Making Inferences from Non-Probability Samples Through Data Integration

The International Association for Survey Statisticians invites you to participate in its webinar "Making inferences from non-probability samples through data integration" 

This event will be recorded, so if you do not wish to get up in the middle of the night to watch this webinar, please register and you will be provided with a link to the recording after the event.

For several decades, national statistical agencies around the world have been using probability surveys as their preferred tool to meet information needs about a population of interest. In the last few years, there has been a wind of change and other data sources are being increasingly explored. Five key factors are behind this trend: the decline in response rates in probability surveys, the high cost of data collection, the increased burden on respondents, the desire for access to “real-time” statistics, and the proliferation of non-probability data sources. In this presentation, the speaker, Jean-François Beaumont, reviews some data integration approaches that take advantage of both probability and non-probability data sources such as the dual frame weighting, calibration, statistical matching, inverse probability weighting and small area estimation. He discusses the characteristics of each approach, including their benefits and limitations, and present a few empirical results.

Speaker: Jean-François Beaumont

Jean-François Beaumont is Senior Statistical Advisor at Statistics Canada, where he has been working for over 25 years. He is currently the Editor of Survey Methodology. His recent research projects and applications have mainly focused on data integration and small area estimation.

Please use the following link to register for the webinar:

https://attendee.gotowebinar.com/register/5376013043902022414