Course Description
Structure of hierarchical data, random intercepts, individual change/growth models, applications in meta-analysis, assessing hierarchical models, hierarchical generalized linear models, hierarchical models for latent variables, cross-classified random effects, estimation. Prerequisite: ERM 782 or STA 671 or permission of instructor.
Syllabus
Student Learning Outcomes, Goals, Objectives:
Students successfully completing this course will be able to:
- Understand hierarchical data structures.
- Explain the purpose of hierarchical data analyses.
- Describe the relevant statistical theories (e.g., assumptions, limitations).
- Use statistical software to conduct hierarchical data analysis.
- Interpret the results.
Course Grading Information:
| Activity/Performance Measure | Percentage/Points |
|---|---|
| Assignment 1: Four homework assignments | 70% |
| Assignment 2: Final project | 30% |
Attendance/Participation:
Attendance/participation is not a graded component included in the student's final grade for this course.
Course Materials Purchased by the Students:
Multilevel Analysis — Snijders, T. A. B. & Bosker R. J., 2nd edition, SAGE, 2012, ISBN 9781849202015
Scholarly Perspectives
This course engages diverse scholarly perspectives to develop critical thinking, analysis, and debate and inclusion of a reading does not imply endorsement.