Mixed models are hard.
They’re abstract, they’re a little weird, and there is not a common vocabulary or notation for them.
But they’re also extremely important to understand because many data sets require their use.
Repeated measures ANOVA has too many limitations. It just doesn’t cut it any more.
One of the most difficult parts of fitting mixed models is figuring out which random effects to include in a model. And that’s hard to do if you don’t really understand what a random effect is or how it differs from a fixed effect. (more…)
When a model has a binary outcome, one common effect size is a risk ratio. As a reminder, a risk ratio is simply a ratio of two probabilities. (The risk ratio is also called relative risk.)
Risk ratios are a bit trickier to interpret when they are less than one.
A predictor variable with a risk ratio of less than one is often labeled a “protective factor” (at least in Epidemiology). This can be confusing because in our typical understanding of those terms, it makes no sense that a risk be protective.
So how can a RISK be protective? (more…)
In a recent article, we reviewed the impact of removing the intercept from a regression model when the predictor variable is categorical. This month we’re going to talk about removing the intercept when the predictor variable is continuous.
Spoiler alert: You should never remove the intercept when a predictor variable is continuous.
Here’s why. (more…)
by Christos Giannoulis, PhD
Attributes are often measured using multiple variables with different upper and lower limits. For example, we may have five measures of political orientation, each with a different range of values.
Each variable is measured in a different way. The measures have a different number of categories and the low and high scores on each measure are different.
(more…)
by Jeff Meyer
As mentioned in a previous post, there is a significant difference between truncated and censored data.
Truncated data eliminates observations from an analysis based on a maximum and/or minimum value for a variable.
Censored data has limits on the maximum and/or minimum value for a variable but includes all observations in the analysis.
As a result, the models for analysis of these data are different. (more…)
Last week I had the pleasure of teaching a webinar on Interpreting Regression Coefficients. We walked through the output of a somewhat tricky regression model—it included two dummy-coded categorical variables, a covariate, and a few interactions.
As always seems to happen, our audience asked an amazing number of great questions. (Seriously, I’ve had multiple guest instructors compliment me on our audience and their thoughtful questions.)
We had so many that although I spent about 40 minutes answering (more…)
