Power Analysis

When you are designing a quantitative study, you need to consider the potential power you have to detect effects. Any power analysis you run will have four components:

1. Alpha (how often are you OK with thinking there's an effect there when there actually isn't? Typically in developmental science these are set at .05; or 5% of the time)
   
2. Beta (How often are you OK with missing an effect that might actually be there? Usually we say 20% of the time: Power is 1- that probability, so  .80) 
   
3. Sample Size (number of people)
   
4. Effect size (how big can you expect the difference or relation you are looking for to be?)

These four components are all parts of the same equation: If you know three of these then you can always predict the fourth. Just like an equation like 3 = X + 1, you create one degree of freedom when you leave one of those four elements blank. That means you can use a power analysis to either pick how much power you have, how many people you need, or how big of an effect you can detect. 

In education,we also often need to consider multiple factors like how the data are structured, like how random assignment occurred (if it's happening at all), or whether kids are nested in classrooms. These factors add additional elements to these equations. In this powerpoint presentation, I go over some of the features of how to calculate a power analysis when you're planning education research.