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Dealing With Low Statistical Power - Threat To Statistical Conclusion Validity - Research Methods

In this video, I discuss Shadish, Cook, and Campbell’s (2002) idea about statistical conclusion validity. This is where you draw conclusions about the covariance of variables, but for some other reason the statistical conclusion is incorrect.

In this video, I’m talk about low statistical power. Low statistical power occurs when you have too much noise to understand the signal in a covariance between variables. It manifests itself in non-statistical significance of the variable when there really is a relationship between the variables.

Ways to increase statistical power either focus on increasing the ‘effect’ of the signal, or reducing the variability of other factors that increase noise.

1. Measure and correct for powerful covariates. Focus on covariates that are going to truly matter.
2. Use matching. This reduces the effects of all other variables.
3. Increase sample size. Try to find more subjects.
4. Improve measurement. Try to find a better measurement for your study.
5. Increase the treatment - either in the strength or the variability.
6. Find more similar subjects. Homogenous subjects reduce other factors.


References

Shadish, W.R., T.D. Cook, D.T. Campbell. 2002. Experimental and Quasi-Experimental Designs for Generalized Causal Inference, 2nd Edition ed. Houghton Mifflin Company, Boston, MA.

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David Maslach is a research professor of entrepreneurship, innovation, and business strategy, I discuss topics, such as behavioral science, strategy, innovation, and entrepreneurship, and apply these to my new peer proofreading and editing platform. Topics include the sharing economy, altruism, investing in technology, and bounded rationality. My favorite videos pertain to incentives, goal setting, and learning from failure to drive behaviors such as weight loss, stopping telemarketers, creating novel technologies, and creating new movements.

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