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Hierarchical Linear Models

QAC 312
Fall 2017
Section: 01  
Certificates: Applied Data Science

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.
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: (DATA-MN)
Past Enrollment Probability: 90% or above

Last Updated on SEP-24-2017
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