Handling bias in analysis of mixed-mode survey data
Working with mixed-mode survey data? Join this free webinar to understand the challenges of using mixed-mode survey data and learn statistical methods to handle these in practice.
Handling bias in analysis of mixed-mode survey data
30 Apr 2026, 12:00 – 14:00
Surveys are increasingly moving to mixed-mode data collection – such as carrying out interviews via face-to-face, telephone, video and/or web modes. In this webinar, we will give an overview of issues that arise when using data collected in mixed-mode surveys. This includes the bias introduced when participants respond differently to survey items depending on the survey mode used – termed “mode effects”.
In this webinar, run by CLS (UCL Centre for Longitudinal Studies) and Survey Futures, we will conceptualise the bias from mode effects within a simple and intuitive empirical framework called Causal Directed Acyclic Graph (DAG). We will then describe statistical methods for handling mode effects, looking in particular at Quantitative Bias Analysis (QBA).
This webinar will be useful for users or managers of mixed-mode survey data, including users of CLS cohort data.
Speakers
- Richard Silverwood, Associate Professor and Chief Statistician at CLS
- Georgia Tomova, Research Fellow (Statistics/Quantitative Social Science) at CLS
- Liam Wright, Lecturer in Applied Statistical Methodology
Further information
Ticketing
Pre-booking essential
Cost
Free
Open to
All
Availability
Yes
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