Recent years have seen the transition to multi-mode data collection with a focus on encouraging web and mobile participation. This trend has been accelerated by the global pandemic. One of the big unknowns in this transition is regarding the quality and comparability of measurement across modes.
Differences in measurement due to the mode of data collection can bias results both in cross-sectional and longitudinal data. The issue can be especially problematic in repeated cross-sectional data and panel studies where trends in time can be biased by the change in the mode of measurement.
In this talk, Alexandru Cernat (University of Manchester) will present recent evidence regarding the impact of change of interview modes on measurement in a number of large surveys such as the European Social Survey, European Value Study,
Understanding Society and the Next Steps cohort study. He will also discuss the issue of estimating change in the context of multi-mode design, an essential and often ignored topic.
About the Speaker
Alexandru Cernat is an associate professor in the social statistics department at the University of Manchester. He has a PhD in survey methodology from the University of Essex and was a post-doc at the National Centre for Research Methods and the Cathie Marsh Institute.
His research and teaching focus on: survey methodology, longitudinal data, measurement error, latent variable modelling and new forms of data.
You can find out more about Alexandru and his research on his website.
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