Utility Measurement

General utility measures in sdcApp

Compare summary statistics

Categorical variables

Continuous variables

IL1s measure

Customized utility measures

As the statistical analyses based on the microdata depend, amongst others, on to the topic of the survey, the country and the definition of the variables, it is not feasible to include all these measures in sdcApp. Instead, it is recommended to compute the statistics and indicators and perform statistical an econometric analyses on the original and anonymized datasets and evaluate the differences in the results. If a publication based on the microdata is already published, it is recommended to recompute the statistics in these publications from the anonymized dataset.

The approach is to compare the indicators calculated on the untreated data and the data after anonymization with different methods. If the differences between the indicators are not too large, the anonymized dataset can be released for use by researchers. It should be taken into account that indicators calculated on samples are estimates with a certain variance and confidence interval. Therefore, for sample data, it is more informative to compare the overlap of confidence intervals and/or to evaluate whether the point estimate calculated after anonymization is contained within the confidence interval of the original estimate.

Note

Some analyses may no longer be possible or not possible in exactly the same way. E.g. regression on age if age is recoded in 5-year intervals.

In order to do so, it is posible to export the dataset at any point in sdcApp. See the Section Export anonymized dataset to learn how to export a dataset from sdcApp. The microdata can be exported at any intermediate stata in the SDC process. Several datasets can be exported after applying different methods with different parameters settings to compare the information loss resulting from the anonymization. This information can be used to select the anonymization methods as well as to inform the user about the implications of the anonymization on the validity of the dataset for analysis.