There are five specific empirical components that affect statistical power in applied research
Here are the five empirical components that influence statistical power:
1. The scale of measurement of the outcome
2. The research design
3. The magnitude of the effect size
4. The variance of the effect size
5. The sample size
Researchers can increase statistical power by measuring for continuous outcomes (increased precision and accuracy in measurement), using within-subjects research designs (each participant serves as their own control), hypothesizing large effect sizes with limited variance (large and homogeneous effects are easier to detect), and collecting a large sample size (there is a strong positive association between sample size and statistical power). Conducting an a priori statistical power analysis is an important part of planning any study.
Statistical Power and Isomorphism
Or, continuing with the question above, a power analysis will tell you "how many people you need in your study to find a p-value below .05!"
Conducting an a priori statistical power analysis is an integral part of planning any study. Running an a priori power analysis gives researchers an idea of how many observations of the outcome will be needed to detect a clinically meaningful effect.
One potential framework for understanding statistical power is grounded in the phenomenon known as isomorphism. Isomorphism can be defined as the systematic interdependency between empirical constructs. A change or decision made in one empirical area will always cause a predictable and logical change in the other associated constructs. The sample size, the scale of measurement of the outcome, the choice of research design, and the magnitude and variance of the effect size each affect statistical power and each other in a predictable manner. There are some fundamental principles that explain the isomorphic tendencies between the five components of an a priori power analysis:
Flexibility means being able to detect both small and large effects, regardless of limited or extensive variance.
Flexibility means being able to detect both small and large effect sizes, regardless of limited or extensive variance.
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