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

Author

Listed:
  • Rocci Fabiana

    (Istat, Via Cesare Balbo 16, 00184 Roma, Italy.)

  • Varriale Roberta

    (Istat, Via Cesare Balbo 16, 00184 Roma, Italy.)

  • Luzi Orietta

    (Istat, Via Cesare Balbo 16, 00184 Roma, Italy.)

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.

Suggested Citation

  • Rocci Fabiana & Varriale Roberta & Luzi Orietta, 2022. "Total Process Error: An Approach for Assessing and Monitoring the Quality of Multisource Processes," Journal of Official Statistics, Sciendo, vol. 38(2), pages 533-556, June.
    • Handle: RePEc:vrs:offsta:v:38:y:2022:i:2:p:533-556:n:12
    DOI: 10.2478/jos-2022-0025
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    References listed on IDEAS

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    1. Ton de Waal & Arnout van Delden & Sander Scholtus, 2020. "Multi‐source Statistics: Basic Situations and Methods," International Statistical Review, International Statistical Institute, vol. 88(1), pages 203-228, April.
    2. Marco Di Zio & Ugo Guarnera & Roberta Varriale, 2016. "Estimation of the main variables of the economic account of small and medium enterprises based on administrative sources," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 18(1), pages 71-81.
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