Inference in surveys with sequential mixed-mode data collection

Mixed-mode surveys are susceptible to mode-dependent selection effects and measurement errors, collectively known as mode effects. In sequential mixed-mode surveys, where non-respondents in one mode are re-approached using a different mode, it is likely that the mode composition of the response differs between subpopulations or between subsequent editions of the survey. Such variations in the mode composition lead to variations in the measurement errors, invalidating classical inference. An approach to inference in these circumstances is proposed, by calibrating the mode composition of the response to fixed levels. Assumptions and risks associated with such a procedure are discussed. The case of the Dutch Crime Survey is discussed as an example.

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