Search

In mixed-mode surveys, mode differences in measurement bias, also called measurement effects or mode effects, continue to pose a problem to survey practitioners. In this paper, we discuss statistical adjustment of measurement bias to the level of a measurement benchmark mode in the context of inference from mixed-mode data. Doing so requires auxiliary information, which we suggest collecting in a re-interview administered to a sub-set of respondents to the first stage of a sequential mixed-mode survey. In the re-interview, relevant questions from the main survey are repeated. After introducing the design and presenting relevant statistical theory, this paper evaluates by Monte Carlo simulation the performance of six candidate estimators that exploit re-interview information. In the simulation parameters are systematically varied that define the size and type of measurement and selection effects between modes in the mixedmode design. Our results indicate that the performance of the estimators strongly depends on the true measurement error model. However, one estimator, called the inverse regression estimator, performs particularly well under all considered scenarios. Our results suggest that the re-interview method is a useful approach to adjust measurement effects in the presence of non-ignorable selectivity between modes in mixed-mode data.

In mixed-mode surveys, mode differences in measurement bias, also called measurement effects or mode effects, continue to pose a problem to survey practitioners. In this paper, we discuss statistical adjustment of measurement bias to the level of a measurement benchmark mode in the context of inference from mixed-mode data. Doing so requires auxiliary information, which we suggest collecting in a re-interview administered to a sub-set of respondents to the first stage of a sequential mixed-mode survey. In the re-interview, relevant questions from the main survey are repeated. After introducing the design and presenting relevant statistical theory, this paper evaluates by Monte Carlo simulation the performance of six candidate estimators that exploit re-interview information. In the simulation parameters are systematically varied that define the size and type of measurement and selection effects between modes in the mixed-mode design. Our results indicate that the performance of the estimators strongly depends on the true measurement error model. However, one estimator, called the inverse regression estimator, performs particularly well under all considered scenarios. Our results suggest that the re-interview method is a useful approach to adjust measurement effects in the presence of non-ignorable selectivity between modes in mixed-mode data.