Handling Mode Effects in Analyses of Mixed-Mode Survey Data

Handling Mode Effects in Analyses of Mixed-Mode Survey Data

Surveys are increasingly adopting mixed-mode methodologies. Due to differences in how items are presented, responses can differ systematically between modes, a phenomenon referred to as a mode effect. Unaccounted for, mode effects can introduce bias in analyses of mixed-mode survey data. Several methods for handling mode effects have been developed but these have mainly appeared in the technical literature and vary in their ease of implementation. Further, the assumptions these methods make (typically, no unmodelled selection into mode) can be implausible.

To improve adoption of methods for handling mode effects, in this interactive workshop we will provide background on the problem of mode effects by placing it within a simple and intuitive Causal Directed Acyclic Graphs (DAGs) framework. Using this framework, we will then describe the main methods for handling mode effects and introduce a promising but underutilised approach, sensitivity analysis, which uses simulation and does not assume no unmodelled selection into mode. Time-permitting, we will show users how to implement sensitivity analysis with a hands-on R tutorial using real-world data; code will be shared in any case.

By the end of the workshop attendees will:

• Understand why mode effects can cause bias in analyses of mixed-mode data.
• Use DAGs to design an analysis of mixed-mode data and to identify the biases that may appear in such an analysis.
• Understand methods for handling mode effects, including sensitivity analysis.

The workshop is intended for anyone who produces or wishes to analyse mixed-mode survey data. It will be delivered by Liam Wright, Georgia Tomova and Richard Silverwood of the Centre for Longitudinal Studies, University College London. The workshop is part of the Economic and Social Research Council's initiative.

Questions about the event can be directed to Liam Wright (bGlhbSAhIHdyaWdodCB8IHVjbCAhIGFjICEgdWs=).

The location for the event is: Room 777, UCL Institute of Education, 20 Bedford Way, WC1H 0AL (Google Maps)

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