Course Description

This course focuses on multilevel regression models. Multilevel models are used in studies where individuals are nested within communities and/or where individuals are measured repeatedly over time. The course emphasizes application of multilevel regression models in family/community research and introduces statistical modeling.

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Athena Title

Multilevel Modeling

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Prerequisite

HDFS 8830 or permission of department

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Semester Course Offered

Offered spring

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Grading System

A - F (Traditional)

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Student learning Outcomes

• Students will understand the conceptual application of multilevel regression analysis. 2. Students will learn proper use of multilevel regression models for their research studies where individuals are nested within communities and/or where individuals are measured repeatedly over time. 3. Students will learn proper interpretations and evaluations of multilevel models. 4. Students will become familiar with statistical software packages.
• Students will learn proper use of multilevel regression models for their research studies where individuals are nested within communities and/or where individuals are measured repeatedly over time.
• Students will learn proper interpretations and evaluations of multilevel models.
• Students become familiar with statistical software packages such as HLM, SAS, and Mplus.

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Topical Outline

• 1. Intra-class correlation, reliability coefficients, and within/between group relations
• 2. Alternative multilevel models
• 3. Random intercept and slope models in detail
• 4. Model comparisons and hypothesis testing
• 5. Centering (group mean and grand mean)
• 6. Design effect and power in multilevel models
• 7. Estimation
• 8. Growth curve models as multilevel models
• 9. Latent trajectory class analysis
• 10. Multilevel models with categorical outcomes

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Syllabus

Select Faculty / Instructor to download syllabus:   Ravindran, Niyantri

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Public CV

Select a file to download:   Ravindran, Niyantri

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