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Mixed and Multilevel Models

Eight Ways to Detect Multicollinearity

by Karen Grace-Martin 5 Comments

Multicollinearity can affect any regression model with more than one predictor. It occurs when two or more predictor variables overlap so much in what they measure that their effects are indistinguishable.

When the model tries to estimate their unique effects, it goes wonky (yes, that’s a technical term).

So for example, you may be interested in understanding the separate effects of altitude and temperature on the growth of a certain species of mountain tree.

[Read more…] about Eight Ways to Detect Multicollinearity

Tagged With: Bivariate Statistics, Correlated Predictors, linear regression, logistic regression, Multicollinearity, p-value, predictor variable, regression models

Related Posts

  • A Visual Description of Multicollinearity
  • Steps to Take When Your Regression (or Other Statistical) Results Just Look…Wrong
  • Is Multicollinearity the Bogeyman?
  • The Impact of Removing the Constant from a Regression Model: The Categorical Case

The Difference Between Random Factors and Random Effects

by Karen Grace-Martin 6 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

Related Posts

  • Specifying Fixed and Random Factors in Mixed Models
  • Multilevel, Hierarchical, and Mixed Models–Questions about Terminology
  • Is there a fix if the data is not normally distributed?
  • What packages allow you to deal with random intercept and random slope models in R?

Member Training: Latent Growth Curve Models

by Jeff Meyer 2 Comments

What statistical model would you use for longitudinal data to analyze between-subject differences with within-subject change?

Most analysts would respond, “a mixed model,” but have you ever heard of latent growth curves? How about latent trajectories, latent curves, growth curves, or time paths, which are other names for the same approach?

[Read more…] about Member Training: Latent Growth Curve Models

Tagged With: between-subject, Latent Growth Curve Model, Longitudinal Data, Mixed, model, SEM, Structural Equation Modeling, within-subject

Related Posts

  • Member Training: Reporting Structural Equation Modeling Results
  • One of the Many Advantages to Running Confirmatory Factor Analysis with a Structural Equation Model
  • First Steps in Structural Equation Modeling: Confirmatory Factor Analysis
  • Member Training: Introduction to Structural Equation Modeling

Understanding Random Effects in Mixed Models

by Kim Love 2 Comments

In fixed-effects models (e.g., regression, ANOVA, generalized linear models), there is only one source of random variability. This source of variance is the random sample we take to measure our variables.

It may be patients in a health facility, for whom we take various measures of their medical history to estimate their probability of recovery. Or random variability may come from individual students in a school system, and we use demographic information to predict their grade point averages.

[Read more…] about Understanding Random Effects in Mixed Models

Tagged With: generalized linear mixed model

Related Posts

  • Regression Diagnostics in Generalized Linear Mixed Models
  • What is the Purpose of a Generalized Linear Mixed Model?
  • Models for Repeated Measures Continuous, Categorical, and Count Data
  • How to Get SPSS GENLINMIXED Output Without the Model Viewer

What is the Purpose of a Generalized Linear Mixed Model?

by Kim Love 1 Comment

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.

[Read more…] about What is the Purpose of a Generalized Linear Mixed Model?

Tagged With: generalized linear mixed model, random effect, Repeated Measures

Related Posts

  • How to Get SPSS GENLINMIXED Output Without the Model Viewer
  • Specifying a Random Intercept or Random Slope Model in SPSS GENLINMIXED
  • Mixed Models for Logistic Regression in SPSS
  • Member Training: Goodness of Fit Statistics

Member Training: Generalized Linear Models

by guest contributer Leave a Comment

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.
[Read more…] about Member Training: Generalized Linear Models

Tagged With: bayesian, distribution, error distribution, generalized linear models, GLM, linear model, linear regression, link function, logistic regression, maximum likelihood, mixed model, Poisson Regression

Related Posts

  • Member Training: Goodness of Fit Statistics
  • Member Training: Types of Regression Models and When to Use Them
  • Member Training: Matrix Algebra for Data Analysts: A Primer
  • Why Generalized Linear Models Have No Error Term

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