26 January 2022 at 2:00 PM CET (please check you own time zone)
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, I review 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. I discuss the characteristics of each approach, including their benefits and limitations, and present a few empirical results.
Speaker: Jean-François Beaumont