Measurement Error Calibration in Mixed-mode Sample Surveys

Abstract

Mixed-mode surveys are known to be susceptible to mode-dependent selection and measurement effects, collectively referred to as mode effects. The use of different data collection modes within the same survey may reduce selectivity of the overall response but is characterized by measurement errors differing across modes. Inference in sample surveys generally proceeds by correcting for selectivity—for example, by applying calibration estimators—and ignoring measurement error. When a survey is conducted repeatedly, such inferences are valid only if the measurement error remains constant between surveys. In sequential mixed-mode surveys, it is likely that the mode composition of the overall response differs between subsequent editions of the survey, leading to variations in the total measurement error and invalidating classical inferences. An approach to inference in these circumstances, which is based on calibrating the mode composition of the respondents toward fixed levels, is proposed. Assumptions and risks are discussed and explored in a simulation and applied to the Dutch crime victimization survey.