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        <title type="main" level="a">Total Process Error framework: an application to economic statistical registers</title>
        <author>
          <persName n="1">
            <forename>Roberta</forename>
            <surname>Varriale</surname>
            <placeName type="affiliation">ISTAT, Italian National Institute of Statistics, Italy</placeName>
          </persName>
          <persName n="2">
            <forename>Fabiana</forename>
            <surname>Rocci</surname>
            <placeName type="affiliation">ISTAT, Italian National Institute of Statistics, Italy</placeName>
          </persName>
          <persName n="3">
            <forename>Orietta</forename>
            <surname>Luzi</surname>
            <placeName type="affiliation">ISTAT, Italian National Institute of Statistics, Italy</placeName>
          </persName>
        </author>
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          <resp>This is a section of <title>ASA 2021 Statistics and Information Systems for Policy Evaluation  </title>(DOI: <idno type="DOI">10.36253/978-88-5518-461-8</idno>) by </resp>
          <name>Alessandra Petrucci, Bruno Bertaccini, Luigi Fabbris</name>
        </respStmt>
      </titleStmt>
      <publicationStmt>
        <publisher>Firenze University Press</publisher>
        <pubPlace>Firenze</pubPlace>
        <date when="2021">2021</date>
        <idno type="DOI">https://doi.org/10.36253/978-88-5518-461-8.28</idno>
        <availability>
          <p>Available for academic research purposes</p>
          <p>Open Access</p>
          <p>Copyright Author(s)</p>
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            <p>Content licence CC BY 4.0</p>
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            <p>Metadata licence CC0 1.0</p>
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        <p>This is original content, published for academic research purposes</p>
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      <abstract xml:lang="en">
        <p>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.</p>
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        <keywords>
          <list>
            <item>quality assessment</item>
            <item>total error</item>
            <item>multisource processes</item>
            <item>statistical registers</item>
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        </keywords>
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    <body>
      <p>It is available online at https://doi.org/10.36253/978-88-5518-461-8.28<ref target="https://doi.org/10.36253/978-88-5518-461-8.28" /></p>
      <div>
        <listBibl>
          <head>References</head>
          <bibl n="60641">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.</bibl>
          <bibl n="60642">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.</bibl>
          <bibl n="60643">Wallgren, A. and B. Wallgren. (2014). Register based statistics: Administrative data for statistical purposes. John Wiley &amp;amp; Sons, Ltd.</bibl>
          <bibl n="60644">Zhang, L.C. 2012. Topics of statistical theory for register-based statistics and data integration. Statistica Neerlandica, 66 (1), pp. 41-63.</bibl>
        </listBibl>
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