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VIRTUAL EVENT: Handling non-response in COVID-19 surveys across five national longitudinal studies
09 December 2020, 1:00 pm–2:00 pm
In this webinar, Richard Silverwood discusses how to restore sample representativeness in the COVID-19 surveys across five national longitudinal studies.
This event is free.
Event Information
Open to
- All
Availability
- Yes
Cost
- Free
Organiser
-
Bozena Wielgoszewska
In response to the COVID-19 pandemic and subsequent national lockdown, the Centre for Longitudinal Studies and the MRC Unit for Lifelong Health and Ageing at UCL have carried out two online surveys. They surveyed the participants of five national longitudinal cohort studies which have collected insights into the lives of study participants.
Richard was a part of the team that have derived and made available non-response weights to enable users of the COVID-19 survey data to handle survey non-response by performing inverse probability weighted analyses.
In this talk, Richard will describe how these non-response weights were derived and demonstrate their ability to restore sample representativeness in the cohorts.
He will then discuss some alternative approaches for handling non-response (and missing data more generally), utilising multiple imputation, inverse probability weighting, and the combination of the two.
QSS seminar series
In this weekly Quantitative Social Science (QSS) seminar series, speakers present research that falls under the broad umbrella of quantitative social science.
Links
Image: Lum3n via Pexels
About the Speaker
Dr Richard Silverwood
Lecturer in Statistics at the Centre for Longitudinal Studies (CLS) at UCL Institute of Education (IOE)
Richard’s responsibilities at CLS include:
- undertaking and publishing research using quantitative methods in areas relevant to CLS studies
- making use of longitudinal datasets housed at CLS and other sources
- making a major contribution to the CLS Applied Statistical Methods and Survey Methods research programmes
- contributing to the work of maintaining, developing and promoting the CLS cohort studies
- advising colleagues at CLS on other statistical matters that they meet in their research.
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