As mixed models are becoming more widespread, there is a lot of confusion about when to use these more flexible but complicated models and when to use the much simpler and easier-to-understand repeated measures ANOVA.
One thing that makes the decision harder is sometimes the results are exactly the same from the two models and sometimes the results are (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:
When you put a continuous predictor into a linear regression model, you assume it has a constant relationship with the dependent variable along the predictor’s range. But how can you be certain? What is the best way to measure this?
And most important, what should you do if it clearly isn’t the case?
Let’s explore a few options for capturing a non-linear relationship between X and Y within a linear regression (yes, really). (more…)
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:
Some variables are straightforward to measure without error – blood pressure, number of arrests, whether someone knew a word in a second language.
But many – perhaps most – are not. Whenever a measurement has a potential for error, a key criterion for the soundness of that measurement is reliability.
Think of reliability as consistency or repeatability in measurements. (more…)
There are multiple ways to interface with R. Some common interfaces are the basic R GUI, R Commander (the package “Rcmdr” that you use on top of the basic R GUI), and RStudio.
When I first started to learn to use R, I was bound and determined to use the basic R GUI.
As someone who was already used to programming in SAS, I wasn’t looking for a (more…)