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Complex surveys play an important role in providing information for policy makers and the general public as well as many scientific in other areas, such as public health and social science research. How to best handle missing data in surveys has been one of the focal points of research in the past three decades, with the ultimate prize in achieving reliable and efficient use of information from complex surveys. Essentially, survey statisticians distinguish unit nonresponse from item nonresponse. Unit nonresponse occurs, for example, when the sample unit is not-at-home or refuses to participate in the survey. Item nonresponse occurs, for example, because the sample unit may refuse to respond to sensitive items, may not know the answer to some items, or because of edit failures. Generally, weight adjustment procedures are used to compensate for unit nonresponse, whereas (single or multiple) imputation is used to treat item nonresponse.

The objective of this workshop is to take stock of new developments in the field of missing survey data, to bring together some of the most active researchers in the field, and to identify the current challenges. The overall goal is to gain a collective view of recent advances in this field, to generate new ideas and training opportunities, as well as to foster interaction between members of the collaborative research team on the CANSSI-funded project "Statistical Inference for Complex Surveys with Missing Observations".