# Mixed and Multilevel Models

### How to Produce Intercepts if the Random Slope Model Produces a Variance Estimate, Not Coefficients

August 14th, 2017 by

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.

If you missed the webinar live, this and the other questions in this video series may make more sense if you watch that first. It was part of our free webinar series, The Craft of Statistical Analysis, and you can sign up to get the free recording, handout, and data set at this link:

http://TheCraftofStatisticalAnalysis.com/random-intercept-random-slope-models

### Is a Random Intercept Different in Each Treatment Group?

August 11th, 2017 by

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.

If you missed the webinar live, this and the other questions in this series may make more sense if you watch that first. It was part of our free webinar series, The Craft of Statistical Analysis, and you can sign up to get the free recording, handout, and data set at this link:

http://TheCraftofStatisticalAnalysis.com/random-intercept-random-slope-models

### What Are Nested Models?

July 28th, 2017 by

Pretty much all of the common statistical models we use, with the exception of OLS Linear Models, use Maximum Likelihood estimation.

This includes favorites like:

That’s a lot of models.

If you’ve ever learned any of these, you’ve heard that some of the statistics that compare model fit in competing models require (more…)

### Member Training: Crossed and Nested Factors

May 1st, 2017 by

We often talk about nested factors in mixed models — students nested in classes, observations nested within subject.

But in all but the simplest designs, it’s not that straightforward. (more…)

### Member Training: A Gentle Introduction to Generalized Linear Mixed Models – Part 2

December 1st, 2016 by

Generalized linear mixed models (GLMMs) are incredibly useful tools for working with complex, multi-layered data. But they can be tough to master.

In this follow-up to October’s webinar (“A Gentle Introduction to Generalized Linear Mixed Models – Part 1”), we’ll cover important topics like:

– Distinction between crossed and nested grouping factors
– Software choices for implementation of GLMMs (more…)

### Member Training: A Gentle Introduction to Generalized Linear Mixed Models

October 3rd, 2016 by

Generalized linear mixed models (GLMMs) are incredibly useful—but they’re also a hard nut to crack.

As an extension of generalized linear models, GLMMs include both fixed and random effects. They are particularly useful when an outcome variable and a set of predictor variables are measured repeatedly over time and the outcome variable is a binary, nominal, ordinal or count variable. These models accommodate nesting of subjects in higher level units such as schools, hospitals, etc., and can also incorporate predictor variables collected at these higher levels.

In this webinar, we’ll provide a gentle introduction to GLMMs, discussing issues like: (more…)