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Observed data values appear in impossible or improbable combinations.


Contradictions are mostly investigated within an observational unit, i.e. across measurements within the same participant or patient.

Contrary to other indicators of the data quality dimension consistency wherein e.g. violations of specified limits are examined, this indicator should focus on eligible values that have passed checks related to range and value violations.


Examples of contradictions are:

  1. men should not be pregnant
  2. a non-smoker will usually not report smoking >0 cigarettes per day
  3. if eating preference is vegetarian or vegan, weekly meat consumption should be zero.
  4. age recorded at baseline cannot be smaller than age recorded at follow-up
  5. the date of hospital admission is after the date of a hospital release


Assessing contradictions is more complicated compared to the assessment of range and value violations because rules must be applied to combinations of data values. Those rules are not assigned to single but to multiple study data values which is a complication to the metadata management.



  • Nonnemacher M, Nasseh D, Stausberg J. Datenqualität in der medizinischen Forschung: Leitlinie zum Adaptiven Datenmanagement in Kohortenstudien und Registern. Berlin: TMF e.V..; 2014.
  • Stausberg J, Bauer U, Nasseh D, et al. Indicators of data quality: review and requirements from the perspective of networked medical research MIBE 2019;15(1):1-8.
  • Kahn MG, Callahan TJ, Barnard J, et al. A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data. EGEMS (Wash DC). 2016;4(1):1244.