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CS92PROD
Latent Variable Analysis


QAC 313
Spring 2020 not offered
Certificates: Applied Data Science
Course Cluster: Data Analysis Minor

The course is an introduction to latent variable modeling. Students will learn the fundamental statistical methods for structural equation modeling (SEM), including principal component analysis, confirmatory factor analysis, path analysis, and SEM for both quantitative and binary observed variables. In addition, students will learn the basic components of SEM, such as assumptions, testing model fit and indices of fit, testing competing models, estimation methods, and issues in model identification. Students will learn to develop structural equation models using AMOS, R, and/or Mplus statistical software.
Credit: .5 Gen Ed Area Dept: NSM QAC, SBS QAC
Course Format: Laboratory CourseGrading Mode: Graded
Level: UGRD Prerequisites: [QAC201 or SOC257 or GOVT201 or PSYC280 or NS&B280] OR [QAC380 or PSYC395] OR ECON300 OR [GOVT367 or QAC302] OR PSYC200
Fulfills a Major Requirement for: (CADS)(DATA-MN)

Last Updated on JUL-20-2019
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