Transparent and Open Social Science Research

Demand for evidence-based policy is growing, but so is recognition that limited transparency in social science research has contributed to what many have called a crisis of reproducibility and credibility. Join this course to discuss major transparency issues, including fraud, publication bias, and data mining. You’ll also discuss emerging solutions to these problems, explore tools to improve transparency in your own research, and identify flaws in others’ work. This course was developed by the Berkeley Initiative for Transparency in the Social Sciences (BITSS), headquartered at UC Berkeley. By the end of the course, you'll be able to... This course is designed for academics and practitioners who are engaging in social science research, as well as anyone who is interested in better understanding open science and research transparency. To get the most out of this course, you will need: To take part in this course, we recommend you install R, a free statistical software. Download R at https://www.r-project.org/ . You can also use Stata, which can be downloaded for a fee. Many educational institutions offer discounted packages to registered students. Learn more at https://www.stata.com/ . Home to more than 27,000 undergraduates and more than 10,000 graduate students, Berkeley is internationally renowned for excellence and pioneering achievements across all disciplines. The Center for Effective Global Action (CEGA) is a hub for research on global development, with a network of over 70 academic researchers. The Berkeley Initiative for Transparency in the Social Sciences (BITSS) works to strengthen the integrity of social science research and evidence used for policy-making.

• Scientific Ethics and the Reproducibility Crisis
• Publication Bias, Specification Search, and the “File Drawer” Problem
• Pre-registration and Pre-analysis Plans
• The Open Science Framework (OSF)
• Approaches to Replication and Meta-Analysis
• Open Data and Code
• Transparent Data Visualization
• Your Role in the Open Science Movement
• a good understanding of statistics
• undergraduate or preferably graduate experience of econometrics and/or statistical methods
• some experience with statistical software such as Stata or R.