Free Online course
Transparent and Open Social Science Research
8 Jan 2018
Transparent and Open Social Science Research
8 Jan 2018
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.
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?
- 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.
Who will you learn with?
Ted Miguel, I'm a Professor of Economics at UC Berkeley and Faculty Director of the Center for Effective Global Action (CEGA). My main research focus is African economic development and research transparency.
Garret Christensen, I'm a research fellow with BITSS with a PhD in Economics from UC Berkeley. I'm interested in research transparency, reproducibility, and questions of causal inference in labor and development.
