Accounting for longitudinal data structures when disseminating synthetic data to the public

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• Joerg Drechsler (Institut für Arbeitsmarkt-und Berufsforschung)
• Friday 09 December 2016, 14:15-15:00
• Seminar Room 1, Newton Institute.

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When using multiple imputation to synthesise confidential data, careful attention must be given to the selection of appropriate synthesis models since only those features that are incorporated in the model will be reflected in the generated data. If the dataset has a longitudinal structure, i.e. the same units are surveyed at more than one point in time, it is not obvious which synthesis model should be used to account for the design. Using multiple imputation for missing data, it has previously been shown that employing fixed effects at the imputation stage may adversely affect inferences obtained by an analyst wishing to use random effects to account for the hierarchy and vice versa. Since it is generally unknown which model users of the data will prefer, a synthesis model should be preferred that suits both analysis models. We evaluate several strategies for generating longitudinal synthetic datasets using extensive simulation studies. In our evaluations we consider both, the analytical validity and the risk of disclosure resulting from the different synthesis strategies. We find that synthesis models should be preferred that cannot be classified as pure random or fixed effects models. We illustrate our findings using data from the German IAB Establishment Panel. This is joint work with Sana Rashid and Robin Mitra, both from the University of Southampton. 

This talk is part of the Isaac Newton Institute Seminar Series series.

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