How can different modes of survey data collection introduce bias?
Introducing directed acyclic graphs (DAGs) to understand the implications of survey mode effects and mode selection
Survey data are self-reported data collected directly from respondents by a questionnaire or an interview, and are commonly used in health research. Such data are traditionally collected via a single mode (e.g. face-to-face interview alone), but use of mixed-mode designs (e.g. offering face-to-face interview or online survey) has become more common. This introduces two key challenges.
First, individuals may respond differently to the same question depending on the mode; these differences are due to measurement are known as 'mode effects'. Second, different types of individuals may participate via different modes; these differences in respondent composition between modes are known as 'mode selection'.
Where recognised, mode effects are often handled by straightforward approaches such as conditioning on survey mode. However, while reducing mode effects, this risks introducing collider bias whenever mode selection is present. The existence of mode effects and the consequences of naive conditioning may be underappreciated in epidemiology.
This talk will offer a simple introduction to these challenges in using directed acyclic graphs by exploring a range of possible data structures, and will outline the benefits and drawbacks of different approaches to dealing with mode effects in analyses of mixed-mode data.
Speaker
- Georgia Tomova, Research Fellow, University College London (UCL)
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