Erasmus University Rotterdam - Multilevel Modeling
- Offered byCoursera
- Public/Government Institute
Multilevel Modeling at Coursera Overview
Multilevel Modeling
at Coursera
Duration | 8 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Multilevel Modeling
Table of content
- Overview
- Highlights
- Course Details
- Curriculum
Multilevel Modeling at Coursera Highlights
Multilevel Modeling
at Coursera
- Reset deadlines in accordance to your schedule.
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Understanding of Bachelor-level statistics.
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Multilevel Modeling at Coursera Course details
Multilevel Modeling
at Coursera
Skills you will learn
More about this course
- The objective of this course is to get participants acquainted with multilevel models.
- These models are often used for the analysis of hierarchical data, in which observations are nested within higher level units (e.g. repeated measures nested within individuals, or pupils nested within schools).
- In this type of data causes of outcomes (e.g. the performance of pupils in schools) are located both at the level of the individual (e.g., own and parental resources), and at a higher, contextual, level shared by some of the individuals (e.g. characteristics of the class and of the teacher). Because of this, the assumption of 'independent observations' is violated with hierarchical data, but multilevel modelling can easily account for that. Moreover, multilevel modelling can easily deal with missing data (in most circumstances).
Multilevel Modeling at Coursera Curriculum
Multilevel Modeling
at Coursera
Errata
Errata
Introduction to Multilevel Modeling (MLM)
1. Why We Need Multilevel Analysis
2. My First Multilevel Model
Multiple Regression in R
Multilevel Regression in R
Intro to (Multilevel) Regression
Random Slopes and Cross-Level Interactions
Multilevel Models with Random Slopes
Cross-level Interactions
Methodological Considerations: (Dis)Aggregation
Methodological Considerations: Centering
Random Slopes in R
Cross-level Interactions in R
Random Slopes, Cross-level Interactions, and Centering
Putting it all Together
Steps of a Multilevel Analysis
Steps of a Multilevel Analysis in R
Steps of a Multilevel Analysis
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