Explore tools to address transparency issues in 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.
What topics will you cover?
- 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
Who is the course for?
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:
- 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.
Do you know someone who’d love this course? Tell them about it...
You can use the hashtag #FLOpenScience to talk about this course on social media.