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Book Chapter

Total Process Error framework: an application to economic statistical registers

  • Roberta Varriale
  • Fabiana Rocci
  • Orietta Luzi

In recent years, the Italian national institute of statistics (Istat), together with most National Statistical Institutes, is progressively moving from traditional production models based on the use of primary source of information - represented by direct surveys - to new production strategies based on the combined use of different primary and secondary sources of information. As result, new multisource statistical processes have been built, that guarantee a major improvement of both amount and quality of information about several phenomena of public interest. In this context, the Total Process Error (TPE) framework has been recently proposed in literature for assessing the quality of multisource processes. The TPE framework represents an evolution of the Zhang’s two-phase life-cycle approach and it additionally includes an operational tool to connect the steps of the multisource production process to the phases of the quality evaluation framework. TPE framework can be used both to support a multisource process design and to monitor an entire production process, in order to provide key elements to assess the quality of both the processes and their statistical outputs. In the present work, we describe as a case study in the new context of Istat production of official statistics the use of the TPE framework to support the process design of the Register for Public Administrations.

  • Keywords:
  • quality assessment,
  • total error,
  • multisource processes,
  • statistical registers,
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Roberta Varriale

ISTAT, Italian National Institute of Statistics, Italy

Fabiana Rocci

ISTAT, Italian National Institute of Statistics, Italy

Orietta Luzi

ISTAT, Italian National Institute of Statistics, Italy

  1. AA.VV. (2014). Memobust Handbook on Methodology of Modern Business Statistics. Available at: https://ec.europa.eu/eurostat/cros/system/files/Memobust%20glossary%20def.pdf.
  2. Rocci F., Varriale R., Luzi O. (2022). Total process error: An approach for assessing and monitoring the quality of multisource processes. Forthcoming in Journal of Official Statistics, June 2022.
  3. Wallgren, A. and B. Wallgren. (2014). Register based statistics: Administrative data for statistical purposes. John Wiley & Sons, Ltd.
  4. Zhang, L.C. 2012. Topics of statistical theory for register-based statistics and data integration. Statistica Neerlandica, 66 (1), pp. 41-63.
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  • Publication Year: 2021
  • Pages: 147-151
  • Content License: CC BY 4.0
  • © 2021 Author(s)

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  • Publication Year: 2021
  • Content License: CC BY 4.0
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Chapter Information

Chapter Title

Total Process Error framework: an application to economic statistical registers

Authors

Roberta Varriale, Fabiana Rocci, Orietta Luzi

DOI

10.36253/978-88-5518-461-8.28

Peer Reviewed

Publication Year

2021

Copyright Information

© 2021 Author(s)

Content License

CC BY 4.0

Metadata License

CC0 1.0

Table of Contents

Book Title

ASA 2021 Statistics and Information Systems for Policy Evaluation

Book Subtitle

BOOK OF SHORT PAPERS of the on-site conference

Editors

Alessandra Petrucci, Bruno Bertaccini, Luigi Fabbris

Peer Reviewed

Publication Year

2021

Copyright Information

© 2021 Author(s)

Content License

CC BY 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/978-88-5518-461-8

eISBN (pdf)

978-88-5518-461-8

eISBN (xml)

978-88-5518-462-5

Series Title

Proceedings e report

Series Issn ISSN

2704-601X

Series E-Issn

2704-5846

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