Mixed and Multilevel Models

What is the Purpose of a Generalized Linear Mixed Model?

If you are new to using generalized linear mixed effects models, or if you have heard of them but never used them, you might be wondering about the purpose of a GLMM.

Mixed effects models are useful when we have data with more than one source of random variability. For example, an outcome may be measured more than once on the same person (repeated measures taken over time).

When we do that we have to account for both within-person and across-person variability. A single measure of residual variance can’t account for both.

Member Training: Generalized Linear Models

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In this webinar, we will provide an overview of generalized linear models. You may already be using them (perhaps without knowing it!).

For example, logistic regression is a type of generalized linear model that many people are already familiar with. Alternatively, maybe you’re not using them yet and you are just beginning to understand when they might be useful to you.

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Member Training: Power Analysis and Sample Size Determination Using Simulation

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This webinar will show you strategies and steps for using simulations to estimate sample size and power. You will learn:

A review of basic concepts of statistical power and effect size
A simulation-based approach to power analysis
An overview of how to implement simulations in various popular software programs.

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Is there a fix if the data is not normally distributed?

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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.

What packages allow you to deal with random intercept and random slope models in R?

by Karen Grace-Martin 1 Comment

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?

by Karen Grace-Martin Leave a Comment

Question: Can you talk more about categorical and repeated Time? If I have 5 waves at ages 0, 1 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. [Read more…] about Can I Treat 5 Waves of Repeated Measurements as Categorical or Continuous?