Modern Techniques for Survey Sampling and Complex Data

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Collaborative Research Team Project: 2020-2023

This project’s goal is to modernize survey sampling methodology and official statistics by bridging the gap between modern statistical tools and classical survey sampling techniques. This is to address the problem that surfaces when researchers use convenient, uncontrolled big data sources which often fail to represent the target population of interest because of inherent selection biases. This research is important because 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.

Team leaders:
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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