- ASA 2022 Data-Driven Decision Making
- Edited by Enrico di Bella, Luigi Fabbris, Corrado Lagazio
A paradata-driven statistical approach to improve fieldwork monitoring: the case of the Non-Profit Institutions census
- Gabriella Fazzi
- Manuela Murgia
- Alessandra Nuccitelli
- Francesca Rossetti
- Valentino Parisi
- Roberta Piergiovanni
- Luigi Arlotta
- Maura Giacummo
- © 2023 Author(s) |
- CC BY 4.0
- DOI: 10.36253/979-12-215-0106-3.53
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.
- Keywords:
- computer-assisted survey,
- Non-Profit Institutions Census,
- performance indicators,
ISTAT, Italian National Institute of Statistics, Italy - ORCID: 0000-0001-5661-3963
ISTAT, Italian National Institute of Statistics, Italy - ORCID: 0000-0003-4154-3784
ISTAT, Italian National Institute of Statistics, Italy
ISTAT, Italian National Institute of Statistics, Italy - ORCID: 0000-0002-5974-0826
ISTAT, Italian National Institute of Statistics, Italy - ORCID: 0000-0001-8429-4250
ISTAT, Italian National Institute of Statistics, Italy - ORCID: 0000-0003-2489-8786
ISTAT, Italian National Institute of Statistics, Italy
ISTAT, Italian National Institute of Statistics, Italy
- Jans, M., Sirkis, R., Morgan, D. (2013). Managing data quality indicators with paradata based statistical quality control tools: the keys to survey performance, in Improving surveys with paradata: analytic uses of process information, ed. F. Kreuter, John
- Montgomery, D. C. (2009). Introduction to statistical quality control, Sixth edition, John Wiley & Sons Inc., Hoboken, (NJ).
- Reed, S. J, Reed, J. H. (1997). The use of statistical quality control charts in monitoring interviewers, in Proceedings of the Joint Statistical Meetings, Survey Research Methods Section, American Statistical Association, Alexandria, (VA).
- SAS Institute Inc. (2018). SAS/QC® 15.1 User’s guide. SAS Institute Inc., Cary, (NC).
Chapter Information
Chapter Title
A paradata-driven statistical approach to improve fieldwork monitoring: the case of the Non-Profit Institutions census
Authors
Gabriella Fazzi, Manuela Murgia, Alessandra Nuccitelli, Francesca Rossetti, Valentino Parisi, Roberta Piergiovanni, Luigi Arlotta, Maura Giacummo
Language
English
DOI
10.36253/979-12-215-0106-3.53
Peer Reviewed
Publication Year
2023
Copyright Information
© 2023 Author(s)
Content License
Metadata License
Bibliographic Information
Book Title
ASA 2022 Data-Driven Decision Making
Book Subtitle
Book of short papers
Editors
Enrico di Bella, Luigi Fabbris, Corrado Lagazio
Peer Reviewed
Publication Year
2023
Copyright Information
© 2023 Author(s)
Content License
Metadata License
Publisher Name
Firenze University Press, Genova University Press
DOI
10.36253/979-12-215-0106-3
eISBN (pdf)
979-12-215-0106-3
eISBN (xml)
979-12-215-0107-0
Series Title
Proceedings e report
Series ISSN
2704-601X
Series E-ISSN
2704-5846