• Undergraduate Course Work from Public Health 490KR Epidemiologic Data Management & Analysis, Taught by Professor Katherine W. Reeves, PhD, MPH

• Course taken Spring Semester 2018

• Students will develop fundamental skills in epidemiologic research, including data management, data analysis, and interpretation of results

• A major emphasis of the course will be to provide students with practical experience in analyzing research datasets using both Stata and SAS

  Course Objectives: At the end of this course, students will be able to:

  - 1) Plan, organize, and document data management and analysis tasks to obtain reproducible results
  
   - 2) Appropriately clean and label epidemiologic datasets using Stata and SAS
  
   - 3) Calculate basic descriptive statistics using Stata and SAS
  
   - 4) Calculate relative risks and odds ratios using Stata and SAS
  

  • CEPH Outcome Competencies: This course educates students in the following Council for Education in Public Health (CEPH) outcome competencies:
  • Analyze data using appropriate epidemiologic and biostatistical techniques; Use computer software for data processing and statistical analyses

• Required Texts

  • Acock, AC. A Gentle Introduction to Stata. (4 ed). StataCorp LP, 2014.
  • Delwiche, L. The Little SAS Book: A Primer. (5th ed). SAS Institute Inc. 2012.

COURSE SCHEDULE

Introduction to Epidemiologic Data Management & Analysis 
- Data Management 1: Survey Design
- Data Management 2: Creating and Documenting Datasets

Module 1: Programming in Stata

Introduction to Stata; Organizing and Documenting Your Work
  - HW 1 Assigned
Stata Data Cleaning 1: Labeling Variables and Their Values 
Stata Data Cleaning 2: Identifying and Addressing Errors
Stata Data Cleaning 3: Creating New Variables
  - HW 2 Assigned
Stata Data Cleaning 4: Creating New Variables (continued) 
Stata Work Session 1
Stata Data Analysis 1: Descriptive Statistics
  - HW 3 Assigned
Stata Data Analysis 2: Odds Ratios & Relative Risk
  - Exam 1

Module 2: Programming in SAS

Introduction to SAS
SAS Data Cleaning 1: Labeling Variables and Their Values
  - HW 4 Assigned
SAS Data Cleaning 2: Identifying and Addressing Errors
SAS Data Cleaning 3: Creating New Variables
SAS Work Session 1
  - HW 5 Assigned
SAS Data Analysis 1: Descriptive Statistics
SAS Data Analysis 2: Odds Ratios & Relative Risk
SAS Work Session 2
  - Exam 2

Group Project Presentations