330.614.01
Advanced Latent Variable Modeling: Matching Model To Question
Location
East Baltimore
Term
4th Term
Department
Mental Health
Credit(s)
3
Academic Year
2019 - 2020
Instruction Method
TBD
Tu, Th, 10:30 - 11:50am
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
No
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Contact Name
Rashelle Musci
Contact Email
Frequency Schedule
Every Year
Resources
Prerequisite
Latent variable methods are commonly used in psychological and mental health research but require in-depth understanding of both the theoretical framework and the real-life applications of such methods. This course will explore a number of advanced theoretical models that have applications in longitudinal and cross-sectional datasets. At the completion, students will be able to understand, apply, and interpret the current state of the science methods within the latent variable methodology field.
Reviews concepts, key assumptions, and published applications of advanced latent variable methods commonly used in psychology or mental health research including growth mixture models, latent class analysis with covariates and distal outcomes, and latent transition analysis. Acquaints students with the current state of science related to latent variable methods, which is a quickly advancing field, and gives students the tools they need to build an appropriate latent model for their research question. Topics include growth mixture modeling, latent class regression, latent transition analysis, multi-level models, and measurement invariance. Presents students with examples from psychological, mental health, and developmental datasets with applications in the behavioral and social sciences. Students will apply lessons from didactic lectures in assignments and class projects.
Learning Objectives
Upon successfully completing this course, students will be able to:
- Critically evaluate the use of advanced latent variable models in studies related to mental health, psychology, epidemiology, etc.
- Conduct latent class analysis, including the use of latent class regression and latent class analysis with distal outcomes within a single and multilevel framework
- Analyze and interpret growth mixture models with complex data
- Analyze and interpret latent transition analyses with covariates
- Write and present a methods and results section with complex latent variable modeling
Methods of Assessment
This course is evaluated as follows:
- 20% Homework
- 10% Midterm Paper
- 30% Final Project
- 10% Final Presentation
- 30% Discussion Board
Jointly Offered With