Administrative Records for Survey Methodology

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 1.1.1. A multisource data perspective 1.1.2. Concept of a proxy variable 1.2. Instances of proxy variable 1.2.1. Representation 1.2.2. Measurement 1.3. Estimation using multiple proxy variables 1.3.1. Asymmetric setting 1.3.2. Uncertainty evaluation: a case of two-way data 1.3.3. Symmetric setting 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 2.2.1. Input noise infusion 2.2.2. Formal privacy models 2.3. Confidentiality protection in linked data: Examples 2.3.1. HRS-SSA 2.3.2. SIPP-SSA-IRS (SSB) 2.3.3. LEHD: Linked establishment and employee records 2.4. Physical and legal protections 2.4.1. Statistical data enclaves 2.4.2. Remote processing 2.4.3. Licensing 2.4.4. Disclosure avoidance methods 2.4.5. Data silos 2.5. Conclusions 2.6. References 2.7. Appendix: Technical Terms and Acronyms 2.7.1. Data 2.7.2. Other Abbreviations 2.7.3. Concepts 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 3.3.1. Source and Metadata hyper dimensions 3.3.2. Data hyper dimension 3.4. Quality evaluation results for the Dutch 2011 Census 3.4.1. Source and Metadata: application of checklist 3.4.2. Data hyper dimension: completeness and accuracy results 3.4.3. Discussion of the quality findings 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 4.5.1. Agreements on data processing 4.5.2. Agreements on cooperation on data quality in administrative data systems The forums for cooperation 4.6. Measuring and documenting input data quality 4.6.1. Quality indicators 4.6.2. Operationalizing the quality checks 4.6.3. Quality reports 4.6.4. The approach is being adopted by the owners of administrative data 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 5.1.1. Example 1 5.1.2. Example 2 5.1.3. Example 3 5.2. Edit/Imputation 5.2.1. Background 5.2.2. Fellegi-Holt Model 5.2.3. Imputation Generalizing Little-Rubin 5.2.4. Connecting Edit with Imputation 5.2.5. Achieving Extreme Computational Speed 5.3. Record Linkage 5.3.1. Fellegi-Sunter Model 5.3.2. Estimating Parameters 5.3.3. Estimating False Match Rates 5.3.4. Achieving Extreme Computational Speed 5.4. Models for Adjusting Statistical Analyses for Linkage Error 5.4.1. Scheuren and Winkler 5.4.2. Lahiri and Larsen 5.4.3. Chambers and Kim 5.4.4. Chippenfield, Bishop, and Campbel 5.4.5. Goldstein, Harron, and Wade 5.4.6. Hof and Zwinderman 5.4.7. Trancredi and Liseo 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 6.2.1. Imperfect matching 6.2.2. Incomplete matching 6.3. Approaches to accounting for uncertainty 6.3.1. Modeling matching matrix as parameter 6.3.2. Direct modeling 6.3.3. Imputation of entire concatenated file 6.4. Concluding Remarks 6.4.1. Problems to be solved 6.4.2. Practical implications 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 7.1.1. What is Linkage Consent? Why is Linkage Consent Needed? 7.1.2. Linkage Consent Rates in Large-Scale Surveys 7.1.3. The impact of Linkage Non-Consent Bias on Survey Inference 7.1.4. The Challenge of Measuring and Controlling for Linkage Non-Consent Bias 7.2. Strategies for Measuring Linkage Non-Consent Bias 7.2.1. Formulation of Linkage Non-Consent Bias 7.2.2. Modeling Non-Consent Using Survey Information 7.2.3. Analyzing Non-Consent Bias for Administrative Variables 7.3. Methods for Minimizing Non-Consent Bias at the Survey Design Stage 7.3.1. Optimizing Linkage Consent Rates 7.3.2. Placement of the Consent Request 7.3.3. Wording of the Consent Request 7.3.4. Active and Passive Consent Procedures 7.3.5. Linkage Consent in Panel Studies 7.4. Methods for Minimizing Non-Consent Bias at the Survey Analysis Stage 7.4.1. Controlling for Linkage Non-Consent Bias via Statistical Adjustment 7.4.2. Weighting Adjustments 7.4.3. Imputation 7.5. Summary 7.5.1. Key Points for Measuring Linkage Non-Consent Bias 7.5.2. Key Points for Controlling Linkage Non-Consent Bias 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 8.4.1. Registers at Statistics Sweden 8.4.2. Facilitating a system of registers 8.4.3. Introducing a dwelling identification key 8.4.4. The census household and dwelling populations 8.5. Evaluation of the census 8.5.1. Introduction 8.5.2. Evaluating household size and type 8.5.3. Evaluating ownership 8.5.4. Lessons learned 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 9.3.1. Administrative Record Usage Determinations 9.3.2. NRFU Design Incorporating Administrative Records 9.3.3. Administrative Records Sources and Data Preparation 9.3.4. Approach to Determine Administrative Record Vacant Addresses 9.3.5. Extension of Vacant Methodology to Non-Existent Cases 9.3.6. Approach to Determine Occupied Addresses 9.3.7. Other Aspects and Alternatives of Administrative Record Enumeration 9.4. Quality Assessment 9.4.1. Micro-Level Evaluations of Quality 9.4.2. Macro-Level Evaluations of Quality 9.5. Other Applications of Administrative Record Usage 9.5.1. Register-Based Census 9.5.2. Supplement Traditional Enumeration with Adjustments for Estimated Error for Official Census Counts 9.5.3. Coverage Evaluation 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 10.3.1. Area-level models 10.3.2. Unit-level models 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 12.1.1. The National Center for Health Statistics (NCHS) 12.1.2. The NCHS Data Linkage Program 12.1.3. Initial Linkages with NCHS Surveys 12.2. Examples of NCHS health surveys that were enhanced through linkage 12.2.1. National Health Interview Survey (NHIS) 12.2.2. National Health and Nutrition Examination Survey (NHANES) 12.2.3. National Health Care Surveys 12.3. NCHS health surveys linked with vital records and administrative data 12.3.1. National Death Index (NDI) 12.3.2. Centers for Medicare & Medicaid Services (CMS) 12.3.3. Social Security Administration (SSA) 12.3.4. Department of Housing and Urban Development (HUD) 12.3.5. United States Renal Data System and the Florida Cancer Data System 12.4. NCHS Data Linkage Program: Linkage Methodology and Processing Issues 12.4.1. Informed consent in health surveys 12.4.2. Informed consent for child survey participants 12.4.3. Adaptive approaches to linking health surveys with administrative data 12.4.4. Use of alternate records 12.4.5. Protecting the privacy of health survey participants and maintaining data confidentiality 12.4.6. Updates over time 12.5. Enhancements to health survey data through linkage 12.6. Analytic considerations and limitations of administrative data 12.6.1. Adjusting sample weights for linkage-eligibility 12.6.2. Residential mobility and linkages to state programs and registries 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 14.1.1. Options for defining a unit respondent when data exist from sources instead of or in addition to an interview 14.1.2. Concerns with defining a unit respondent without having an interview 14.2. Literature Review 14.3. Methodology 14.3.1. Computing weights for interview respondents and for unit respondents who may not have interview data (useable case respondents) 14.3.2. Imputing data when all or some interview data are missing 14.3.3. Conducting nonresponse bias analyses to appropriately consider interview and study nonresponse 14.4. Example of Defining a Unit Respondent for the National Postsecondary Student Aid Study (NPSAS) 14.4.1. Overview of NPSAS 14.4.2. Useable case respondent approach 14.4.3. Interview respondent approach 14.4.4. Comparison of estimates, variances, and nonresponse bias using two approaches to define a unit respondent 14.5. Discussion: Advantages and disadvantages of two approaches to defining a unit respondent 14.5.1. Interview respondents 14.5.2. Useable case respondents 14.6. Practical Implications for Implementation with Surveys and Censuses 14.7. References 14.8. Appendix: NPSAS:08 Study Respondent Definition