Non-response bias has long been a concern for surveys, even more so over the past decades with the increasing decline of the response rates. A similar problem concerns the surveys based on non-representative samples, the convenience and costeffectiveness of which has increased with the recent technological innovations that allow for collecting large numbers of highly non-representative samples via online surveys. In both cases it must be assumed that the bias is the result of a self-selection process and, for both, quality indicators are needed to measure the impact of this process. The goal of this research is to show the opportunity in each survey of monitoring the risk of self-selection bias at two different level: at the level of the whole survey and at the level of each statistic of interest. The combined use of two indicators is suggested and empirically evaluated under various scenarios.

Indicators for monitoring the survey data quality when non-response or a convenience sample occurs / Emilia Rocco. - ELETTRONICO. - (2019), pp. 233-245.

Indicators for monitoring the survey data quality when non-response or a convenience sample occurs

Emilia Rocco
2019

Abstract

Non-response bias has long been a concern for surveys, even more so over the past decades with the increasing decline of the response rates. A similar problem concerns the surveys based on non-representative samples, the convenience and costeffectiveness of which has increased with the recent technological innovations that allow for collecting large numbers of highly non-representative samples via online surveys. In both cases it must be assumed that the bias is the result of a self-selection process and, for both, quality indicators are needed to measure the impact of this process. The goal of this research is to show the opportunity in each survey of monitoring the risk of self-selection bias at two different level: at the level of the whole survey and at the level of each statistic of interest. The combined use of two indicators is suggested and empirically evaluated under various scenarios.
2019
978-3-030-21158-5
New Statistical Developments in Data Science
233
245
Emilia Rocco
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1155299
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