Week 1

Overview of Multilevel Models

Before you start the materials for week 1, make sure you have reviewed the syllabus.

Week Learning Objectives

By the end of this module, you will be able to

• Identify alternative names for multilevel modeling (MLM)
• Describe the types of data MLM can handle
• Knit a simple R Markdown file

Task Lists

1. Attend the Tuesday session for course introduction and Q&A
2. Install/Update R and RStudio on your computer
3. Review the resources (lecture videos and slides)
4. Complete the assigned readings
   • Snijders & Bosker chapters 1 and 2 (they’re relatively short)
   • R Markdown Intro
   • Markdown Quick Reference
5. Introduce yourself on the #introduction Slack channel (as part of HW 1)
6. Attend the Thursday session and participate in the class exercise
7. Complete Homework 1

Lecture

Slides

You can view and download the slides here: HTML PDF

Multilevel Data Structure

Check your learning: How would you describe the data structure in the video?

observational educational hierarchical

Name and History

Check your learning: What is another name for multilevel modeling?

mixed-effect modeling
hierarchical regression
fixed-effect modeling

Usage of MLM

Another example that relates to ecological fallacies is the “happiness paradox” in economics, which says that income is related to happiness in a cross-sectional analysis (i.e., between-person level) but not in a longitudinal/time series analysis (i.e., within-person level).

Check your learning: In the data structure where there are multiple measurements for the same person, which level is level 1?

measurement person