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Mixed and Multilevel Models

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

Member Training: Power Analysis and Sample Size Determination Using Simulation

by guest contributer  Leave a Comment

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.
[Read more…] about Member Training: Power Analysis and Sample Size Determination Using Simulation

Tagged With: ANOVA, effect size, mediation, mixed model, Path Analysis, Power Analysis, quantitative research, sample size, simulation

Related Posts

  • Member Training: A Gentle Introduction to Bootstrapping
  • Member Training: Matrix Algebra for Data Analysts: A Primer
  • Member Training: Generalized Linear Models
  • Member Training: The Fundamentals of Sample Size Calculations

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

by Karen Grace-Martin  Leave a Comment

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.

[Read more…] about Is there a fix if the data is not normally distributed?

Tagged With: covariance terms, linear mixed model, random effect, random intercept, random slope

Related Posts

  • What packages allow you to deal with random intercept and random slope models in R?
  • What is the intercept for each individual in a random slope model?
  • Impact of Covariance Terms on Random Slope Model
  • How to Use the Fitted Mixed Model to Calculate Predicted Values

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.

[Read more…] about What packages allow you to deal with random intercept and random slope models in R?

Tagged With: covariance terms, linear mixed model, random effect, random intercept, random slope

Related Posts

  • Is there a fix if the data is not normally distributed?
  • What is the intercept for each individual in a random slope model?
  • Impact of Covariance Terms on Random Slope Model
  • How to Use the Fitted Mixed Model to Calculate Predicted Values

Can I Treat 5 Waves of Repeated Measurements as Categorical or Continuous?

by Karen Grace-Martin  2 Comments

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?

Tagged With: continuous time, linear mixed model, Repeated Measures

Related Posts

  • Six Differences Between Repeated Measures ANOVA and Linear Mixed Models
  • Linear Mixed Models for Missing Data in Pre-Post Studies
  • Mixed Models: Can you specify a predictor as both fixed and random?
  • Three Designs that Look Like Repeated Measures, But Aren’t

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