Teaching Materials and Lectures
Courses
Current 2016-2017 Emory Nursing PhD Handbook
N741 Big Data Analytics for Healthcare
Instructor(s): Vicki Hertzberg, PhD & Melinda Higgins, PhD.
SPRING 2017 - This course teaches Big Data Analytics with applications in healthcare, including knowledge discovery, precision medicine/nursing, and the development of targeted interventions to improve health outcomes. Students will use R & RStudio software and Reproducible Research principles for their projects. This course is an elective to be taught approximately every other year.
N736 Quantitative Analysis of Clinical Research Data
Instructor: Melinda Higgins, PhD.
FALL 2017 - This course builds on the required statistical sequence (i.e. completion of BIOS 500 and 501 during the 1st year) and focuses on practical application of statistics including understanding clinical research questions. This course is typically taken in the Fall of the 2nd or 3rd year of the PhD track. It is an elective course and taught every other year. Analyzing data is the major emphasis of the course including examining if assumptions of the statistical analysis are being met and interpreting the findings. Course assignments focus on using SPSS to analyze data sets from actual clinical research studies and interpretation of output and literature.
Workshops on R, RStudio, GIT and Github
Materials for the three R Workshops given Fall 2016
Slides on How to get started with GIT and Github
Spring 2016 R Workshops "Book"
Online book of the materials compiled for the "Series of R Workshops" held Spring 2016 at Emory University - School of Nursing. This workshop series will have 4 workshops - each 2 hours long with a focus on hands-on learning using R and RStudio. These workshops are introductory and provide skills necessary to begin to be comfortable working with R and RStudio performing data analysis and writing research reports
Archived Materials
Research Rountables provided during 2007 - 2013 on topics including:
- Multicollinearity
- Sources of Variance
- Intro to Longitudinal Models
- Data Screening
- Building Regression Models
- Moderation and Mediation
- Power
- Multilevel Mixed Models
- Categorical Data
- Structural Equation Modeling
- More Longitudinal Models
- Demystification of Statistical Terms