers at this review stage combats publication bias, and provides a clear distinction between confirmatory and exploratory research. Some editors raise a practical concern about adopting RRs. By reducing publication bias, RRs may produce more negative or mixed results and, if such results are not valued by the research community, receive less citations as a consequence. If so, by adopting RRs, a journal’s impact factor may decline. Despite known flaws with impact factor, it is still used as a heuristic for judging journal prestige and quality. Whatever the merits of considering impact factor as a decision-rule for adopting RRs, it is worthwhile to know whether RRs are cited less than other articles. We will conduct a naturalistic comparison of citation and altmetric impact between published RRs and comparable empirical articles from the same journals.

This webinar provides an overview of TOP Factor: its rationale, how it is being used, and how each of the TOP standards relate to individual scores. We also cover how to get involved with TOP Factor by inviting interested community members to suggest journals be added to the database and/or evaluate journal policies for submission.

Proponents of preregistration argue that, among other benefits, it improves the diagnosticity of statistical tests. In the strong version of this argument, preregistration does this by solving statistical problems, such as family-wise error rates. In the weak version, it nudges people to think more deeply about their theories, methods, and analyses. We argue against both: the diagnosticity of statistical tests depend entirely on how well statistical models map onto underlying theories, and so improving statistical techniques does little to improve theories when the mapping is weak. There is also little reason to expect that preregistration will spontaneously help researchers to develop better theories (and, hence, better methods and analyses).

Registered reports present a substantial departure from traditional publishing models with the goal of enhancing the transparency and credibility of the scientific literature. We map the evolving universe of registered reports to assess their growth, implementation and shortcomings at journals across scientific disciplines.

This is an archived version of a blog post on preregistration. The first half of the post argues that there is not a strong justification for preregistration as a tool to solve problems with statistical inference (p-hacking); the second half argues that preregistration has a stronger justification as one tool (among many) that can aid scientists in documenting our projects.

This video shows interested researchers how to get started on their own preregistration as part of the Preregistration Challenge. Learn how to create a new draft, find example preregistrations from different fields, respond to comments from the preregistration review team, and turn your final draft into a formal preregistration. For more information, check out https://www.cos.io/initiatives/prereg-more-information.

What is Preregistration? When you preregister your research, you're simply specifying your research plan in advance of your study and submitting it to a registry. Preregistration separates hypothesis-generating (exploratory) from hypothesis-testing (confirmatory) research. Both are important. But the same data cannot be used to generate and test a hypothesis, which can happen unintentionally and reduce the credibility of your results. Addressing this problem through planning improves the quality and transparency of your research. This helps you clearly report your study and helps others who may wish to build on it.

In recent years, open science practices have become increasingly popular in psychology and related sciences. These practices aim to increase rigour and transparency in science as a potential response to the challenges posed by the replication crisis. Many of these reforms -- including the highly influential preregistration -- have been designed for experimental work that tests simple hypotheses with standard statistical analyses, such as assessing whether an experimental manipulation has an effect on a variable of interest. However, psychology is a diverse field of research, and the somewhat narrow focus of the prevalent discussions surrounding and templates for preregistration has led to debates on how appropriate these reforms are for areas of research with more diverse hypotheses and more complex methods of analysis, such as cognitive modelling research within mathematical psychology. Our article attempts to bridge the gap between open science and mathematical psychology, focusing on the type of cognitive modelling that Crüwell, Stefan, & Evans (2019) labelled model application, where researchers apply a cognitive model as a measurement tool to test hypotheses about parameters of the cognitive model. Specifically, we (1) discuss several potential researcher degrees of freedom within model application, (2) provide the first preregistration template for model application, and (3) provide an example of a preregistered model application using our preregistration template. More broadly, we hope that our discussions and proposals constructively advance the debate surrounding preregistration in cognitive modelling, and provide a guide for how preregistration templates may be developed in other diverse or complex research contexts.

Preregistration, the act of specifying a research plan in advance, is becoming a central step in the way science is conducted. Preregistration for infant researchers might be different than in other fields, due to the specific challenges having to do with testing infants. Infants are a hard-to-reach population, usually yielding small sample sizes, they have a low attention span which usually can limit the number of trials, and they can be excluded based on hard to predict complications (e.g., parental interference, fussiness). In addition, as effects themselves potentially change with age and population, it is hard to calculate an a priori effect size. At the same time, these very factors make preregistration in infant studies a valuable tool. A priori examination of the planned study, including the hypotheses, sample size, and resulting statistical power, increase the credibility of single studies and thus add value to the field. It might arguably also improve explicit decision-making to create better studies. We present an in-depth discussion of the issues uniquely relevant to infant researchers, and ways to contend with them in preregistration and study planning. We provide recommendations to researchers interested in following current best practices.

In this webinar, Tamarinde Haven provides an overview of the process of preregistration in qualitative research. We review the process of preregistration, how we partnered with a community of qualitative researchers to develop a template for qualitative research through a Delphi study, and a guide to the fields that were included in the final form.

Discussions of how to improve research quality are predominant in a number of fields, including education. But how prevalent are the use of problematic practices and the improved practices meant to counter them? This baseline information will be a critical data source as education researchers seek to improve our research practices. In this preregistered study, we replicated and extended previous studies from other fields by asking education researchers about 10 questionable research practices and 5 open research practices. We asked them to estimate the prevalence of the practices in the field, self-report their own use of such practices, and estimate the appropriateness of these behaviors in education research. We made predictions under four umbrella categories: comparison to psychology, geographic location, career stage, and quantitative orientation. Broadly, our results suggest that both questionable and open research practices are part of the typical research practices of many educational researchers. Preregistration, code, and data can be found at https://osf.io/83mwk/.