Multilevel Modeling

This course covers the statistical techniques used to analyze data with a hierarchical or nested structure. This can include data from surveys where students are nested within schools or experiments where different observations are nested within subjects. The class will cover the theoretical foundations of multilevel models. Students will learn the distinction between fixed and random effects. Students will learn how to fit multilevel models using statistical software and how to interpret the results. This course will also cover advanced topics such as model selection, missing data handling, and dyadic data analysis.


The articles written in English have blue links or blue table cells. The articles written in Thai have red links or red table cells.

Fall 2021 (Intro to Multilevel Modeling; Undergraduate, CU)

Lectures Materials Assignments
Multiple Regression (1)
Multiple Regression (2)
Random Intercept Model
Random Slope Model
Cross-level Interaction
Effect Size
Model Building
Multilevel Latent Covariate Model
Growth Curve Model (1)
Growth Curve Model (2)
Three-level Model
Missing Data
Dyadic Data Analysis

Lecture Material Archive

Topics Materials
Interpreting Regression Models and Basic Multilevel Models PowerPoint (2013).
Interpreting Multilevel Models with Centering PowerPoint (2013).
Interpreting Interactions in Multilevel Models PowerPoint (2013).
Interpreting Multilevel Models for Longitudinal Data PowerPoint (2013).
Using R for Multilevel Models Paper (2013).
Data set (2013).
R script (2013).
Supplemental Material for Interpreting Multivariate Growth Curve Model (2013).