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Causality in Statistical Power: Isomorphic Properties of Measurement, Research Design, Effect Size, and Sample Size
Newest publication from Dr. Eric Heidel, creator of Research Engineer
Directly related to Statistical Power engine of Research Engineer
My newest published article in Scientifica is now available for download online and on the Research Engineer website. The creation of the Statistical Power engine of Research Engineer led me to write the article. Click on the Download Article button below to download a .pdf directly from the website or click on the Statistical Power button to be taken to the aforementioned engine. Many thanks and regards to everyone that uses Research Engineer! -EH
Within-subjects designs increase statistical power
Each participant serves as their own control in within-subjects designs
Within-subjects designs increase statistical power. because participants serve as their own control. Between-subjects designs necessitate more observations of the outcome to be able to effectively compare independent groups on an outcome. Multivariate analyses further decrease statistical power in that many more observations of the outcome to detect significant effects. At least 20 -40 more observations of the outcome have to collected per variable entered into a simultaneous of hierarchial regression model in order to meet statistical power when trying to account for demographic, etiological, clinical, and confounding effects.
Within-subjects designs, when coupled with with continuous outcomes, large effect sizes, limited variance in the outcome and a large sample size, greatly increase statistical power. Small effect sizes are also easier to detect using within-subjects statistics because participants serve as their own control. Within-subjects design also provide more statistical power when small sample sizes are used.
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