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.

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

Spring 2024 (Intro to Multilevel Modeling; Undergraduate, CU)

Lectures

Materials

Assignments

Introduction

Multiple Regression: Basics

Multiple Regression: Interactions

Random Intercept Model

Random Slope Model

Cross-level Interaction

Centering

Effect Size

Model Building

Multilevel Latent Covariate Model

Growth Curve Model

Complex Growth Curve Model

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).