I am reviewing your notes from your workshop on assumptions. You have made it very clear how to analyze normality for regressions, but I could not find how to determine normality for ANOVAs. Do I check for normality for each independent variable separately? Where do I get the residuals? What plots do I run? Thank you!
I received this great question this morning from a past participant in my Assumptions of Linear Models workshop.
It’s one of those quick questions without a quick answer. Or rather, without a quick and useful answer. The quick answer is:
Do it exactly the same way. All of it.
The longer, useful answer is this: (more…)
R² is such a lovely statistic, isn’t it? Unlike so many of the others, it makes sense–the percentage of variance in Y accounted for by a model.
I mean, you can actually understand that. So can your grandmother. And the clinical audience you’re writing the report for.
A big R² is always good and a small one is always bad, right?
Well, maybe. (more…)
If you learned much about calculating power or sample sizes in your statistics classes, chances are, it was on something very, very simple, like a z-test.
But there are many design issues that affect power in a study that go way beyond a z-test. Like:
- repeated measures
- clustering of individuals
- blocking
- including covariates in a model
Regular sample size software can accommodate some of these issues, but not all. And there is just something wonderful about finding a tool that does just what you need it to.
Especially when it’s free.
Enter Optimal Design Plus Empirical Evidence software. (more…)
Factor is confusing much in the same way as hierarchical and beta, because it too has different meanings in different contexts. Factor might be a little worse, though, because its meanings are related.
In both meanings, a factor is a variable. But a factor has a completely different meaning and implications for use in two different contexts. (more…)
Generalized linear models, linear mixed models, generalized linear mixed models, marginal models, GEE models. You’ve probably heard of more than one of them and you’ve probably also heard that each one is an extension of our old friend, the general linear model.
This is true, and they extend our old friend in different ways, particularly in regard to the measurement level of the dependent variable and the independence of the measurements. So while the names are similar (and confusing), the distinctions are important.
It’s important to note here that I am glossing over many, many details in order to give you a basic overview of some important distinctions. These are complicated models, but I hope this overview gives you a starting place from which to explore more. (more…)
You may have noticed conflicting advice about whether to leave insignificant effects in a model or take them out in order to simplify the model.
One effect of leaving in insignificant predictors is on p-values–they use up precious df in small samples. But if your sample isn’t small, the effect is negligible.
The bigger effect is on interpretation, and really the above cases are about whether it aids interpretation to leave them in. Models do get so cluttered it’s hard to figure out what’s going on, and it makes sense to eliminate effects that aren’t serving a purpose, but even insignificant effects can have a purpose. (more…)
