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Course Catalog

330.614.01 Advanced Latent Variable Modeling: Matching Model to Question

Department:
Mental Health
Term:
4th term
Credits:
3 credits
Academic Year:
2019 - 2020
Location:
East Baltimore
Class Times:
  • Tu Th,  10:30 - 11:50am
Auditors Allowed:
Yes, with instructor consent
Grading Restriction:
Letter Grade or Pass/Fail
Contact:
Rashelle Musci
Course Instructor:
Resources:
Description:

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:

  1. Critically evaluate the use of advanced latent variable models in studies related to mental health, psychology, epidemiology, etc.
  2. Conduct latent class analysis, including the use of latent class regression and latent class analysis with distal outcomes within a single and multilevel framework
  3. Analyze and interpret growth mixture models with complex data
  4. Analyze and interpret latent transition analyses with covariates
  5. Write and present a methods and results section with complex latent variable modeling
Methods of Assessment:

Homework assignments (25%); Final data analysis project (35%); Class participation (40%)

Instructor Consent:

No consent required

Jointly Offered With: