After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
Course Description
This course begins by reviewing slopes and intercepts in linear regressions before moving on to random-effects. You'll learn what a random effect is and how to use one to model your data. Next, the course covers linear mixed-effect regressions. These powerful models will allow you to explore data with a more complicated structure than a standard linear regression. The course then teaches generalized linear mixed-effect regressions. Generalized linear mixed-effects models allow you to model more kinds of data, including binary responses and count data. Lastly, the course goes over repeated-measures analysis as a special case of mixed-effect modeling. This kind of data appears when subjects are followed over time and measurements are collected at intervals. Throughout the course you'll work with real data to answer interesting questions using mixed-effects models.
Prerequisites
Curriculum
Course outline
1
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.
- What is a hierarchical model?50 XP
- Examples of hierarchical datasets100 XP
- Multi-level student data100 XP
- Exploring multiple-levels: Classrooms and schools100 XP
- Parts of a regression50 XP
- Intercepts100 XP
- Slopes and multiple regression100 XP
- Random-effects in regressions with school data50 XP
- Random-effect intercepts100 XP
- Random-effect slopes100 XP
- Building the school model100 XP
- Interpreting the school model100 XP
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.
R
Hierarchical and Mixed Effects Models in R
Course
Complete
