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Administrative Records for Survey Methodology

Chun, Asaph Young / Larsen, Michael D. / Durrant, Gabriele / Reiter, Jerome P. (Herausgeber)

Wiley Series in Survey Methodology

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1. Auflage Juni 2021
384 Seiten, Hardcover
Wiley & Sons Ltd

ISBN: 978-1-119-27204-5
John Wiley & Sons

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ADMINISTRATIVE RECORDS FOR SURVEY METHODOLOGY

Addresses the international use of administrative records for large-scale surveys, censuses, and other statistical purposes

Administrative Records for Survey Methodology is a comprehensive guide to improving the quality, cost-efficiency, and interpretability of surveys and censuses using administrative data research. Contributions from a team of internationally-recognized experts provide practical approaches for integrating administrative data in statistical surveys, and discuss the methodological issues--including concerns of privacy, confidentiality, and legality--involved in collecting and analyzing administrative records. Numerous real-world examples highlight technological and statistical innovations, helping readers gain a better understanding of both fundamental methods and advanced techniques for controlling data quality reducing total survey error.

Divided into four sections, the first describes the basics of administrative records research and addresses disclosure limitation and confidentiality protection in linked data. Section two focuses on data quality and linking methodology, covering topics such as quality evaluation, measuring and controlling for non-consent bias, and cleaning and using administrative lists. The third section examines the use of administrative records in surveys and includes case studies of the Swedish register-based census and the administrative records applications used for the US 2020 Census. The book's final section discusses combining administrative and survey data to improve income measurement, enhancing health surveys with data linkage, and other uses of administrative data in evidence-based policymaking. This state-of-the-art resource:
* Discusses important administrative data issues and suggests how administrative data can be integrated with more traditional surveys
* Describes practical uses of administrative records for evidence-driven decisions in both public and private sectors
* Emphasizes using interdisciplinary methodology and linking administrative records with other data sources
* Explores techniques to leverage administrative data to improve the survey frame, reduce nonresponse follow-up, assess coverage error, measure linkage non-consent bias, and perform small area estimation.

Administrative Records for Survey Methodology is an indispensable reference and guide for statistical researchers and methodologists in academia, industry, and government, particularly census bureaus and national statistical offices, and an ideal supplemental text for undergraduate and graduate courses in data science, survey methodology, data collection, and data analysis methods.

Section 1: Fundamentals of Administrative Records Research and Applications

1. On the use of proxy variables in combining register and survey data, Li-Chun Zhang, Statistics Norway and University of Southampton

1.1. Introduction

1.2. Instances of proxy variable

1.3. Estimation using multiple proxy variables

1.4. Summary

1.5. References

2. Disclosure Limitation and Confidentiality Protection in Linked Data, John Maron Abowd, U.S. Census Bureau and Cornell University, Ian M. Schmutte, University of Georgia; and Lars Vilhuber, Cornell University.

2.1. Introduction

2.2. Paradigms of protection

2.3. Confidentiality protection in linked data: Examples

2.4. Physical and legal protections

2.5. Conclusions

2.6. References

2.7. Appendix: Technical Terms and Acronyms

Section 2: Data Quality of Administrative Records and Linking Methodology

3. Evaluation of the Quality of Administrative Data Used in the Dutch Virtual Census, Piet Daas, Eric Schulte Nordholt, Martijn Tennekes, and Saskia Ossen, Statistics Netherlands

3.1. Introduction

3.2. Data sources and variables

3.3. Quality framework

3.4. Quality evaluation results for the Dutch 2011 Census

3.5. Summary

3.6. Practical implications for implementation with surveys and censuses

3.7. Exercises

3.8. References

4. Improving input data quality in register-based statistics: The Norwegian experience, Coen Hendriks, Statistics Norway

4.1. Introduction

4.2. The use of administrative sources in Statistics Norway

4.3. Managing statistical populations

4.4. Experiences from the first Norwegian purely register based Population and Housing Census of 2011

4.5. The contact with the owners of administrative registers was put into system

4.6. Measuring and documenting input data quality

4.7. Summary

4.8. Exercises

4.9. References

4.10. Appendix: Example of a quality report for registered persons in the Central Population Register

5. Cleaning and Using Administrative Lists: Enhanced Practices and Computational Algorithms for Record Linkage and Modeling/Editing/Imputation, William Erwin Winkler, U.S. Census Bureau

5.1. Introductory comments

5.2. Edit/Imputation

5.3. Record Linkage

5.4. Models for Adjusting Statistical Analyses for Linkage Error

5.5. Concluding Remarks

5.6. Issues and some related questions

5.7. References

6. Assessing Uncertainty when Using Linked Administrative Records, Jerome P. Reiter, Duke University

6.1. Introduction

6.2. General sources of uncertainty

6.3. Approaches to accounting for uncertainty

6.4. Concluding Remarks

6.5. Exercises

6.6. References

7. Measuring and Controlling for Non-Consent Bias in Linked Survey and Administrative Data, Joseph W. Sakshaug, University of Manchester, United Kingdom, and Institute for Employment Research, Nuremberg, Germany

7.1. Introduction

7.2. Strategies for Measuring Linkage Non-Consent Bias

7.3. Methods for Minimizing Non-Consent Bias at the Survey Design Stage

7.4. Methods for Minimizing Non-Consent Bias at the Survey Analysis Stage

7.5. Summary

7.6. Practical implications for implementation with surveys and censuses

7.7. Exercises

7.8. References

Section 3: Use of Administrative Records in Surveys

8. A Register-Based Census: The Swedish Experiences, Martin Axelson, Anders Holmberg, Ingegerd Jansson, and Sara Westling, Statistics Sweden

8.1. Introduction

8.2. Background

8.3. Census 2011

8.4. A register based census

8.5. Evaluation of the census

8.6. Impact on population and housing statistics

8.7. Summary and final remarks

8.8. References

9. Administrative Records Applications for the 2020 Census, Vincent Tom Mule, Jr., Andrew Keller, U.S. Census Bureau

9.1. Introduction

9.2. Administrative Record Usage in the United States Census

9.3. Administrative Record Integration in 2020 Census Research

9.4. Quality Assessment

9.5. Other Applications of Administrative Record Usage

9.6. Summary

9.7. Exercises

9.8. References

10. Use of Administrative Records in Small Area Estimation, Andrea L. Erciulescu, National Institute of Statistical Sciences, Carolina Franco, U.S. Census Bureau, Partha Lahiri, University of Maryland

10.1. Introduction

10.2. Data Preparation

10.3. Small area estimation models for combining information

10.4. An Application

10.5. Concluding Remarks

10.6. Exercises

10.7. Acknowledgments

10.8. References

11. Using Administrative Records to Control for Nonresponse Bias, Asaph Young Chun, Statistics Korea

Section 4: Use of Administrative Data in Evidence-Based Policymaking

12. Enhancement of Health Surveys with Data Linkage, Cordell Golden, Lisa B. Mirel, NCHS

12.1. Introduction

12.2. Examples of NCHS health surveys that were enhanced through linkage

12.3. NCHS health surveys linked with vital records and administrative data

12.4. NCHS Data Linkage Program: Linkage Methodology and Processing Issues

12.5. Enhancements to health survey data through linkage

12.6. Analytic considerations and limitations of administrative data

12.7. Future of the NCHS Data Linkage Program

12.8. Exercises

12.9. Acknowledgments and Disclaimer

12.10. References

13. Combining Administrative and Survey Data to Improve Income Measurement, Bruce D. Meyer, University of Chicago, and Nikolas Mittag, Charles University

13.1. Introduction

13.2. Measuring and Decomposing Total Survey Error

13.3. Representation Error

13.4. Item Non-response and Imputation Error

13.5. Measurement Error

13.6. Illustration: Using Data Linkage to Better Measure Income and Poverty

13.7. Accuracy of Links and the Administrative Data

13.8. Conclusions

13.9. Study Problems

13.10. References

14. Combining Data from Multiple Sources to Define a Respondent: The Case of Education Data, Peter Siegel, Darryl Creel, James Chromy, RTI International

14.1. Introduction

14.2. Literature Review

14.3. Methodology

14.4. Example of Defining a Unit Respondent for the National Postsecondary Student Aid Study (NPSAS)

14.5. Discussion: Advantages and disadvantages of two approaches to defining a unit respondent

14.6. Practical Implications for Implementation with Surveys and Censuses

14.7. References

14.8. Appendix: NPSAS:08 Study Respondent Definition
Asaph Young Chun, PhD, is Director-General, Statistics Research Institute, Statistics Korea, Republic of Korea.

Michael D. Larsen, PhD, is Professor and Chair, Department of Mathematics and Statistics, Saint Michael's College, Vermont, USA.

Gabriele Durrant, PhD, is Professor, Department of Social Statistics and Demography, University of Southampton, UK.

Jerome P. Reiter, PhD, is Professor and Chair, Department of Statistical Science, Duke University, North Carolina, USA.

A. Y. Chun, Statistics Research Institute, Korea; M. D. Larsen, Saint Michael's College, United States; G. Durrant, Southampton University, UK; J. P. Reiter, Duke University, United States