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Viewing: EDUC 8174 : Hierarchical Linear Modeling

Last approved: Fri, 18 Mar 2016 17:41:34 GMT

Last edit: Wed, 30 Sep 2015 22:15:21 GMT

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Graduate School of Education and Human Development
Educational Leadership (EDUC)
Hierarchical Linear Modeling
Hierarchical Linear Modeling
Fall 2015
Course Type
Field Work (Internship)
Default Grading Method
Letter Grade

EDUC 8171


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Fee Type


Techniques appropriate for analyses of hierarchically structured data. Theoretical concepts of hierarchical linear models (HLM); social and behavioral research; popular HLM software such as HLMwin; and large scale datasets.
By the end of the course, participating students will: • have solid grounding in the theoretical concepts of hierarchical linear models • be exposed to “hands on” practical examples in social and behavioral research in HLM • have experiences of popular HLM software such as HLMwin • have experiences with several large scale datasets
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Multilevel Models (also hierarchical linear models [HLM], nested models, mixed models, random coefficient, random-effects models, random parameter models, or split-plot designs) are is the fastest-growing methodology in educational or organizational research. HLM is statistical models of parameters that vary at more than one level. These models can be seen as generalizations of linear models (in particular, linear regression), although they can also extend to non-linear models.
HLMs are particularly appropriate for research designs where data for participants are organized at more than one level (i.e., nested data). The units of analysis are usually individuals (at a lower level) who are nested within contextual/aggregate units (at a higher level). While the lowest level of data in multilevel models is usually an individual, repeated measurements of individuals may also be examined, i.e., Growth Modeling.
gharris (Fri, 25 Sep 2015 15:51:10 GMT): returned to dept. for assistance editing course description into bulletin style as no instructor is indicated.
Key: 9839