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March 1, 2018

The Department of Epidemiology and Biostatistics will offer three new graduate level courses in the Summer 2018 semester. 

 

 

 

Analysis of National Health Survey Data (EPIB660)

Do you want to learn weighted analysis of national survey data for your thesis or dissertation? If so, this online course will meet your need. Students will learn to

  1. Understand sampling weights and use them in weighted descriptive analysis; and
  2. Use SAS® to perform weighted multiple regression analyses, including linear and logistic regression, and survival analysis.

The instructor, Dr. Taylor Lewis, will use his new book, Complex Survey Data Analysis with SAS, to teach this course and illustrate analytic techniques by using national survey data, such as NHANES, NHIS, and NIS. Please contact Dr. Lewis with questions: tlewis9@umd.edu

 

Applied Multilevel Modeling of Health Data (EPIB635)

Multilevel models are used when data are collected at different levels and observations are nested, such as children nested in schools, or patients nested within doctors, or hospitals. Students will learn to

  1. Use multilevel modeling to disentangle effects of factors at different levels on health outcomes
  2. Analyze interaction effects between factors at different levels.

Dr. Sirin Yaemsiri will use her experience in multilevel modeling to teach this class. SAS will be used to perform multilevel modeling techniques, including linear, logistic, Poisson regression models. Please contact Dr. Sirin with questions: siriny@umd.edu

Applied data analysis in social epidemiology and behavioral health (EPIB634)

This course focuses on the application of factor analysis, mediation analysis using path analytic model and structural equation model in social epidemiology and behavioral health. The course will teach how to blend behavioral change models with statistical models. Students will learn to develop understanding of these techniques and skills in data analysis.

Dr. Hongjie Liu will use real-world examples from his studies to illustrate different methods. SAS will be used to analyze data. Please contact Dr. Liu with questions: hliu1210@umd.edu

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Hongjie Liu