About
Most of us have encountered the idea that randomization is important for proper experimental design, but may have less clear guidance on how to randomize or why randomization is so important. We also might be distracted by common appeals to the “randomness” of winning lottery numbers or other patterned behaviors that feel like pure chance.
Randomization in experimental design is about systematically and thoughtfully imposing an order onto our treatment of variables. Specifically, randomization is a series of actions that reduce the interference of confounding variables and disturbance variables as you study causal relationships. Intentional action like this doesn’t feel very random, and that’s on purpose.
This unit will help you examine the potential ramifications of not controlling for confounding factors, which would skew your results and prevent you from collecting useful data. They also supply an opportunity to explore a few randomization methods that can be applied in your own research.
Overview of Lessons and Goals
Explore a dataset to review relationships between variables, and see how those relationships impact your ability to identify true effects.
- Begin to build an intuition on the impacts of confounding variables in a study and how they skew causal inference.
- Self-check: How do I assess relationships between variables?
Acknowledgments
Material is CC BY Community for Rigor
Authors: Lisa Mangiamele and Mary Harrington
Contributor: CENTER staff (C4R staff)
Explore a dataset to review relationships between variables, and see how those relationships impact your ability to identify true effects.
Modules
Something about supplementary materials
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