Mixed and Multilevel Models

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

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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

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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:

• All Generalized Linear Models, including logistic, probit, beta, Poisson, negative binomial regression
• Linear Mixed Models
• Generalized Linear Mixed Models
• Parametric Survival Analysis models, like Weibull models
• Structural Equation Models

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…)

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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…)

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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…)

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