A well-fitting regression model results in predicted values close to the observed data values. The mean model, which uses the mean for every predicted value, generally would be used if there were no useful predictor variables. The fit of a proposed regression model should therefore be better than the fit of the mean model. But how do you measure that model fit?
Karen Grace-Martin
Beyond R-squared: Assessing the Fit of Regression Models
February 20th, 2024 by Karen Grace-Martin
The Difference Between Crossed and Nested Factors
December 18th, 2023 by Karen Grace-Martin
One of those tricky, but necessary, concepts in statistics is the difference between crossed and nested factors.
As a reminder, a factor is any categorical independent variable. In experiments, or any randomized designs, these factors are often manipulated. Experimental manipulations (like Treatment vs. Control) are factors.
Observational categorical predictors, such as gender, time point, poverty status, etc., are also factors. Whether the factor is observational or manipulated won’t affect the analysis, but it will affect the conclusions you draw from the results.
Five Ways to Analyze Ordinal Variables (Some Better than Others)
December 3rd, 2023 by Karen Grace-Martin
There are not a lot of statistical methods designed just to analyze ordinal variables.
But that doesn’t mean that you’re stuck with few options. There are more than you’d think.
Some are better than others, but it depends on the situation and research questions.
Here are five options when your dependent variable is ordinal.
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Concepts in Linear Regression to know before learning Multilevel Models
November 21st, 2023 by Karen Grace-Martin
Are you learning Multilevel Models? Do you feel ready? Or in over your head?
It’s a very common analysis to need to use. I have to say, learning it is not so easy on your own. The concepts of random effects are hard to wrap your head around and there is a ton of new vocabulary and notation. Sadly, this vocabulary and notation is not consistent across articles, books, and software, so you end up having to do a lot of translating.
The Difference between Standard Deviation and Standard Error
November 10th, 2023 by Karen Grace-Martin
Standard deviation and standard error are statistical concepts you probably learned well enough in Intro Stats to pass the test. Conceptually, you understand them, yet the difference doesn’t make a whole lot of intuitive sense.
So in this article, let’s explore the difference between the two. We will look at an example, in the hopes of making these concepts more intuitive. You’ll also see why sample size has a big effect on standard error. (more…)
Mixed Models with Crossed Random Factors
October 23rd, 2023 by Karen Grace-Martin
When you hear about multilevel models or mixed models, you very often think of a nested design. Level 1 units nested in Level 2 units, which are in turn possibly nested in Level 3 units. But these variables that define the units and that become random factors in the model can, in fact, be crossed with each other, not nested.