Latent Variable Analysis
QAC 313
Fall 2022
| Section:
01
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| Course Cluster and Certificates: Applied Data Science Certificate |
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. |
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| Credit: .5 |
Gen Ed Area Dept:
NSM QAC, SBS QAC |
| Course Format: Laboratory Course | Grading Mode: Graded |
| Level: UGRD |
Prerequisites: [QAC201 or GOVT201 or PSYC280 or NS&B280] OR QAC380 OR ECON300 OR [GOVT367 or QAC302] OR PSYC200 |
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Fulfills a Requirement for: (CADS)(DATA-MN)(PSYC) |
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Past Enrollment Probability: 90% or above |
| SECTION 01 - 2nd Quarter | | Special Attributes: CQC |
Major Readings: Wesleyan RJ Julia Bookstore
No textbook will be required. Instead, students will be asked to read published papers covering tutorials, issues, and applications of structural equation modeling.
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Examinations and Assignments:
Each student will be asked to complete and present a project using real data sources, for which they will be expected to develop, test, evaluate, and interpret a structural equation model. |
Additional Requirements and/or Comments:
An introductory statistics/data analysis background is a prerequisite for the course and that is why QAC201, or 380, or ECON 300, or GOVT367, or PSYC 200 etc. are listed as formal prerequisites. Pre-req overrides will be approved by the Professor for students who satisfy this basic requirements through other course work. The course includes a strong lab component and programming with a statistical analysis software (e.g. SAS, or Stata, or R) is a significant part of the course work.
PLEASE NOTE::
1. The course meets the first half of the semester while QAC 313 meets during the second half of the term.
2. Only graded courses can satisfy the requirements for the data analysis minor and the applied data science certificate. Courses completed with a CR/U grading mode will not satisfy the requirements of the two programs. |
| Instructor(s): Rose,Jennifer S. Times: .M.W... 08:20AM-09:40AM; Location: ALLB204; |
| Total Enrollment Limit: 16 | | SR major: 0 | JR major: 0 | | |
| Seats Available: 13 | GRAD: 1 | SR non-major: 6 | JR non-major: 6 | SO: 3 | FR: 0 |
| Drop/Add Enrollment Requests | | | | | |
| Total Submitted Requests: 0 | 1st Ranked: 0 | 2nd Ranked: 0 | 3rd Ranked: 0 | 4th Ranked: 0 | Unranked: 0 |
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