<abstract xmlns="http://www.w3.org/1999/xhtml">

Most National Statistical Institutes are progressively moving from traditional production models to new strategies based on the combined use of different sources of information, which can be both primary and secondary. In this article, we propose a framework for assessing the quality of multisource processes, such as statistical registers.
The final aim is to develop a tool supporting decisions about the process design and its monitoring, and to provide quality measures of the whole production. The starting point is the adaptation of the life-cycle paradigm, that results in a three-phases framework described in recent literature. An evolution of this model is proposed, focusing on the first two phases of the life-cycle, to better represent the source integration/combination phase, that can vary accordingly to the features of different types of processes.
The proposed enhancement would improve the existing quality framework to support the evaluation of different multisource processes. An application of the proposed framework to two Istat (Italian national statistical institute) registers in the economic area taken as case studies is presented. These experiences show the potentials of such tool in supporting National Statistical Institutes in assessing multisource statistical production processes.
</abstract>

Most National Statistical Institutes are progressively moving from traditional production models to new strategies based on the combined use of different sources of information, which can be both primary and secondary. In this article, we propose a framework for assessing the quality of multisource processes, such as statistical registers.
The final aim is to develop a tool supporting decisions about the process design and its monitoring, and to provide quality measures of the whole production. The starting point is the adaptation of the life-cycle paradigm, that results in a three-phases framework described in recent literature. An evolution of this model is proposed, focusing on the first two phases of the life-cycle, to better represent the source integration/combination phase, that can vary accordingly to the features of different types of processes.
The proposed enhancement would improve the existing quality framework to support the evaluation of different multisource processes. An application of the proposed framework to two Istat (Italian national statistical institute) registers in the economic area taken as case studies is presented. These experiences show the potentials of such tool in supporting National Statistical Institutes in assessing multisource statistical production processes.

Total Process Error: An Approach for Assessing and Monitoring the Quality of Multisource Processes

Journal of Official Statistics

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

Most National Statistical Institutes are progressively moving from traditional production models to new strategies based on the combined use of different...

Total Process Error: An Approach for Assessing and Monitoring the Quality of Multisource Processes

Publié en ligne: 14 juin 2022

Pages: 533 - 556

Reçu: 01 févr. 2020

Accepté: 01 août 2021

DOI: https://doi.org/10.2478/jos-2022-0025

Mots clés
Quality framework, multi-source processes, total survey error, statistical register

© 2022 Fabiana Rocci et al., published by Sciendo

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

Total Process Error: An Approach for Assessing and Monitoring the Quality of Multisource Processes

Publié en ligne: 14 juin 2022

Pages: 533 - 556

Reçu: 01 févr. 2020

Accepté: 01 août 2021

DOI: https://doi.org/10.2478/jos-2022-0025

Mots clésQuality framework, multi-source processes, total survey error, statistical register

© 2022 Fabiana Rocci et al., published by Sciendo

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

Mots clés
Quality framework, multi-source processes, total survey error, statistical register