Mode Effects in Practice |
|
| Coordinator 1 | Dr Richard Silverwood (University College London) |
| Coordinator 2 | Dr Liam Wright (University College London) |
Recent years have seen greater mixing of modes, both within and (in longitudinal surveys) between sweeps of data collection. Unaccounted for, mode effects – differential measurement errors due solely to the mode utilised – can be a problem for obtaining accurate and unbiased estimates. For instance, mode effects can induce associations between variables and changes in survey mode between sweeps may bias estimates of change. Investigating and addressing mode effects is therefore an increasingly important area of methodological research.
Mode effects in mixed mode survey data are typically confounded by mode selection. Many methods to account for mode effects – e.g. statistical adjustment and imputation – presuppose that mode selection can be controlled for with available data. This is difficult, however, as selection factors may be unmeasured (especially in cross-sectional studies), measured poorly, subject to mode effects themselves, or unknown to the applied researcher, as this typically lies outside their area of expertise. Determining the level of bias in practice and promoting methods that do not rely on assumptions about mode selection is of fundamental importance given the continued move towards mixed mode designs.
We invite submissions of research that investigates and addresses mode effects, with particular focus on the longitudinal survey setting. Novel methods for overcoming mode selection, including via integrating experimental work or using other non-standard analytic approaches, are of particular interest. We also invite contributions relating to user-friendly implementations of methods to handle mode effects and their communication to non-technical audiences.