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Mixed and Multilevel Models

The Intraclass Correlation Coefficient in Mixed Models

by Karen Grace-Martin  22 Comments

The ICC, or Intraclass Correlation Coefficient, can be very useful in many statistical situations, but especially so in Linear Mixed Models.

Linear Mixed Models are used when there is some sort of clustering in the data.

Two common examples of clustered data include:

  • individuals were sampled within sites (hospitals, companies, community centers, schools, etc.). The site is the cluster.
  • repeated measures or longitudinal data where multiple observations are collected from the same individual. The individual is the cluster in which multiple observations are [Read more…] about The Intraclass Correlation Coefficient in Mixed Models

Tagged With: Intraclass Correlation Coefficient, mixed model

Related Posts

  • Specifying Fixed and Random Factors in Mixed Models
  • The Difference Between Random Factors and Random Effects
  • Examples for Writing up Results of Mixed Models
  • The Difference Between Crossed and Nested Factors

Member Training: Using Excel to Graph Predicted Values from Regression Models

by Karen Grace-Martin  1 Comment

Graphing predicted values from a regression model or means from an ANOVA makes interpretation of results much easier.

Every statistical software will graph predicted values for you. But the more complicated your model, the harder it can be to get the graph you want in the format you want.

Excel isn’t all that useful for estimating the statistics, but it has some very nice features that are useful for doing data analysis, one of which is graphing.

In this webinar, I will demonstrate how to calculate predicted means from a linear and a logistic regression model, then graph them. It will be particularly useful to you if you don’t have a very clear sense of where those predicted values come from.


Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

Not a Member? Join!

About the Instructor

Karen Grace-Martin helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.

She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor. She has master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.

Not a Member Yet?
It’s never too early to set yourself up for successful analysis with support and training from expert statisticians. Just head over and sign up for Statistically Speaking.

You'll get access to this training webinar, 100+ other stats trainings, a pathway to work through the trainings that you need — plus the expert guidance you need to build statistical skill with live Q&A sessions and an ask-a-mentor forum.

Tagged With: ANOVA, excel, graphing, linear regression, logistic regression

Related Posts

  • Member Training: Hierarchical Regressions
  • Member Training: Types of Regression Models and When to Use Them
  • Member Training: The Link Between ANOVA and Regression
  • Member Training: Centering

Member Training: Hierarchical Regressions

by Karen Grace-Martin  Leave a Comment

Hierarchical regression is a very common approach to model building that allows you to see the incremental contribution to a model of sets of predictor variables.Stage 2

Popular for linear regression in many fields, the approach can be used in any type of regression model — logistic regression, linear mixed models, or even ANOVA.

In this webinar, we’ll go over the concepts and steps, and we’ll look at how it can be useful in different contexts.


Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
Not a Member? Join!

About the Instructor

Karen Grace-Martin helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.

She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor. She has master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.

Not a Member Yet?
It’s never too early to set yourself up for successful analysis with support and training from expert statisticians. Just head over and sign up for Statistically Speaking.

You'll get access to this training webinar, 100+ other stats trainings, a pathway to work through the trainings that you need — plus the expert guidance you need to build statistical skill with live Q&A sessions and an ask-a-mentor forum.

Tagged With: ANOVA, hierarchical regression, linear mixed model, logistic regression

Related Posts

  • Member Training: Using Excel to Graph Predicted Values from Regression Models
  • Member Training: Types of Regression Models and When to Use Them
  • Member Training: The Link Between ANOVA and Regression
  • Member Training: Centering

Concepts you Need to Understand to Run a Mixed or Multilevel Model

by Karen Grace-Martin  4 Comments

Have you ever been told you need to run a mixed (aka: multilevel) model and been thrown off by all the new vocabulary?

It happened to me when I first started my statistical consulting job, oh so many years ago. I had learned mixed models in an ANOVA class, so I had a pretty good grasp on many of the concepts.

But when I started my job, SAS had just recently come out with Proc Mixed, and it was the first time I had to actually implement a true multilevel model.  I was out of school, so I had to figure it out on the job.

And even with my background, I had a pretty steep learning curve to get to a point where it made sense.  Sure, I was able to figure out the steps, but there are some pretty tricky situations and complicated designs out there.

To implement it well, you need a good understanding of the big picture, and how the small parts fit into it.  [Read more…] about Concepts you Need to Understand to Run a Mixed or Multilevel Model


Related Posts

  • What Are Nested Models?
  • Five Extensions of the General Linear Model
  • Member Training: Multinomial Logistic Regression
  • Member Training: Missing Data

Analyzing Pre-Post Data with Repeated Measures or ANCOVA

by Karen Grace-Martin  87 Comments

One area in statistics where I see conflicting advice is how to analyze pre-post data. I’ve seen this myself in consulting. A few years ago, I received a call from a distressed client. Let’s call her Nancy.

Nancy had asked for advice about how to run a repeated measures analysis. The advisor told Nancy that actually, a repeated measures analysis was inappropriate for her data.

Nancy was sure repeated measures was appropriate. This advice led her to fear that she had grossly misunderstood a very basic tenet in her statistical training.

The Study Design

Nancy had measured a response variable at two time points for two groups. The intervention group received a treatment and a control group did not. Participants were randomly assigned to one of the two groups.

The researcher measured each participant before and after the intervention.

Analyzing the Pre-Post Data

Nancy was sure that this was a classic repeated measures experiment. It has [Read more…] about Analyzing Pre-Post Data with Repeated Measures or ANCOVA

Tagged With: Covariate, pre-post design, Repeated Measures

Related Posts

  • When Does Repeated Measures ANOVA not work for Repeated Measures Data?
  • The Wide and Long Data Format for Repeated Measures Data
  • The Difference Between Crossed and Nested Factors
  • An Example of Specifying Within-Subjects Factors in Repeated Measures

Three Issues in Sample Size Estimates for Multilevel Models

by Karen Grace-Martin  4 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

Related Posts

  • Sample Size Estimates for Multilevel Randomized Trials
  • Multilevel, Hierarchical, and Mixed Models–Questions about Terminology
  • Covariance Matrices, Covariance Structures, and Bears, Oh My!
  • Concepts in Linear Regression you need to know before learning Multilevel Models

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