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Inference with non-probability samples and survey data integration: a science mapping study

Author

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  • Camilla Salvatore

    (University of Milano-Bicocca
    Universiteit Van Amsterdam)

Abstract

In recent years, survey data integration and inference based on non-probability samples have gained considerable attention. Because large probability-based samples can be cost-prohibitive in many instances, combining a probabilistic survey with auxiliary data is appealing to enhance inferences while reducing the survey costs. Also, as new data sources emerge, such as big data, inference and statistical data integration will face new challenges. This study aims to describe and understand the evolution of this research field over the years with an original approach based on text mining and bibliometric analysis. In order to retrieve the publications of interest (books, journal articles, proceedings, etc.), the Scopus database is considered. A collection of 1023 documents is analyzed. Through the use of such methodologies, it is possible to characterize the literature and identify contemporary research trends as well as potential directions for future investigation. We propose a research agenda along with a discussion of the research gaps which need to be addressed.

Suggested Citation

  • Camilla Salvatore, 2023. "Inference with non-probability samples and survey data integration: a science mapping study," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 83-107, April.
    • Handle: RePEc:spr:metron:v:81:y:2023:i:1:d:10.1007_s40300-023-00243-6
    DOI: 10.1007/s40300-023-00243-6
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    References listed on IDEAS

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    1. Oriol J. Bosch & Melanie Revilla, 2022. "When survey science met web tracking: Presenting an error framework for metered data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 408-436, December.
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    8. Gunnar Andersson & Kirk Scott, 2007. "Childbearing dynamics of couples in a universalistic welfare state," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 17(30), pages 897-938.
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    17. Belfiore, Alessandra & Cuccurullo, Corrado & Aria, Massimo, 2022. "IoT in healthcare: A scientometric analysis," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    18. Cobo, M.J. & López-Herrera, A.G. & Herrera-Viedma, E. & Herrera, F., 2011. "An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field," Journal of Informetrics, Elsevier, vol. 5(1), pages 146-166.
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    21. Denny, Matthew J. & Spirling, Arthur, 2018. "Text Preprocessing For Unsupervised Learning: Why It Matters, When It Misleads, And What To Do About It," Political Analysis, Cambridge University Press, vol. 26(2), pages 168-189, April.
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