Foundations of Quantitative Research in Political Science
Introduction to Research Design
After we have identified a research question and posed hypotheses, we need to identify our research design--how we will collect and analyze our data. As we will see as we progress through this class, the way we collect our data has important implications for how we go about analyzing the data.
In this module, we introduce you to an ideal research design--experiments (aka “randomized controlled trials”)--and explain how they help us make causal claims. In the second video, we discuss ways to account for confounding variables when we don't have experimental designs. Throughout this module, we acknowledge that there are situations in which it might not be possible or ethical to conduct experiments.
Module Learning Outcomes
After completing this module, you should be able to...
- Identify a randomized experiment and articulate how it is different from observational studies
- Define ‘treatment’ and identify the treatment group in an example experiment
- Explain how randomized experiments mitigate our concerns about confounds
The image below shows a simplified version of the scientific method, and highlights the where concepts covered in this module fit into the larger process.
Sources
- Gerber, A. S. & Green, D. P. (2000). The effects of canvassing, telephone calls, and direct mail on voter turnout: A field experiment. American Political Science Review, 94(3), 653–663. http://isps.yale.edu/sites/default/files/publication/2012/12/ISPS00-001.pdf Links to an external site.
- Green, D. P., & Gerber, A. S. (2019). Get out the vote: How to increase voter turnout. Brookings Institution Press.
- Gerber, A. S., & Green, D. P. (2012). Field experiments: Design, analysis, and interpretation. WW Norton.
- Parlapiano, A. & Pearce, A. (2016). "For Every 10 U.S. Adults, Six Vote and Four Don't. What Separates Them?" https://www.nytimes.com/interactive/2016/09/13/us/politics/what-separates-voters-and-nonvoters.html