Synthetic Data Sets for Statistical Disclosure Limitation (ADDED FEE) — Professional Development Continuing Education Course
ASA , Survey Research Methods Section
This continuing education course will provide a detailed overview of the topic, covering all important aspects relevant for the synthetic data approach. Starting with a short introduction to data confidentiality in general and synthetic data in particular, the workshop will discuss the different approaches to generating synthetic datasets in detail. Possible modeling strategies and analytical validity evaluations will be assessed and potential measures to quantify the remaining risk of disclosure will be presented. To provide the participants with hands on experience, all steps will be illustrated using simulated and real data examples in R.
The course intends to summarize the state of the art in synthetic data. The main focus will be on practical implementation and not so much on the motivation of the underlying statistical theory. Some background regarding general linear modelling is expected. Familiarity with the concept of Bayesian statistics is helpful but not required. The statistical software R will be used to illustrate the implementation of the approach.
Familiarity with basics in R would be useful but is not required.
Instructor(s): Jörg Drechsler, Institute for Employment Research, Jerry Reiter, Department of Statistical Science, Duke University
