Sample size estimates are one of those data analysis tasks that look straightforward, but once you try to do one, make you want to bang your head against the computer in frustration. Or, maybe 
that’s just me.
Regardless of how they make you feel, they are super important to do for your study before you collect the data.
After all, with the typical Type I error rate of 5% used in most tests, we are allowing ourselves to “get lucky” 1 in 20 times for each test. When you figure out the probability of Type I error across all the tests, that probability skyrockets.
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Transformations don’t always help, but when they do, they can improve your linear regression model in several ways simultaneously.
They can help you better meet the linear regression assumptions of normality and homoscedascity (i.e., equal variances). They also can help avoid some of the artifacts caused by boundary limits in your dependent variable — and sometimes even remove a difficult-to-interpret interaction.
Interpreting regression coefficients can be tricky, especially when the model has interactions or categorical predictors (or worse – both).
But there is a secret weapon that can help you make sense of your regression results: marginal means.
They’re not the same as descriptive stats. They aren’t usually included by default in our output. And they sometimes go by the name LS or Least-Square means.
And they’re your new best friend.
So what are these mysterious, helpful creatures?
What do they tell us, really? And how can we use them?
Have you ever experienced befuddlement when you dust off a data analysis that you ran six months ago?
Ever gritted your teeth when your collaborator invalidates all your hard work by telling you that the data set you were working on had “a few minor changes”?
Or panicked when someone running a big meta-analysis asks you to share your data?
If any of these experiences rings true to you, then you need to adopt the philosophy of reproducible research.
We often talk about nested factors in mixed models — students nested in classes, observations nested within subject.
But in all but the simplest designs, it’s not that straightforward. (more…)