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Statistical Methods for Complex Survey Data

Project Type: 
complet

This project focuses on developing new tools to address issues which arise during the analysis of complex data including longitudinal data, information which is based on a set of repeated observations of an individual, or group of individuals, over time.

Project Leader(s): 

Dr. Changbao Wu, University of Waterloo

The surveys being developed by government, health and social science organizations have increased in complexity and as a result, the data that is collected is similarly more complicated. Thus, this project focuses on developing new tools to address issues which arise during the analysis of this complex data including longitudinal data, information which is based on a set of repeated observations of an individual, or group of individuals, over time. Research achievements over the past year include new estimation methods for analysis of survey data, where accurate estimates are required but the data is sparse and new methods for handling data when values of some survey variables are unavailable due to refusals or drop-out. For survey data collected in two-phases, new replication variance estimation methods are proposed. These will provide a systematic solution to a variety of situations which would ordinarily require specific development for each individual case.

Project team: 
Dr. Jiahua Chen, University of Waterloo
Dr. David Haziza, Université de Montréal
Dr. Jerry Lawless, University of Waterloo
Dr. Wilson Lu, Acadia University
Dr. Nancy Reid, University of Toronto
Dr. Jamie Stafford, University of Toronto
Dr. Brajendra Sutradhar, Memorial University of Newfoundland
Dr. Roland Thomas, Carleton University
Dr. Roland Thomas, Carleton University
Dr. Zilin Wang, Wilfrid Laurier University
Funding period: 
April 1, 2021 - March 31, 2021