- Location
- HS 208
- Series/Type
- DLSPH Event, Student Event
- Format
- In-Person
- Dates
- March 31, 2025 from 1:00pm to 2:00pm
Links
Abstract
Respondent-driven sampling (RDS) is a data collection and analysis technique that has been gaining popularity over the last few years. As this technique leverages existing social networks to gain access hidden populations (e.g., Indigenous Peoples, people who experience homelessness, and people who inject drugs) and applies weighting to account for unequal sampling probabilities and homophily, we are able to obtain a much more accurate picture of target outcomes in these populations than traditional sampling techniques. In this workshop, we will compare RDS to traditional sampling strategies and examine what the data collection process looks like as well as how to analyze the collected data.
Speaker Bio
Octavia Wong is a Post-Doctoral Research Fellow at the Data Science Institute, University of Toronto, under the co-supervision of Professor Aya Mitani and Dr. Janet Smylie. She completed her PhD in quantitative methods in Kinesiology at York University, developing a novel meta-analysis technique for RDS data and examining health outcomes among First Nations, Inuit, and Métis (FNIM) populations living in Ontario prioritized by the Indigenous community partners she collaborated with. She is currently developing a longitudinal data analysis method for RDS data and examining patterns of emergency room admissions among FNIM in Toronto in collaboration with the research co-lead Seventh Generation Midwives Toronto.
