Courses Taught by Sara Adar

EPID503: Strategies and Uses of Epidemiology

  • Graduate level
  • Winter term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Needham, Belinda; Kardia, Sharon; Adar, Sara; Karvonen-Gutierrez, Carrie;
  • Last offered Winter 2018
  • Prerequisites: Biostat 501 or Biostat 521, and Graduate Status
  • Description: This course offers an introduction to the principles, concepts, and methods of population-based epidemiologic research. It is intended to be the introductory course for students who are NOT majoring in Epidemiology. The course is divided into three primary sections: introduction to the basic principles of epidemiology and the measures used in epidemiology; epidemiologic study design and analysis;special topics that are important to an introductory understanding of epidemiology.
  • Syllabus for EPID503

EPID640: SAS for Epidemiological Research

  • Graduate level
  • Fall term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Adar, Sara
  • Last offered Fall 2015
  • Prerequisites: BIOSTAT 503 or 553
  • Description: This course teaches the fundamentals of data management, processing, manipulation, and critical review of data in SAS for epidemiologic and statistical analysis.
  • Course Goals: As a hands-on class, this course aims to teach the basics of SAS in addition to sharpening student's intuition about how to use, manipulate, review, interpret, and judge others' claims about data.
  • Competencies: 3.H. Computer Packages in Data Analysis Skill 1.Use of computer packages for data entry and data analysis, to include spreadsheets, SAS, SPSS, STATA, and Epi Info. 3.J. Data Management Knowledge 1.Different types of data (qualitative and quantitative), the scale used to measure the data (nominal, ordinal, interval, and ratio scales), and how the scale used relates to data coding, data entry, and generating a codebook. 2.Standard practices for data coding, data entry, generating codebooks for an epidemiological dataset, data verification, cleaning, and editing.
  • Learning Objectives: By the end of this course, students should be able to read in raw data, merge files, recode existing variables, create new parameters, critically review data for errors, create graphics to understand data, construct datasets for statistical analysis, and interpret simple statistical output in SAS.