Course
Hierarchical and Mixed Effects Models in R
AdvancedSkill Level
Updated 01/2026RProbability & Statistics4 hr13 videos55 Exercises4,750 XP22,857Statement of Accomplishment
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Prerequisites
Generalized Linear Models in R1
Overview and Introduction to Hierarchical and Mixed Models
The first chapter provides an example of when to use a mixed-effect and also describes the parts of a regression. The chapter also examines a student test-score dataset with a nested structure to demonstrate mixed-effects.
2
Linear Mixed Effect Models
This chapter providers an introduction to linear mixed-effects models. It covers different types of random-effects, describes how to understand the results for linear mixed-effects models, and goes over different methods for statistical inference with mixed-effects models using crime data from Maryland.
3
Generalized Linear Mixed Effect Models
This chapter extends linear mixed-effects models to include non-normal error terms using generalized linear mixed-effects models. By altering the model to include a non-normal error term, you are able to model more kinds of data with non-linear responses. After reviewing generalized linear models, the chapter examines binomial data and count data in the context of mixed-effects models.
4
Repeated Measures
This chapter shows how repeated-measures analysis is a special case of mixed-effect modeling. The chapter begins by reviewing paired t-tests and repeated measures ANOVA. Next, the chapter uses a linear mixed-effect model to examine sleep study data. Lastly, the chapter uses a generalized linear mixed-effect model to examine hate crime data from New York state through time.
Hierarchical and Mixed Effects Models in R
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FAQs
What types of mixed-effects models does this course teach?
You will learn linear mixed-effect regressions, generalized linear mixed-effect regressions for binary and count data, and repeated-measures analysis as a special case of mixed-effect modeling.
How advanced is this course and what prior knowledge do I need?
This is an advanced course requiring knowledge of linear regression, generalized linear models, ggplot2, dplyr, and basic R programming. It has nine prerequisite courses.
Does the course use real datasets for the exercises?
Yes. You will work with real data throughout the course to answer interesting questions using mixed-effects models, applying what you learn to practical scenarios.
Will I learn when to use a random effect versus a fixed effect?
Yes. The course begins by reviewing slopes and intercepts in linear regressions before explaining what random effects are and how to decide when to use them.
How long does this course typically take to finish?
The course has 4 chapters and 55 exercises. The median completion time is about 3.85 hours, though individual pace may vary.
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