Administrative records for survey methodology. (2021)

• Record Type: Book
• Further Information: Note: Asaph Young Chun, Michael D. Larsen.
• Authors: Chun, Asaph Young; Larsen, Michael D, 1977-
• Contents: 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
• Edition: 1st
• Publisher Details: Hoboken : John Wiley & Sons, Inc
• Publication Date: 2021
• Extent: 1 online resource
• Subjects: 001.433; Surveys -- Methodology; Surveys -- Quality control
• Languages: English
• ISBNs: 9781119272069; 9781119272052
• Related ISBNs: 9781119272045
• Notes: Note: Description based on CIP data; resource not viewed.
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