Impact Evaluation in Practice EducationImpact Evaluation
The second edition of the Impact Evaluation in Practice handbook is a comprehensive and accessible introduction to impact evaluation for policymakers and development practitioners. First published in 2011, it has been used widely across the development and academic communities. The book incorporates real-world examples to present practical guidelines for designing and implementing impact evaluations. Readers will gain an understanding of impact evaluation and the best ways to use impact evaluations to design evidence-based policies and programs. The updated version covers the newest techniques for evaluating programs and includes state-of-the-art implementation advice, as well as an expanded set of examples and case studies that draw on recent development challenges. It also includes new material on research ethics and partnerships to conduct impact evaluation.
The New Statistics (+OSF Learning Page) EducationMeta-AnalysisPsychologyReplicationsSoftware
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).
Research Transparency MOOC EducationInterdisciplinaryVideo Library
Demand is growing for evidence-based policy making, 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, 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.
The full course is available on the FutureLearn platform. The first course run begins July 10, 2017.
All of the course videos are also available on the BITSS website here.
Research Transparency MOOC (French) EducationInterdisciplinaryVideo Library
TRANSLATED CONTENT COMING SOON! Demand is growing for evidence-based policy making, 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, 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.
Manual of Best Practices Education
Manual of Best Practices, written by Garret Christensen (BITSS) with assistance from Courtney Soderberg (Center for Open Science), is a working guide to the latest “best practices” for transparent quantitative social science research. The manual is regularly updated on GitHub. For suggestions or feedback, contact email@example.com.
Open Science Training Initiative Education
Open Science Training Initiative (OSTI), provides a series of lectures in open science, data management, licensing and reproducibility, for use with graduate students and postdoctoral researchers. The lectures can be used individually as one-off information lectures in aspects of open science, or can be integrated into existing course curriculum. Content, slides and advice sheets for the lectures and other training materials are being gradually released on the GitHub repository as the official release versions become available.
Reproducible Research EducationOnline Course
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.
Implementing Reproducible Research EducationTextbook
Implementing Reproducible Research by Victoria Stodden, Friedrich Leisch, and Roger D. Peng covers many of the elements necessary for conducting and distributing reproducible research. The book focuses on the tools, practices, and dissemination platforms for ensuring reproducibility in computational science.