Cost and Data Quality Impact of Fraud Mitigation in Online Research: Framework Application and Evaluation Study - Takeaways - MDSpire
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Evaluating the Costs and Data Quality Effects of Fraud Prevention in Online Research: Applying a Framework

  • By

  • Maya Stemmer

  • Benjamin Buck

  • Justin Tauscher

  • Angelina Pei-Tzu Tsai

  • Anna Larsen

  • Patrick Wedgeworth

  • Gillian Sparks

  • Ella DeVries

  • Mercedes Bishop

  • Alexa Beaulieu

  • Trevor Cohen

  • Dror Ben-Zeev

  • October 8, 2026

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  • 1

    Online research is vulnerable to fraudulent participation, including identity falsification, duplicate enrollment, and synthetic data.

  • 2

    Fraudulent records can distort recruitment, compromise analytic data, and increase study costs.

  • 3

    The CATCH framework organizes fraud mitigation into configure, assess, triage, corroborate, and hone phases.

  • 4

    This paper applies CATCH to a large-scale remote mental health study assessing hallucinations and related outcomes with longitudinal digital data.

  • 5

    The authors developed stage-based cost accounting to track where resources are used and who receives them during fraud mitigation.

Original Source(s)

Cost and Data Quality Impact of Fraud Mitigation in Online Research: Framework Application and Evaluation Study

  • by Maya Stemmer, Benjamin Buck, Justin Tauscher, Angelina Pei-Tzu Tsai, Anna Larsen, Patrick Wedgeworth, Gillian Sparks, Ella DeVries, Mercedes Bishop, Alexa Beaulieu, Trevor Cohen, Dror Ben-Zeev

  • October 8, 2026

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