In this session, participants will have the opportunity to ask questions and seek feedback on their qualitative analyses from facilitators and other attendees. This session serves as a workshop in which participants get a chance to discuss and review qualitative analysis techniques while also learning from others.

Please note: We will not be presenting information about qualitative analysis in this session; instead, we will ask participants to share where they are in their analysis process, ask questions about their analyses, and collectively discuss strategies for moving our analyses forward. This event will not be recorded.

Facilitators:
MaryBeth Grewe, MPH
Program Manager, Qualitative Research Service
Research Specialist, Patient and Community Engagement in Research (PaCER) Program

Simone Frank, MPH
Senior Project Manager, Patient and Community Engagement in Research (PaCER) Program
Research Specialist, Qualitative Research Service

Sharita Thomas, MPP
Research Specialist, Qualitative Research Service

For questions about this workshop, please contact MaryBeth Grewe at This email address is being protected from spambots. You need JavaScript enabled to view it..

Eligibility Screener

Join us for a laptop-optional workshop to learn more about statistical power, the relationship of statistical power to study design, and how to design a simulation study to assess power. Code samples will be provided in both R and Python.

In this workshop, you will learn how to:

• Define statistical power as a concept
• Understand how power calculations relate to study design
• Learn to design a simulation study to assess power

Workshop outline:

1. Welcome and introduction
    a. Define statistical power in the context of null hypothesis significance testing

2. Overview of power calculations
    a. Why calculate power?
    b. What impacts power?
    c. Why is power meaningful?

3. Introduction to simulation studies for power
    a. Precision
    b. Two-group comparison
    c. Three-group comparison
    d. Correlation

4. Conclusions/next steps
    a. Detecting and avoiding common mistakes

Prerequisites
You should be able to do these things in either R or Python: 

• Write, save, and execute code
• Make simple visualizations