Statistical controls
Channel: Mikko Rönkkö
Date: 2019-07-12

The video delves into the concept of statistical control in business research, specifically addressing how it aids in making causal claims when experiments are not feasible. It emphasizes introducing plausible alternative explanations in an analysis to ensure a fair comparison. For instance, when comparing men-led companies to women-led companies, potential confounding factors such as company size need to be factored into the analysis. The relationship between gender, performance, and company size is exemplified, and the strategy of "matching" to make samples more comparable is discussed.

The video then elaborates on the impracticality of "matching" when dealing with multiple control variables, and recommends the use of a statistical model instead. It underscores the importance of carefully selecting and justifying control variables based on theory, and not just defaulting to a standard set like gender or industry revenue. Finally, the video highlights the risk of omitted variable bias if an important control variable is ignored, and concludes by comparing the method of statistical controls to experimental approaches in research. It underscores that in statistical controls, researchers are passive observers who need to consider alternate explanations based on existing theory and confirm or rule them out using control variables in their analysis.

Slides: https://osf.io/7c2xy