How to Use the Fitted Mixed Model to Calculate Predicted Values

by Karen

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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.

If you missed the webinar live, this and the other questions in this series may make more sense if you watch that first. It was part of our free webinar series, The Craft of Statistical Analysis, and you can sign up to get the free recording, handout, and data set at this link:

http://TheCraftofStatisticalAnalysis.com/random-intercept-and-random-slope-models

Interested in learning more? Check out our free webinar recording, Random Intercept and Random Slope Models: An Introduction to Mixed Models.

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How to Interpret the Coefficients of Fixed Effects in Random Slope Models

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 the full article →

Why Call a Model with a Random Intercept and Slope a Random Slope Model?

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 the full article →

How to Produce Intercepts if the Random Slope Model Produces a Variance Estimate, Not Coefficients

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 the full article →

Is a Random Intercept Different in Each Treatment Group?

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 the full article →

August 2017 Member Webinar: Making Sense of Statistical Distributions

If your statistical training was typical, it centered on the normal distribution and models that assume the data are normal.

And while the normal distribution is incredibly helpful in many applications, it’s easy to forget that it isn’t so “normal” when dealing with actual data.

So… what do you do when the normal distribution just doesn’t work?

Read the full article →

What Are Nested Models?

Pretty much all of the common statistical models we use, with the exception of OLS Linear Models, use Maximum Likelihood estimation.

This includes favorites like:

All Generalized Linear Models, including logistic, probit, Poisson, beta, negative binomial regression
Linear Mixed Models
Generalized Linear Mixed Models
Parametric Survival Analysis models, like Weibull models
Structural Equation Models
That’s a lot of models.

If you’ve ever learned any of these, you’ve heard that some of the statistics that compare model fit in competing models require that models be nested. This is particularly important while you’re trying to do model building.

Read the full article →

July 2017 Member Webinar: The Multi-Faceted World of Residuals

Residuals can be a very broad topic – one that most everyone has heard of, but few people truly understand. It’s time to change that.

By definition, a “residual” is “the quantity remaining after other things have been subtracted or allowed for.” In statistics, we use the term in a similar fashion.

Residuals come in various forms:

Standardized
Studentized
Pearson
Deviance

But which ones do we use… and why?

Read the full article →

June 2017 Member Webinar: Mediated Moderation and Moderated Mediation

Often a model is not a simple process from a treatment or intervention to the outcome. In essence, the value of X does not always directly predict the value of Y.

Mediators can affect the relationship between X and Y. Moderators can affect the scale and magnitude of that relationship. And sometimes the mediators and moderators affect each other.

Read the full article →

What Is Latent Class Analysis?

One of the most common—and one of the trickiest—challenges in data analysis is deciding how to include multiple predictors in a model, especially when they’re related to each other. Here’s an example. Let’s say you are interested in studying the relationship between work spillover into personal time as a predictor of job burnout. You have 5 categorical yes/no variables that indicate whether a particular symptom of work spillover is present (see below). While you could use each individual item, you’re not really interested if one in particular is related to the outcome. Perhaps it’s not really each symptom that’s important, but the idea that spillover is happening. One possibility is to count up the number of items to which each respondent said yes. This variable will measure the degree to which spillover is happening. In many studies, this is just what you need. But it doesn’t tell you something important—whether there are certain combinations that generally co-occur, and is it these combinations that affect burnout? In other words, what if it’s not just the degree of spillover that’s important, but the type? Enter Latent Class Analysis (LCA).

Read the full article →

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