Hierarchical Linear Models
QAC 312
Fall 2020
| Section:
01
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| Course Cluster and Certificates: Applied Data Science Certificate |
Research questions cannot always be explored by collecting data with independent observations. Sometimes this is due to limitations or constraints on the data collection method, and other times our questions pertain to data that are measured at both the individual and group levels (e.g., patients from different hospitals or students from different schools that belong to different districts). Hierarchical linear models (HLM), also called multi-level or mixed models, explicitly model such nested data structures and address analytical and estimation issues not accounted within the framework of the classical linear model. Using data sets from different fields of study (e.g., education, medicine, and health) students will learn to formulate multilevel research questions, estimate and critically examine HLM applications. |
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| Credit: .5 |
Gen Ed Area Dept:
NSM QAC, SBS QAC |
| Course Format: Laboratory Course | Grading Mode: Student Option |
| 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)(IDEA-COMP)(PSYC) |
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Past Enrollment Probability: 90% or above |
| SECTION 01 - 1st Quarter | | Special Attributes: CQC |
Major Readings: Wesleyan RJ Julia Bookstore
No required textbook. Journal articles and online material
Some references from:
Raudenbush, S.W., & Bryk, A.S., (2002). HIERARCHICAL LINEAR MODELS: APPLICATIONS AND DATA ANALYSIS METHODS. Newbury Park, CA: Sage. 2nd edition
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Examinations and Assignments:
Several homework assignments and a final presentation linked to the course project. Part of the grade will depend on class preparation and participation. |
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.
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| Instructor(s): Rose,Jennifer S. Times: ..T.R.. 08:50AM-10:10AM; Location: ONLINE; |
| Total Enrollment Limit: 16 | | SR major: 0 | JR major: 0 | | |
| Seats Available: 5 | 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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