Observed data values appear in impossible or improbable
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:
- men should not be pregnant
- a non-smoker will usually not report smoking >0 cigarettes per
- if eating preference is vegetarian or vegan, weekly meat consumption
should be zero.
- age recorded at baseline cannot be smaller than age recorded at
- the date of hospital admission is after the date of a hospital
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
- 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.