Modern Techniques for Survey Sampling and Complex Data

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.

The team leaders are David Haziza, University of Montreal and Changbao Wu, University of Montreal. Collaborators include 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; and Puying Zhao, Yunnan University.

Comments are closed.