Register–based surveys require their own methodology and the development of these methods is an important challenge to statistical science. Instead of methods on how to collect data, methods for integrating data from different sources are necessary. How should administrative data be transformed to meet the statistical needs?
Register–based Statistics offers readers a detailed account of the principles and practices of this increasingly popular area of statistics.
- Provides a comprehensive overview of register–based statistics, both in terms of theory and advanced application.
- Uses real life examples taken from Statistics Sweden to illustrate fundamental global principles.
- Proposes a much–needed systematic terminology for the field.
- Describes how to create statistical registers and a methodology for integration of data from many sources as a key tool for the future.
- Develops estimation methods and quality concepts for register–based surveys.
- Discusses statistical systems consisting of many statistical registers and surveys, highlighting the importance of consistency and coherence.
Register–based Statistics provides a unique guide for all those working in statistical agencies. It will also prove invaluable for academic researchers and teachers in statistics, and statisticians working with administrative systems in government institutions and enterprises.
Chapter 1. Register–based surveys an introduction.
1.1 Do we need a theory on register–based surveys?
1.2 What is a statistical survey?
1.3 What is a register?
1.4 What is a register–based survey?
1.5 Administrative and statistical information systems.
1.6 Why should statistics be based on administrative data?
1.7 An overview of this book.
Chapter 2. How to structure a register system.
2.1 Object types and relations.
2.2 The system of base registers.
2.3 Standardised variables in the register system.
2.4 The register system as a whole.
2.5 Building and using the system.
2.6 Statistical register systems outside Statistics Sweden.
Chapter 3. A terminology for register–based surveys.
3.1 Terminology different language.
3.2 Register terms.
3.3 Terms for different kinds of variables.
Chapter 4. Sample surveys and registers.
4.1 How can sample surveys benefit by the register system?
4.2 How can register–based surveys and sample surveys be combined?
4.3 Comparing sample surveys and register–based surveys.
Chapter 5. How to create a register the population.
5.1 How should register–based surveys be structured?
5.2 Determining the research objectives.
5.3 The inventory phase making an inventory of different sources.
5.4 Defining a register s object set.
5.5 Defining and deriving objects.
5.6 How to produce regional register–based statistics.
Chapter 6. How to create a register the variables.
6.1 Deciding the register′s variable content.
6.2 Forming derived variables using models.
6.3 Editing and correcting register variables.
6.4 Creating longitudinal registers.
Chapter 7. Estimation methods.
7.1 Estimation in sample surveys and register–based surveys.
7.2 Fundamental estimation methods in register–based surveys.
7.3 Using weights in register–based surveys.
7.4 Estimation methods using weights calendar year registers.
7.5 Calibration of weights in register–based surveys.
Chapter 8. Calibration and imputation.
8.1 The nonresponse problem.
8.2 Estimation methods to correct for overcoverage.
8.3 Estimation methods to correct for level shifts in time series.
Chapter 9. Estimation with combination objects.
9.1 Aggregation errors.
9.2 Estimation methods for multi–valued variables.
9.3 Linking of time series at micro level using combination objects.
Chapter 10. Quality of register–based statistics.
10.1 Specific quality issues for register–based statistics?
10.2 Comparing errors in sample surveys and register–based surveys.
10.3 The users and the producers view of quality.
10.4 Detailed knowledge of the characteristics of a register.
10.3 Overall appraisal of quality.
Chapter 11. Metadata and IT–systems.
11.1 Primary registers the need for metadata.
11.2 Changes over time the need for metadata.
11.3 Integrated registers the need for metadata.
11.4 Classification and definitions database.
11.5 The need for metadata for registers.
11.6 IT systems for register–based statistics.
Chapter 12. Protection of privacy and confidentiality.
12.1 Internal security.
12.2 Disclosure risks tables.
12.3 Disclosure risks micro data.
Chapter 13. Coordination and coherence.
13.1 Content–related coordination.
13.3 Consistent and coherent enterprise statistics.
Chapter 14. Conclusions.