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

Selection Bias

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

Project Team

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


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.