Synthetic Datasets for Statistical Disclosure Control: Theory and Implementation - Paperback

Book in our database since 2008-11-29T11:20:17+00:00 (London)
Detail page last modified on 2023-11-26T12:31:31+00:00 (London)
ISBN/EAN: 9781461403258

ISBN - alternate spelling:
1-4614-0325-1, 978-1-4614-0325-8
Alternate spelling and related search-keywords:
Book author: drechsler
Book title: drechsler, statistical disclosure control, datasette

Information from Publisher

Author: Jörg Drechsler
Title: Lecture Notes in Statistics; Synthetic Datasets for Statistical Disclosure Control - Theory and Implementation
Publisher: Springer; Springer US
138 Pages
Publishing year: 2011-06-29
New York; NY; US
Printed / Made in
Language: English
128,39 € (DE)
131,99 € (AT)
142,00 CHF (CH)
POD
XX, 138 p. 19 illus.

BC; Hardcover, Softcover / Soziologie/Methoden der empirischen und qualitativen Sozialforschung; Sozialforschung und -statistik; Verstehen; confidentiality; disclosure; multiple imputation; synthetic; Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy; Statistics in Business, Management, Economics, Finance, Insurance; Biostatistics; Wahrscheinlichkeitsrechnung und Statistik; Wirtschaftswissenschaft, Finanzen, Betriebswirtschaft und Management; EA

Introduction.- Background on Multiply Imputed Synthetic Datasets.- Background on Multiple Imputation.- The IAB Establishment Panel.- Multiple Imputation for Nonresponse.- Fully Synthetic Datasets.- Partially Synthetic Datasets.- Multiple Imputation for Nonresponse and Statistical Disclosure Control.- A Two-Stage Imputation Procedure to Balance the Risk-Utility Trade-Off.- Chances and Obstacles for Multiply Imputed Synthetic Datasets.

From the reviews:

“This book explores … the generation of synthetic data with the same statistical properties as the original data, but containing different records. … It also addresses complications which arise with real data, such as missing values. The book provides an excellent overview of the area, and would make ideal reading for anyone new to the issues, as well as serving as a good source book describing technical details.” (David J. Hand, International Statistical Review, Vol. 80 (3), 2012)

Jörg Drechsler is a Research Scientist at the German Institute for Employment Research, Department for Statistical Methods. His main areas of research involve statistical disclosure control and imputation with published papers in JASA, Statistica Sinica, JOS, and Survey Methodology.

The aim of this book is to give the reader a detailed introduction to the different approaches to generating multiply imputed synthetic datasets. It describes all approaches that have been developed so far, provides a brief history of synthetic datasets, and gives useful hints on how to deal with real data problems like nonresponse, skip patterns, or logical constraints.

Each chapter is dedicated to one approach, first describing the general concept followed by a detailed application to a real dataset providing useful guidelines on how to implement the theory in practice.

The discussed multiple imputation approaches include imputation for nonresponse, generating fully synthetic datasets, generating partially synthetic datasets, generating synthetic datasets when the original data is subject to nonresponse, and a two-stage imputation approach that helps to better address the omnipresent trade-off between analytical validity and the risk of disclosure.

The book concludes with a glimpse into the future of synthetic datasets, discussing the potential benefits and possible obstacles of the approach and ways to address the concerns of data users and their understandable discomfort with using data that doesn’t consist only of the originally collected values. 

The book is intended for researchers and practitioners alike. It helps the researcher to find the state of the art in synthetic data summarized in one book with full reference to all relevant papers on the topic. But it is also useful for the practitioner at the statistical agency who is considering the synthetic data approach for data dissemination in the future and wants to get familiar with the topic.