Most of us know that binary logistic regression is appropriate when the outcome variable has two possible outcomes: success and failure.
There are two more situations that are also appropriate for binary logistic regression, but they don’t always look like they should be.
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.
(more…)
Statistics is, to a large extent, a science of comparison. You are trying to test whether one group is bigger, faster, or smarter than another.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?