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A hierarchical clustering approach to identify repeated enrollments in web survey data.

Elizabeth A HandorfCarolyn J HeckmanSusan DarlowMichael SlifkerLee Ritterband
Published in: PloS one (2018)
When we excluded the clustered enrollments and/or lower-quality latent classes from the analysis of study outcomes, the estimates of the intervention effect were larger. This demonstrates how including repeat or low quality participants can introduce bias into a web-based study. As much as is possible, web-based surveys should be designed to verify participant quality. Our method can be used to verify survey quality and identify problematic groups of enrollments when necessary.
Keyphrases
  • cross sectional
  • quality improvement
  • randomized controlled trial
  • type diabetes
  • machine learning
  • metabolic syndrome
  • big data
  • artificial intelligence
  • insulin resistance