The BITSS Resource Library contains resources for learning, teaching, and practicing research transparency and reproducibility, including curricula, slide decks, books, guidelines, templates, software, and other tools. All resources are categorized by i) topic, ii) type, and iii) discipline. Filter results by applying criteria along these parameters or use the search bar to find what you’re looking for.
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Comparison of multiple hypothesis testing commands in Stata EconomicsStatistics and Data Science
Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Educational Expansion EpidemiologyStatistical LiteracyTransparent Reporting
Created by Catalyst Melissa Sharp, this is an open-source repository for epidemiological research methods and reporting skills for observational studies, structured based on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement. Use it to discover new methods and reporting guidelines and contribute through the GitHub repository (https://github.com/sharpmel/STROBECourse/).
Videos: Research Transparency and Reproducibility Training (RT2) – Washington, D.C. Data Management and De-identificationInterdisciplinaryIssues with transparency and reproducibilityMeta-AnalysesPower analysisPre-Analysis PlansPreprintsRegistriesReplicationsResults-Blind Review & Registered ReportsStatistical LiteracyTransparent ReportingVersion Control
BITSS hosted a Research Transparency and Reproducibility Training (RT2) in Washington DC, September 11-13, 2019. This was the eighth training event of this kind organized by BITSS since 2014.
RT2 provides participants with an overview of tools and best practices for transparent and reproducible social science research. Click here to videos of presentations given during the training. Find slide decks and other useful materials on this OSF project page (https://osf.io/3mxrw/).
PhD Course Materials: Transparent, Open, and Reproducible Policy Research Data Management and De-identificationDynamic Documents and Coding PracticesHealth SciencesInterdisciplinaryIssues with transparency and reproducibilityMeta-AnalysesOpen PublishingPre-Analysis PlansPreprintsPublic PolicyRegistriesReplicationsStatistical LiteracyTransparent ReportingVersion Control
BITSS Catalyst Sean Grant developed and delivered a PhD course on Transparent, Open, and Reproducible Policy Research at the Pardee RAND Graduate School in Policy Analysis. Find all course materials at the project’s OSF page.
Transparency Training Module for Undergraduate Experimental Economics Dynamic Documents and Coding PracticesIssues with transparency and reproducibilityMeta-AnalysesPre-Analysis PlansReplicationsStatistical Literacy
These materials were used in the final weeks of an undergraduate course experimental economics at Wesleyan University taught by Professor Jeffrey Naecker.
These materials were developed as part of a BITSS Catalyst Training Project “Incorporating Reproducibility and Transparency in an Undergraduate Economics Course” led by Catalyst Jeffrey Naecker.
Course Syllabi for Open and Reproducible Methods Anthropology, Archaeology, and EthnographyData RepositoriesData VisualizationDynamic Documents and Coding PracticesEconomics and FinanceEngineering and Computer ScienceHealth SciencesHumanitiesInterdisciplinaryIssues with transparency and reproducibilityLife SciencesLinguisticsMeta-AnalysesMetascience (Methods and Archival Science)Open PublishingOther Social SciencesPolitical SciencePower analysisPre-Analysis PlansPsychologyPublic PolicyRegistriesReplicationsSociologyStatistical LiteracyStatistics and Data ScienceTransparent ReportingVersion Control
A collection of course syllabi from any discipline featuring content to examine or improve open and reproducible research practices. Housed on the OSF.
Improving Your Statistical Inference Dynamic Documents and Coding PracticesIssues with transparency and reproducibilityPower analysisPsychologyStatistical Literacy
This course aims to help you to draw better statistical inferences from empirical research. Students discuss how to correctly interpret p-values, effect sizes, confidence intervals, Bayes Factors, and likelihood ratios, and how these statistics answer different questions you might be interested in. Then, they learn how to design experiments where the false positive rate is controlled, and how to decide upon the sample size for a study, for example in order to achieve high statistical power. Subsequently, students learn how to interpret evidence in the scientific literature given widespread publication bias, for example by learning about p-curve analysis. Finally, the course discusses how to do philosophy of science, theory construction, and cumulative science, including how to perform replication studies, why and how to pre-register an experiment, and how to share results following Open Science principles.
The New Statistics (+OSF Learning Page) Data Management and De-identificationDynamic Documents and Coding PracticesInterdisciplinaryMeta-AnalysesOpen PublishingPower analysisPre-Analysis PlansPsychologyReplicationsStatistical LiteracyStatistics and Data ScienceTransparent ReportingVersion Control
This OSF project helps organize resources for teaching the “New Statistics” — an approach that emphasizes asking quantitative questions, focusing on effect sizes, using confidence intervals to express uncertainty about effect sizes, using modern data visualizations, seeking replication, and using meta-analysis as a matter of course.
JASP Dynamic Documents and Coding PracticesMeta-AnalysesStatistical LiteracyStatistics and Data ScienceVersion Control
JASP is a cross-platform software program with a state-of-the-art graphical user interface. The JASP interface allows you to conduct statistical analyses in seconds, and without having to learn programming or risking a programming mistake. JASP is statistically inclusive as it offers both frequentist and Bayesian analysis methods. Open source and free of charge.
Transparent and Open Social Science Research Dynamic Documents and Coding PracticesIssues with transparency and reproducibilityMeta-AnalysesPre-Analysis PlansRegistriesReplicationsStatistical LiteracyTransparent Reporting
Demand is growing for evidence-based policymaking, but there is also growing recognition in the social science community that limited transparency and openness in research have contributed to widespread problems. With this course created by BITSS, you can explore the causes of limited transparency in social science research, as well as tools to make your own work more open and reproducible.
You can access the course videos for self-paced learning on the BITSS YouTube channel here, (also available with subtitles in French here). You can also enroll for free during curated course runs on the FutureLearn platform.
Data Science Certificate Data VisualizationEngineering and Computer ScienceInterdisciplinaryStatistical LiteracyStatistics and Data Science
Data Science Certificate offered on Coursera, is set of nine classes that cover the concepts and tools needed to analyze data starting with asking the right kinds of questions to making inferences and publishing results.
Reproducible Research Data Management and De-identificationInterdisciplinaryStatistical LiteracyStatistics and Data Science
Reproducible Research taught by Roger D. Peng, Jeff Leek, and Brian Caffoof of Johns Hopkins University is a course on Coursera that teaches methods to organize data analysis so that it is reproducible and accessible to others. In this course students will learn to write a document using R markdown, integrate live R code into a literate statistical program and compile R markdown documents using knitr and related tools.
The Workflow of Data Analysis Using Stata Data Management and De-identificationInterdisciplinaryStatistical LiteracyStatistics and Data Science
Stata by J. Scott Long, explains how to manage aspects of data analysis including cleaning data; creating, renaming, and verifying variables; performing and presenting statistical analyses and producing replicable results.
Reshaping Institutions Economics and FinanceIssues with transparency and reproducibilityPolitical SciencePre-Analysis PlansStatistical Literacy
Reshaping Institutions is a paper by Katherine Casey, Rachel Glennerster, and Edward Miguel that uses a pre-analysis plan to analyze the effects of a community driven development program in Sierra Leone. They discuss the contents and benefits of a PAP in detail, and include a “cherry-picking” table that shows the wide flexibility of analysis that is possible without pre-specification. The PAP itself is included in Appendix A in the supplementary materials, available at the link above.