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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PhD Course Materials: Transparent, Open, and Reproducible Policy Research Data Management and De-identification
Transparency Training Module for Undergraduate Experimental Economics Dynamic Documents and Coding Practices
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.
Improving Your Statistical Inference Dynamic Documents and Coding Practices
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-identification
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 (Cumming, 2011).
JASP Dynamic Documents and Coding Practices
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 Practices
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.
Reproducible Research Data Management and De-identification
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-identification
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 Finance
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.
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