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multilevel model

Member Training: A Gentle Introduction To Random Slopes In Multilevel Models

by TAF Support

A Gentle Introduction to Random Slopes in Multilevel Modeling

…aka, how to look at cool interaction effects for nested data.

Do the words “random slopes model” or “random coefficients model” send shivers down your spine? These words don’t have to be so ominous. Journal editors are increasingly asking researchers to analyze their data using this particular approach, and for good reason.

[Read more…] about Member Training: A Gentle Introduction To Random Slopes In Multilevel Models

Tagged With: multilevel model, nested, random slope

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  • Member Training: Latent Growth Curve Models

Member Training: A Gentle Introduction to Multilevel Models

by guest

In this Stat’s Amore Training, Marc Diener will help you make sense of the strange terms and symbols that you find in studies that use multilevel modeling (MLM). You’ll learn about the basic ideas behind MLM, different MLM models, and a close look at one particular model, known as the random intercept model. A running example will be used to clarify the ideas and the meaning of the MLM results.

[Read more…] about Member Training: A Gentle Introduction to Multilevel Models

Tagged With: multilevel model, nested

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  • Member Training: A Gentle Introduction To Random Slopes In Multilevel Models
  • Member Training: Elements of Experimental Design
  • Member Training: Latent Growth Curve Models
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Multilevel, Hierarchical, and Mixed Models–Questions about Terminology

by Karen Grace-Martin Leave a Comment

Multilevel models and Mixed Models are generally the same thing. In our recent webinar on the basics of mixed models, Random Intercept and Random Slope Models, we had a number of questions about terminology that I’m going to answer here.

If you want to see the full recording of the webinar, get it here. It’s free.

Q: Is this different from multi-level modeling?

A: No. I don’t really know the history of why we have the different names, but the difference in multilevel modeling [Read more…] about Multilevel, Hierarchical, and Mixed Models–Questions about Terminology

Tagged With: fixed effect, Fixed Factor, hierarchical linear model, mixed model, multilevel model, panel data, random effect, Random Factor

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  • Mixed Models: Can you specify a predictor as both fixed and random?
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Member Training: Elements of Experimental Design

by Karen Grace-Martin

Whether or not you run experiments, there are elements of experimental design that affect how you need to analyze many types of studies.

The most fundamental of these are replication, randomization, and blocking. These key design elements come up in studies under all sorts of names: trials, replicates, multi-level nesting, repeated measures. Any data set that requires mixed or multilevel models has some of these design elements. [Read more…] about Member Training: Elements of Experimental Design

Tagged With: ANOVA, blocking, Crossed factors, Crossover Design, Latin squares, multilevel model, nested models, Regression, Repeated Measures, replication

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  • Member Training: Interactions in ANOVA and Regression Models, Part 2

The Difference Between Random Factors and Random Effects

by Karen Grace-Martin 3 Comments

Mixed models are hard.

They’re abstract, they’re a little weird, and there is not a common vocabulary or notation for them.

But they’re also extremely important to understand because many data sets require their use.

Repeated measures ANOVA has too many limitations. It just doesn’t cut it any more.

One of the most difficult parts of fitting mixed models is figuring out which random effects to include in a model. And that’s hard to do if you don’t really understand what a random effect is or how it differs from a fixed effect. [Read more…] about The Difference Between Random Factors and Random Effects

Tagged With: ANOVA, fixed variable, linear mixed model, mixed model, multilevel model, random effect, Random Factor, random intercept, random slope

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Three Issues in Sample Size Estimates for Multilevel Models

by Karen Grace-Martin 3 Comments

If you’ve ever worked with multilevel models, you know that they are an extension of linear models. For a researcher learning them, this is both good and bad news.

The good side is that many of the concepts, calculations, and results are familiar. The down side of the extension is that everything is more complicated in multilevel models.

This includes power and sample size calculations. [Read more…] about Three Issues in Sample Size Estimates for Multilevel Models

Tagged With: Intraclass Correlation Coefficient, multilevel model, Sample Size Calculations

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  • Concepts in Linear Regression you need to know before learning Multilevel Models

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