Hierarchical Linear Models|
Spring 2019 not offered
|Certificates: Applied Data Science|
|Course Cluster: Data Analysis Minor|
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.
||Gen Ed Area Dept:
NSM QAC, SBS QAC|
|Course Format: Laboratory Course||Grading Mode: Graded|
||Prerequisites: [QAC201 or GOVT201 or PSYC280 or NS&B280] OR QAC380 OR ECON300 OR [GOVT367 or QAC302] OR PSYC200
||Fulfills a Major Requirement for: (CADS)(DATA-MN)(IDEA-COMP)
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
|Examinations and Assignments: |
Several homework assignments and a take-home final exam 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 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.
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