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synthpop: Bespoke Creation of Synthetic Data in R

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  • Nowok, Beata
  • Raab, Gillian M.
  • Dibben, Chris

Abstract

In many contexts, confidentiality constraints severely restrict access to unique and valuable microdata. Synthetic data which mimic the original observed data and preserve the relationships between variables but do not contain any disclosive records are one possible solution to this problem. The synthpop package for R, introduced in this paper, provides routines to generate synthetic versions of original data sets. We describe the methodology and its consequences for the data characteristics. We illustrate the package features using a survey data example.

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  • Nowok, Beata & Raab, Gillian M. & Dibben, Chris, 2016. "synthpop: Bespoke Creation of Synthetic Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i11).
    • Handle: RePEc:jss:jstsof:v:074:i11
    DOI: http://hdl.handle.net/10.18637/jss.v074.i11
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    7. Andreas Alfons & Stefan Kraft & Matthias Templ & Peter Filzmoser, 2011. "Simulation of close-to-reality population data for household surveys with application to EU-SILC," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(3), pages 383-407, August.
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    Cited by:

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    5. Asunur Cezar & Srinivasan Raghunathan & Sumit Sarkar, 2020. "Adversarial Classification: Impact of Agents’ Faking Cost on Firms and Agents," Production and Operations Management, Production and Operations Management Society, vol. 29(12), pages 2789-2807, December.
    6. Lau Lilleholt & Ingo Zettler & Cornelia Betsch & Robert Böhm, 2023. "Development and validation of the pandemic fatigue scale," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    7. Wesley J. Marrero & Mariel S. Lavieri & Jeremy B. Sussman, 2021. "Optimal cholesterol treatment plans and genetic testing strategies for cardiovascular diseases," Health Care Management Science, Springer, vol. 24(1), pages 1-25, March.
    8. Gunjan Chandra & Pekka Siirtola & Satu Tamminen & Mikael J. Knip & Riitta Veijola & Juha Röning, 2022. "Impacts of Data Synthesis: A Metric for Quantifiable Data Standards and Performances," Data, MDPI, vol. 7(12), pages 1-26, December.
    9. Jahangir Alam M. & Dostie Benoit & Drechsler Jörg & Vilhuber Lars, 2020. "Applying data synthesis for longitudinal business data across three countries," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 212-236, August.
    10. James Jackson & Robin Mitra & Brian Francis & Iain Dove, 2022. "Using saturated count models for user‐friendly synthesis of large confidential administrative databases," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1613-1643, October.
    11. Speidel, Matthias & Drechsler, Jörg & Jolani, Shahab, 2018. "R package hmi: a convenient tool for hierarchical multiple imputation and beyond," IAB-Discussion Paper 201816, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    12. Erik D. Mueller & J. S. Onésimo Sandoval & Srikanth P. Mudigonda & Michael Elliott, 2019. "Extending cluster-based ensemble learning through synthetic population generation for modeling disparities in health insurance coverage across Missouri," Journal of Computational Social Science, Springer, vol. 2(2), pages 271-291, July.
    13. Felix Ritchie & Jim Smith, 2019. "Confidentiality and linked data," Papers 1907.06465, arXiv.org.

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