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

Claudia de Vitiis (), Guandalini Alessio (), Inglese Francesca () and Terribili Marco D. ()
Additional contact information
Claudia de Vitiis: Italian National Institute of Statistics (ISTAT), Via Cesare Balbo 16, Rome, 00184, Italy.
Guandalini Alessio: Italian National Institute of Statistics (ISTAT), Via Cesare Balbo 16, Rome, 00184, Italy.
Inglese Francesca: Italian National Institute of Statistics (ISTAT), Via Cesare Balbo 16, Rome, 00184, Italy.
Terribili Marco D.: Italian National Institute of Statistics (ISTAT), Via Cesare Balbo 16, Rome, 00184, Italy.

Journal of Official Statistics, 2021, vol. 37, issue 2, 461-480

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.

Keywords: Mixed-mode; mode effect; R-indicator; instrumental variable approach; propensity score (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2478/jos-2021-0020 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:37:y:2021:i:2:p:461-480:n:12

DOI: 10.2478/jos-2021-0020

Access Statistics for this article

Journal of Official Statistics is currently edited by Annica Isaksson and Ingegerd Jansson

More articles in Journal of Official Statistics from Sciendo
Bibliographic data for series maintained by Peter Golla ().