Even with a few years of experience, interpreting the coefficients of interactions in a regression table can take some time to figure out. Trying to explain these coefficients to a group of non-statistically inclined people is a daunting task.
For example, say you are going to speak to a group of dieticians. They are interested (more…)
We’ve looked at the interaction effect between two categorical variables. Now let’s make things a little more interesting, shall we?
What if our predictors of interest, say, are a categorical and a continuous variable? How do we interpret the interaction between the two? (more…)
In a previous post we discussed using marginal means to explain an interaction to a non-statistical audience. The output from a linear regression model can be a bit confusing. This is the model that was shown.
In this model, BMI is the outcome variable and there are three predictors:
Sometimes what is most tricky about understanding your regression output is knowing exactly what your software is presenting to you.
Here’s a great example of what looks like two completely different model results from SPSS and Stata that in reality, agree.
I ran a linear model regressing “physical composite score” on education and “mental composite score”.
The outcome variable, physical composite score, is a measurement of one’s physical well-being. The predictor “education” is categorical with four categories. The other predictor, mental composite score, is continuous and measures one’s mental well-being.
I am interested in determining whether the association between physical composite score and mental composite score is different among the four levels of education. To determine this I included an interaction between mental composite score and education.
Here is the result of the regression using SPSS:
The concept of a statistical interaction is one of those things that seems very abstract. Obtuse definitions, like this one from Wikipedia, don’t help:
In statistics, an interaction may arise when considering the relationship among three or more variables, and describes a situation in which the simultaneous influence of two variables on a third is not additive. Most commonly, interactions are considered in the context of regression analyses.
First, we know this is true because we read it on the internet! Second, are you more confused now about interactions than you were before you read that definition? (more…)
If you have significant a significant interaction effect and non-significant main effects, would you interpret the interaction effect?
It’s a question I get pretty often, and it’s a more straightforward answer than most.