<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The mixed-mode (MM) designs are adopted by NSIs both to contrast declining response and coverage rates and to reduce the cost of the surveys. However, MM introduces several issues that must be addressed both at the design phase, by defining the best collection instruments to contain the measurement error, and at the estimation phase, by assessing and adjusting the mode effect. In the MM surveys, the mode effect refers to the introduction of bias effects on the estimate of the parameters of interest due to the difference in the selection and measurement errors specific to each mode. The switching of a survey from single to mixed-mode is a delicate operation: the accuracy of the estimates must be ensured in order to preserve their consistency and comparability over time. This work focuses on the methods chosen for the evaluation of the mode effect in the Italian National Institute of Statistics (ISTAT) mixed-mode survey “Aspects of Daily Life – 2017”, in the experimental context for which an independent control single-mode (SM) PAPI sample was planned to assess the introduction of the sequential web/PAPI survey. The presented methods aim to analyze the causes that can determine significant differences in the estimates obtained with the SM and MM surveys.</p></abstract>

The mixed-mode (MM) designs are adopted by NSIs both to contrast declining response and coverage rates and to reduce the cost of the surveys. However, MM introduces several issues that must be addressed both at the design phase, by defining the best collection instruments to contain the measurement error, and at the estimation phase, by assessing and adjusting the mode effect. In the MM surveys, the mode effect refers to the introduction of bias effects on the estimate of the parameters of interest due to the difference in the selection and measurement errors specific to each mode. The switching of a survey from single to mixed-mode is a delicate operation: the accuracy of the estimates must be ensured in order to preserve their consistency and comparability over time. This work focuses on the methods chosen for the evaluation of the mode effect in the Italian National Institute of Statistics (ISTAT) mixed-mode survey “Aspects of Daily Life – 2017”, in the experimental context for which an independent control single-mode (SM) PAPI sample was planned to assess the introduction of the sequential web/PAPI survey. The presented methods aim to analyze the causes that can determine significant differences in the estimates obtained with the SM and MM surveys.

Assessing and Adjusting Bias Due to Mixed-Mode in Aspect of Daily Life Survey

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Assessing and Adjusting Bias Due to Mixed-Mode in Aspect of Daily Life Survey"}

The mixed-mode (MM) designs are adopted by NSIs both to contrast declining response and coverage rates and to reduce the cost of the surveys. However, MM...

Assessing and Adjusting Bias Due to Mixed-Mode in Aspect of Daily Life Survey

Publicado en línea: 22 jun 2021

Páginas: 461 - 480

Recibido: 01 jun 2019

Aceptado: 01 oct 2020

DOI: https://doi.org/10.2478/jos-2021-0020

Palabras clave
Mixed-mode, mode effect, -indicator, instrumental variable approach, propensity score

© 2021 Claudia de Vitiis et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Assessing and Adjusting Bias Due to Mixed-Mode in Aspect of Daily Life Survey

Publicado en línea: 22 jun 2021

Páginas: 461 - 480

Recibido: 01 jun 2019

Aceptado: 01 oct 2020

DOI: https://doi.org/10.2478/jos-2021-0020

Palabras claveMixed-mode, mode effect, -indicator, instrumental variable approach, propensity score

© 2021 Claudia de Vitiis et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Palabras clave
Mixed-mode, mode effect, -indicator, instrumental variable approach, propensity score