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hierarchical linear model

Confusing Statistical Term #10: Mixed and Multilevel Models

by Karen Grace-Martin  5 Comments

What’s the difference between Mixed and Multilevel Models? What about Hierarchical Models or Random Effects models?

I get this question a lot.

The answer: very little.

[Read more…] about Confusing Statistical Term #10: Mixed and Multilevel Models

Tagged With: crossed random effects, hierarchical linear model, individual growth curve model, mixed effects model, mixed model, multilevel model, random coefficient model, random effect, random intercept, Random Slope Model

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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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Confusing Statistical Term #4: Hierarchical Regression vs. Hierarchical Model

by Karen Grace-Martin  2 Comments

This one is relatively simple.  Very similar names for two totally different concepts.Stage 2

Hierarchical Models (aka Hierarchical Linear Models or HLM) are a type of linear regression models in which the observations fall into hierarchical, or completely nested levels.

Hierarchical Models are a type of Multilevel Models.

So what is a hierarchical data structure, which requires a hierarchical model?

The classic example is data from children nested within schools.  The dependent variable could be something like math scores, and the predictors a whole host of things measured about the child and the school.

Child-level predictors could be things like GPA, grade, and gender. School-level predictors could be things like: total enrollment, private vs. public, mean SES.

Because multiple children are measured from the same school, their measurements are not independent.  Hierarchical modeling takes that into account.

Hierarchical regression is a model-building technique in any regression model. It is the practice of building successive linear regression models, each adding more predictors.

For example, one common practice is to start by adding only demographic control variables to the model.   In the next model, you can add predictors of interest, to see if they predict the DV above and beyond the effect of the controls.

You’re actually building separate but related models in each step.  But SPSS has a nice function where it will compare the models, and actually test if successive models fit better than previous ones.

So hierarchical regression is really a series of regular old OLS regression models–nothing fancy, really.

Confusing Statistical Terms #1: Independent Variable

Confusing Statistical Terms #2: Alpha and Beta

Confusing Statistical Terms #3: Levels

Tagged With: hierarchical linear model, hierarchical regression, HLM

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