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Creating Personal Extracts While Keeping Confidentiality

Presenter 1
David Schiller
Institute for Employment Research (IAB)

The German National Educational Panel Study (NEPS) collects longitudinal data for educational research. With six starting cohorts and 60.000 target persons, a more and more complex data structure emerges with every new wave. Data for scientific research is normally offered as flat files with a fixed number of variables and values. Therefore data providers have to build appropriate flat files for the scientific community, which is a difficult manual task as the data structure is complex. New ways to solve the mentioned problem open up when storing the data within a relational database. Variables or sets of variables, out of the entire range of data, can be selected and combined via a user interface similar to a shopping basket system or a "variable browser". The results are exported as individual datasets in different formats that meet the individual needs of researchers (e.g., Stata, SPSS). Database functionality and tools of business intelligence provide a wide range of possibilities to support the researcher during the selection process (e.g., descriptive statistics or metadata information). Confidentiality can be maintained by using a combination of database tools and statistical disclosure control methods (e.g., by using "security levels" for variables, or by using synthetic data).

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