In this video I will answer another question from a recent webinar, Random Intercept and Random Slope Models.
We are answering questions here because we had over 500 people live on the webinar so we didn’t have time to get through all the questions.
OptinMon
Is there a fix if the data is not normally distributed?
February 19th, 2018 by Karen Grace-Martin
What packages allow you to deal with random intercept and random slope models in R?
February 13th, 2018 by Karen Grace-Martin
In this video I will answer a question from a recent webinar, Random Intercept and Random Slope Models.
We are answering questions here because we had over 500 people live on the webinar so we didn’t have time to get through all the questions.
Can I Treat 5 Waves of Repeated Measurements as Categorical or Continuous?
January 29th, 2018 by Karen Grace-Martin
Question: Can you talk more about categorical and repeated Time? If I have 5 waves at ages 0, 1Â year, 3 years, 5 years, and 9 years, would that be categorical or repeated? Does mixed account for different spacing in time?
Mixed models can account for different spacing in time and you’re right, it entirely depends on whether you treat Time as categorical or continuous.
First let me mention that not all designs can treat time as either categorical or continuous. The reason it could go either way in your example is because time is measured discretely, yet there are enough numerical values that you could fit a line to it. (more…)
Six Differences Between Repeated Measures ANOVA and Linear Mixed Models
January 22nd, 2018 by Karen Grace-Martin
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…)
Using Pairwise Comparisons to Help you Interpret Interactions in Linear Regression
January 12th, 2018 by Jeff Meyer
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:
Segmented Regression for Non-Constant Relationships
January 8th, 2018 by Jeff Meyer
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…)