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Mixed and Multilevel Models

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

Tagged With: multilevel model, nested, random slope

Related Posts

  • Member Training: A Gentle Introduction to Multilevel Models
  • Member Training: Elements of Experimental Design
  • The Difference Between Random Factors and Random Effects
  • Member Training: Latent Growth Curve Models

December Member Training: Missing Data

by TAF Support

Missing data causes a lot of problems in data analysis. Unfortunately, some of the “solutions” for missing data cause more problems than they solve.

[Read more…] about December Member Training: Missing Data

Tagged With: data issues, listwise deletion, mean imputation, Missing Data, Multiple Imputation

Related Posts

  • Member Training: Elements of Experimental Design
  • Member Training: Hierarchical Regressions
  • Member Training: Multiple Imputation for Missing Data
  • Linear Mixed Models for Missing Data in Pre-Post Studies

Three Designs that Look Like Repeated Measures, But Aren’t

by Karen Grace-Martin 1 Comment

Repeated measures is one of those terms in statistics that sounds like it could apply to many situations. In fact, it describes only one specific situation.

A repeated measures design is one where each subject is measured repeatedly over time, space, or condition on the dependent variable. 

These repeated measurements on the same subject are not independent of each other. They’re clustered. They are more correlated to each other than they are to responses from other subjects. Even if both subjects are in the same condition.  [Read more…] about Three Designs that Look Like Repeated Measures, But Aren’t

Tagged With: autocorrelation, clustered data, communicate results, correlated variable, Repeated Measures

Related Posts

  • Six Differences Between Repeated Measures ANOVA and Linear Mixed Models
  • The Difference Between Clustered, Longitudinal, and Repeated Measures Data
  • Can I Treat 5 Waves of Repeated Measurements as Categorical or Continuous?
  • Linear Mixed Models for Missing Data in Pre-Post Studies

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

Related Posts

  • January Member Training: A Gentle Introduction To Random Slopes In Multilevel Models
  • Member Training: Elements of Experimental Design
  • Member Training: Latent Growth Curve Models
  • Member Training: Generalized Linear Models

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

Related Posts

  • The Difference Between Random Factors and Random Effects
  • Mixed Models: Can you specify a predictor as both fixed and random?
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  • Covariance Matrices, Covariance Structures, and Bears, Oh My!

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

Related Posts

  • Member Training: Hierarchical Regressions
  • December Member Training: Missing Data
  • Member Training: Crossed and Nested Factors
  • Member Training: Interactions in ANOVA and Regression Models, Part 2

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This Month’s Statistically Speaking Live Training

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

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(4th Edition)

by Stephen Sweet and
Karen Grace-Martin

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