Collaborative Research Team Project #15
Modern Techniques for Survey Sampling and Complex Data
This project explores modern survey sampling using complex data to avoid selection biases.
Research Category: Humanities & Social Science
Why Study Survey Sampling?
Canada’s population is widely diverse, and survey data needs to be representative of our population. If industry, government and academia use biased data to make data-driven decisions, it can negatively impact Canadians in critical and sometimes unforeseen ways.
Modern survey sampling reduces selection biases, better representing the target population of interest. It prevents researchers from using convenient, uncontrolled big data sources.
Areas of Exploration
Includes examining the implications of selection bias in survey sampling.
Classical Sampling Techniques
Includes reviewing classical survey sampling techniques and identifying potential entry points for selection biases.
Modern Survey Sampling
Includes bridging the gap between classical methods and modern statistical tools to better control big data.
Solving Global Challenges
Research Team’s Goal
To improve modern survey sampling and reduce selection biases in large data sources.
People Behind the Project
David Haziza – University of Montreal
Changbao Wu – University of Waterloo
Jean-François Beaumont – Statistics Canada
Audrey Béliveau – University of Waterloo
Song Cai – Carleton University
Jiahua Chen – University of British Columbia
Sixia Chen – University of Oklahoma
Camelia Goga – Université de France-Comté
Jae-Kwang Kim – Iowa State University
Zilin Wang – Wilfrid Laurier University
Puying Zhao – Yunnan University
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Modern Techniques for Survey Sampling and Complex Data is a Collaborative Research Team project. This program tackles complex problems through a three-year research and training agenda.
CANSSI offers approximately $200,000 for this type of project, which requires a team of faculty, postdocs, and students.