The capacity of a dataset to lead to the same inferences being drawn before and after treatment using disclosure control methods, when the same analysis is conducted.
A companion to analytical completeness, loss of analytical validity is more critical because of its insidious nature. Technically, loss of validity can be said to occur when a disclosure control method has changed a p. 13dataset to the point where a user reaches a different conclusion from the same analysis. This typically happens with perturbative disclosure control techniques such as microaggregation, local suppression, post randomisation or noise addition. Concerns about the utility impacts of differential privacy are essentially concerns about analytical validity.
Further reading:
See also: STATISTICAL DISCLOSURE CONTROL
Purdam, K. and Elliot, M. (2007). A case study of the impact of statistical disclosure control on data quality in the individual UK samples of anonymised records. Environment and Planning A, 39(5), 1101–18, https://doi.org/10.1068/a38335.
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Purdam, K. and Elliot, M. (2007). A case study of the impact of statistical disclosure control on data quality in the individual UK samples of anonymised records. Environment and Planning A, 39(5), 1101–18, https://doi.org/10.1068/a38335.
