Demand is growing for evidence-based policy making, but there is growing recognition in the social science community that limited transparency and openness in research have contributed to widespread problems.
Explore transparency issues in social science research – and how to solve them
In this free online course, we will discuss the major transparency and reproducibility issues across the social sciences today, including the problems of fraud, publication bias and data mining.
We will also discuss many of the emerging solutions to these problems, including:
- pre-registering studies and writing pre-analysis plans
- performing replications
- conducting meta-analyses
- making data open and available
- visualizing data in ways that are honest and effective.
Apply tools to make your own social science research open and transparent
By the end of the three-week course, you will:
- understand the root and systemic causes of limited transparency and openness in social science research
- explore the tools that you can use to improve transparency in your own research and identify flaws in other people’s work
- apply these tools to real data presented in problem sets
- discuss the tools that publishers are beginning to use to incentivize transparency and reproducibility
- reflect on your own research experiences and assess how you might improve your methods.
Learn with research transparency experts at UC Berkeley.
The course has been developed by the Berkeley Initiative for Transparency in the Social Sciences (BITSS). Headquartered at UC Berkeley, BITSS has been leading the social science research transparency movement since 2012.
Through the course, you will learn with lead educator, Ted Miguel – a leading expert in research transparency, who is widely respected within the social science and international development communities.
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
What will you achieve?
By the end of the course, you'll be able to...
- Develop an understanding of the root and systemic causes of limited transparency and openness in social science research, including publication bias, p-hacking, and fraud.
- Explore the tools that you can use to improve transparency in your own research and identify flaws in other research.
- Apply research transparency tools (such as p-curve.com) to real data presented in interactive quizzes and other activities.
- Establish an account on the Open Science Framework (OSF), explore the platform, and reflect on how you might use it in your own work.
- Discuss the tools that publishers are beginning to use to incentivize research transparency and reproducibility.
- Learn how to design a pre-analysis plan (PAP), as well as explore study registries where PAPs are posted.
- Explore different frameworks to improve the robustness and credibility of social science research, including meta-analysis and replication.
- Participate in the open science movement and become an engaged researcher!
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.
This school offers programs in:
Last updated November 11, 2017