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DQI-4005

Definition

Observed scale parameters differ from expected scale parameters.

Explanation

This indicator targets discrepancies between the expected and observed scale of a distribution. A scale parameter determines how much spread out a distribution is, or, in other words, the statistical dispersion of a probability distribution. Measures of scale are for example the variance, the standard deviation, the range or interquartile range.

Example

In an adult European general population sample the standard deviation of body weight in kilograms is expected to lie within 15-20 kg. Yet the observed standard deviation is almost 37. At inspection, it is revealed that the unit of measurement was mistakenly converted to pounds, resulting in a standard deviation of approx. 37 instead of 17.

Guidance

Deviations of observed from expected scale parameters may indicate a wide range of issues such as examiner effects, device effects but also sampling issues. When using different devices issues related to diverging units of measuement may be considered.

In a designed study, little effects of study design factors, such as devices or examiners, should be exerted on scale parameters. Finding associations of relevance between these factors and sccale parameters are commonly indicative of measurement error.

For any interpretation it is important to take the number of cases into account. Low numbers may introduce a considerable amount of uncertainty.

Interpretation

Within variables:

The larger the deviation of expected and observed shape/scale parameters, the larger the probability of a lower data quality.

Across variables:

The higher the number or percentage of variables affected by unexpected shape/scale parameter related issues, the higher the probability of a low data quality.

Descriptors

Literature

  • 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.