https://github.com/wibeasley/class-longitudinal-analysis-2016

Longitudinal Data Analysis Class, Summer 2016
https://github.com/wibeasley/class-longitudinal-analysis-2016

Last synced: 30 days ago
JSON representation

Longitudinal Data Analysis Class, Summer 2016

• Host: GitHub
• URL: https://github.com/wibeasley/class-longitudinal-analysis-2016
• Owner: wibeasley
• License: mit
• Created: 2016-06-19T01:00:59.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2016-06-19T01:14:18.000Z (over 8 years ago)
• Last Synced: 2024-12-18T01:21:35.904Z (about 1 month ago)
• Size: 2.93 KB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
Longitudinal Data Analysis Class, Summer 2016

=========================================

### Directed Reading, 9 weeks

### Instructor: William Beasley, Ph. D.

    Email: [email protected]

    Office Hours: Monday mornings

    

### Prerequisite

1. Knowledge of Multivariate statistics and matrix algebra

2. Working knowledge of R and Mplus

    

### Required Textbook

* Behavioral Research Data Analysis with R, by Yuelin Li, Jonathan Baron

### Recommended Textbooks

* Statistical Learning with Sparsity: The Lasso and Generalizations by Trevor Hastie and Robert Tibshirani

* Longitudinal Data Analysis, 2006 by Donald Hedekar and Robert D. Gibsons

### Software

* R and Mplus

### Course Description

This course will cover the modern statistical approaches to the analysis of longitudinal data, i.e., data collected repeatedly on experimental units over time (or other conditions). Topics include mixed effects models, ANOVA and MANOVA approaches to longitudinal data, covariance pattern models, generalized estimating equations, computational issues, and missing data in longitudinal studies

The main topics covered are:

1. Introduction to longitudinal data analysis

2. ANOVA and MANOVA approaches to longitudinal data analysis

4. Covariance pattern models

3. Mixed-Effects models for continuous, binary, ordinal, nominal, and count outcomes

4. Generalized Estimating Equations Models including GLM and GEE and time dependent covariates

5. Missing data in longitudinal studies

6. Cross-sectional and intra-individual strategies

7. Goals: Prediction vs theory building