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    Chapter A paradata-driven statistical approach to improve fieldwork monitoring: the case of the Non-Profit Institutions census

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    Author(s)
    Nuccitelli, Alessandra
    Arlotta, Luigi
    Giacummo, Maura
    Fazzi, Gabriella
    Murgia, Manuela
    Rossetti , Francesca
    Parisi, Valentino
    Piergiovanni, Roberta
    Language
    English
    Show full item record
    Abstract
    A complex process requires relevant information on the crucial nodes of the process itself to make more effective decisions. This is the case for large complex surveys where among the several causes of wrong or inappropriate interviewers’ behaviors only the crucial ones have to be identified and corrected to avoid a knock-on effect. An example of such a survey is the Non-Profit Institutions Census (NPIC), for which fieldwork monitoring is improved using a paradata-driven approach based on the use of quality control tools. The complexity of NPIC is not only due to the large amount of units it involves but also to the great variety of unit-typologies: from large and structured institutions to very small associations. Complexity depends also on the different data collection modes and on the wide variety of communication channels. Besides, two questionnaires with different research aims – to assess the quality of statistical registers (short questionnaire) and to collect information (long questionnaire) – contribute to boosting complexity. The use of computer-assisted survey instruments offers the opportunity to automatically record paradata, making it possible to apply statistical procedures that allow for near real-time monitoring. To this end, a set of performance indicators is defined to assess the adequacy and observance of the survey protocols and to uncover any problematic situations that need to be addressed quickly. Once indicators are defined, control charts can be used to display them. Control charts help balance cost and thoroughness of monitoring activities by using statistical principles to differentiate potentially problematic cases from those that vary naturally around a process average. In this way, survey managers can make targeted interventions, without spending time exploring false alarms. The work will describe the experience made with the NPIC and how it can be applied to other Censuses or to any other interviewer-based survey.
    Book
    ASA 2022 Data-Driven Decision Making
    URI
    https://library.oapen.org/handle/20.500.12657/74925
    Keywords
    computer-assisted survey; Non-Profit Institutions Census; performance indicators
    DOI
    10.36253/979-12-215-0106-3.53
    ISBN
    9791221501063, 9791221501063
    Publisher
    Firenze University Press, Genova University Press
    Publication date and place
    Florence, 2023
    Series
    Proceedings e report, 134
    Classification
    Society and Social Sciences
    Pages
    6
    Rights
    https://creativecommons.org/licenses/by/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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