Adjusting Measurement Bias in Sequential Mixed-Mode Surveys using re-interview data
| Autor: | Klausch, L.T., Schouten, J.G., Buelens, B, van den Brakel, J., Leerstoel Heijden, Methodology and statistics for the behavioural and social sciences |
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| Přispěvatelé: | QE Econometrics, RS: GSBE EFME, APH - Methodology, Epidemiology and Data Science |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Statistics and Probability
SELECTION Computer science Missing data Monte Carlo method Population Inference ADJUSTMENT 01 natural sciences Mixed-mode survey 010104 statistics & probability Measurement error Statistics REGRESSION 050602 political science & public administration Error adjustment IMPUTATION Imputation (statistics) 0101 mathematics Statistical theory education INCOMPLETE DATA POPULATION education.field_of_study SUBJECT Applied Mathematics 05 social sciences Estimator 0506 political science Errors-in-variables models Measurementerror Statistics Probability and Uncertainty Social Sciences (miscellaneous) Measurement bias |
| Zdroj: | Journal of Survey Statistics and Methodology, 5(4), 409-432. Oxford University JOURNAL OF SURVEY STATISTICS AND METHODOLOGY, 5(4), 409-432. Oxford University Press Klausch, T, Schouten, B, Buelens, B & Van Den Brakel, J 2017, ' ADJUSTING MEASUREMENT BIAS IN SEQUENTIAL MIXED-MODE SURVEYS USING RE-INTERVIEW DATA ', JOURNAL OF SURVEY STATISTICS AND METHODOLOGY, vol. 5, no. 4, pp. 409-432 . https://doi.org/10.1093/jssam/smx022 Journal of Survey Statistics and Methodology, 5(4), 409. Oxford University Press |
| ISSN: | 2325-0984 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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