Atjaunināt sīkdatņu piekrišanu

E-grāmata: Administrative Records for Survey Methodology

Edited by (Saint Michael's College, United States), Edited by (Southampton University, UK), Edited by (Duke University, United States), Edited by (Statistics Research Institute, Korea)
Citas grāmatas par šo tēmu:
  • Formāts - EPUB+DRM
  • Cena: 118,91 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
  • Bibliotēkām
Administrative Records for Survey MethodologyPalielināt
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

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 issuesincluding concerns of privacy, confidentiality, and legalityinvolved 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 books 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.
Preface xv
Acknowledgments xxi
List of Contributors
xxiii
Part I Fundamentals of Administrative Records Research and Applications
1(60)
1 On The Use Of Proxy Variables In Combining Register And Survey Data
3(22)
Li-Chun Zhang
1.1 Introduction
3(4)
1.1.1 A Multisource Data Perspective
3(2)
1.1.2 Concept of Proxy Variable
5(2)
1.2 Instances of Proxy Variable
7(5)
1.2.1 Representation
7(3)
1.2.2 Measurement
10(2)
1.3 Estimation Using Multiple Proxy Variables
12(8)
1.3.1 Asymmetric Setting
13(2)
1.3.2 Uncertainty Evaluation: A Case of Two-Way Data
15(2)
1.3.3 Symmetric Setting
17(3)
1.4 Summary
20(5)
References
20(5)
2 Disclosure Limitation And Confidentiality Protection In Linked Data
25(36)
John M. Abowd
Ian M. Schmutte
Lars Vilhuber
2.1 Introduction
25(2)
2.2 Paradigms of Protection
27(5)
2.2.1 Input Noise Infusion
29(1)
2.2.2 Formal Privacy Models
30(2)
2.3 Confidentiality Protection in Linked Data: Examples
32(11)
2.3.1 HRS-SSA
32(1)
2.3.1.1 Data Description
32(1)
2.3.1.2 Linkages to Other Data
32(1)
2.3.1.3 Disclosure Avoidance Methods
33(1)
2.3.2 SIPP-SSA-IRS (SSB)
34(1)
2.3.2.1 Data Description
34(1)
2.3.2.2 Disclosure Avoidance Methods
35(1)
2.3.2.3 Disclosure Avoidance Assessment
35(2)
2.3.2.4 Analytical Validity Assessment
37(1)
2.3.3 LEHD: Linked Establishment and Employee Records
38(1)
2.3.3.1 Data Description
38(1)
2.3.3.2 Disclosure Avoidance Methods
39(2)
2.3.3.3 Disclosure Avoidance Assessment for QWI
41(1)
2.3.3.4 Analytical Validity Assessment for QWI
42(1)
2.4 Physical and Legal Protections
43(6)
2.4.1 Statistical Data Enclaves
44(2)
2.4.2 Remote Processing
46(1)
2.4.3 Licensing
46(1)
2.4.4 Disclosure Avoidance Methods
47(1)
2.4.5 Data Silos
48(1)
2.5 Conclusions
49(12)
2.A.1 Other Abbreviations
51(1)
2.A.2 Concepts
52(2)
Acknowledgments
54(1)
References
54(7)
Part II Data Quality of Administrative Records and Linking Methodology
61(118)
3 Evaluation Of The Quality Of Administrative Data Used In The Dutch Virtual Census
63(22)
Piet Daas
Eric S. Nordholt
Martijn Tennekes
Saskia Ossen
3.1 Introduction
63(1)
3.2 Data Sources and Variables
64(2)
3.3 Quality Framework
66(3)
3.3.1 Source and Metadata Hyper Dimensions
66(2)
3.3.2 Data Hyper Dimension
68(1)
3.4 Quality Evaluation Results for the Dutch 2011 Census
69(12)
3.4.1 Source and Metadata: Application of Checklist
69(3)
3.4.2 Data Hyper Dimension: Completeness and Accuracy Results
72(1)
3.4.2.1 Completeness Dimension
73(2)
3.4.2.2 Accuracy Dimension
75(3)
3.4.2.3 Visualizing with a Tableplot
78(2)
3.4.3 Discussion of the Quality Findings
80(1)
3.5 Summary
81(1)
3.6 Practical Implications for Implementation with Surveys and Censuses
81(1)
3.7 Exercises
82(3)
References
82(3)
4 Improving Input Data Quality In Register-Based Statistics: The Norwegian Experience
85(20)
Coen Hendriks
4.1 Introduction
85(1)
4.2 The Use of Administrative Sources in Statistics Norway
86(3)
4.3 Managing Statistical Populations
89(2)
4.4 Experiences from the First Norwegian Purely Register-Based Population and Housing Census of 2011
91(2)
4.5 The Contact with the Owners of Administrative Registers Was Put into System
93(3)
4.5.1 Agreements on Data Processing
93(2)
4.5.2 Agreements of Cooperation on Data Quality in Administrative Data Systems
95(1)
4.5.3 The Forums for Cooperation
96(1)
4.6 Measuring and Documenting Input Data Quality
96(4)
4.6.1 Quality Indicators
96(1)
4.6.2 Operationalizing the Quality Checks
97(2)
4.6.3 Quality Reports
99(1)
4.6.4 The Approach Is Being Adopted by the Owners of Administrative Data
99(1)
4.7 Summary
100(1)
4.8 Exercises
101(4)
References
104(1)
5 Cleaning And Using Administrative Lists: Enhanced Practices And Computational Algorithms For Record Linkage And Modeling/Editing/Imputation
105(34)
William E. Winkler
5.1 Introductory Comments
105(3)
5.1.1 Example 1
105(1)
5.1.2 Example 2
106(1)
5.1.3 Example 3
107(1)
5.2 Edit/Imputation
108(5)
5.2.1 Background
108(2)
5.2.2 Fellegi--Holt Model
110(1)
5.2.3 Imputation Generalizing Little-Rubin
110(1)
5.2.4 Connecting Edit with Imputation
111(1)
5.2.5 Achieving Extreme Computational Speed
112(1)
5.3 Record Linkage
113(11)
5.3.1 Fellegi--Sunter Model
113(3)
5.3.2 Estimating Parameters
116(2)
5.3.3 Estimating False Match Rates
118(1)
5.3.3.1 The Data Files
118(5)
5.3.4 Achieving Extreme Computational Speed
123(1)
5.4 Models for Adjusting Statistical Analyses for Linkage Error
124(9)
5.4.1 Scheuren--Winkler
124(1)
5.4.2 Lahiri--Larsen
125(2)
5.4.3 Chambers and Kim
127(1)
5.4.4 Chipperfield, Bishop, and Campbell
128(2)
5.4.4.1 Empirical Data
130(2)
5.4.5 Goldstein, Harron, and Wade
132(1)
5.4.6 Hof and Zwinderman
133(1)
5.4.7 Tancredi and Liseo
133(1)
5.5 Concluding Remarks
133(1)
5.6 Issues and Some Related Questions
134(5)
References
134(5)
6 Assessing Uncertainty When Using Linked Administrative Records
139(16)
Jerome P. Reiter
6.1 Introduction
139(1)
6.2 General Sources of Uncertainty
140(2)
6.2.1 Imperfect Matching
140(1)
6.2.2 Incomplete Matching
141(1)
6.3 Approaches to Accounting for Uncertainty
142(7)
6.3.1 Modeling Matching Matrix as Parameter
143(3)
6.3.2 Direct Modeling
146(2)
6.3.3 Imputation of Entire Concatenated File
148(1)
6.4 Concluding Remarks
149(101)
6.4.1 Problems to Be Solved
149(1)
6.4.2 Practical Implications
150(1)
6.5 Exercises
150(1)
Acknowledgment
151(1)
References
151(4)
7 Measuring And Controlling For Non-Consent Bias In Linked Survey And Administrative Data
155(1)
Joseph W. Sakshaug
7.1 Introduction
155(4)
7.1.1 What Is Linkage Consent? Why Is Linkage Consent Needed?
155(1)
7.1.2 Linkage Consent Rates in Large-Scale Surveys
156(2)
7.1.3 The Impact of Linkage Non-Consent Bias on Survey Inference
158(1)
7.1.4 The Challenge of Measuring and Controlling for Linkage Non-Consent Bias
158(1)
1.2 Strategies for Measuring Linkage Non-Consent Bias
159(4)
7.2.1 Formulation of Linkage Non-Consent Bias
159(1)
1.2.2 Modeling Non-Consent Using Survey Information
160(2)
7.2.3 Analyzing Non-Consent Bias for Administrative Variables
162(1)
7.3 Methods for Minimizing Non-Consent Bias at the Survey Design Stage
163(5)
7.3.1 Optimizing Linkage Consent Rates
163(1)
7.3.2 Placement of the Consent Request
163(2)
7.3.3 Wording of the Consent Request
165(1)
7.3.4 Active and Passive Consent Procedures
166(1)
7.3.5 Linkage Consent in Panel Studies
167(1)
7.4 Methods for Minimizing Non-Consent Bias at the Survey Analysis Stage
168(4)
7.4.1 Controlling for Linkage Non-Consent Bias via Statistical Adjustment
169(1)
7.4.2 Weighting Adjustments
169(1)
7.4.3 Imputation
170(2)
7.5 Summary
172(1)
7.5.1 Key Points for Measuring Linkage Non-Consent Bias
172(1)
7.5.2 Key Points for Controlling for Linkage Non-Consent Bias
172(1)
7.6 Practical Implications for Implementation with Surveys and Censuses
173(1)
1.1 Exercises
174(5)
References
174(5)
Part III Use of Administrative Records in Surveys
179(90)
8 A Register-Based Census: The Swedish Experience
181(24)
Martin Axelson
Anders Holmberg
Ingegerd Jansson
Sara Westling
8.1 Introduction
181(1)
8.2 Background
182(1)
8.3 Census 2011
183(2)
8.4 A Register-Based Census
185(5)
8.4.1 Registers at Statistics Sweden
185(1)
8.4.2 Facilitating a System of Registers
186(1)
8.4.3 Introducing a Dwelling Identification Key
187(1)
8.4.4 The Census Household and Dwelling Populations
188(2)
8.5 Evaluation of the Census
190(9)
8.5.1 Introduction
190(2)
8.5.2 Evaluating Household Size and Type
192(1)
8.5.2.1 Sampling Design
192(1)
8.5.2.2 Data Collection
193(1)
8.5.2.3 Reconciliation
194(1)
8.5.2.4 Results
194(1)
8.5.3 Evaluating Ownership
195(3)
8.5.4 Lessons Learned
198(1)
8.6 Impact on Population and Housing Statistics
199(2)
8.7 Summary and Final Remarks
201(4)
References
203(2)
9 Administrative Records Applications For The 2020 Census
205(26)
Vincent T. Mule Jr.
Andrew Keller
9.1 Introduction
205(1)
9.2 Administrative Record Usage in the U.S. Census
206(1)
9.3 Administrative Record Integration in 2020 Census Research
207(12)
9.3.1 Administrative Record Usage Determinations
207(1)
9.3.2 NRFU Design Incorporating Administrative Records
208(2)
9.3.3 Administrative Records Sources and Data Preparation
210(2)
9.3.4 Approach to Determine Administrative Record Vacant Addresses
212(2)
9.3.5 Extension of Vacant Methodology to Nonexistent Cases
214(1)
9.3.6 Approach to Determine Occupied Addresses
215(2)
9.3.7 Other Aspects and Alternatives of Administrative Record Enumeration
217(2)
9.4 Quality Assessment
219(5)
9.4.1 Microlevel Evaluations of Quality
219(2)
9.4.2 Macrolevel Evaluations of Quality
221(3)
9.5 Other Applications of Administrative Record Usage
224(2)
9.5.1 Register-Based Census
224(1)
9.5.2 Supplement Traditional Enumeration with Adjustments for Estimated Error for Official Census Counts
224(1)
9.5.3 Coverage Evaluation
225(1)
9.6 Summary
226(1)
9.7 Exercises
227(4)
References
228(3)
10 Use Of Administrative Records In Small Area Estimation
231(38)
Andreea L. Erciulescu
Carolina Franco
Partha Lahiri
10.1 Introduction
231(2)
10.2 Data Preparation
233(5)
10.3 Small Area Estimation Models for Combining Information
238(14)
10.3.1 Area-level Models
238(9)
10.3.2 Unit-level Models
247(5)
10.4 An Application
252(7)
10.5 Concluding Remarks
259(1)
10.6 Exercises
259(10)
Acknowledgments
261(1)
References
261(8)
Part IV Use of Administrative Data in Evidence-Based Policymaking
269(80)
11 Enhancement Of Health Surveys With Data Linkage
271(26)
Cordell Golden
Lisa B. Mirel
11.1 Introduction
271(2)
11.1.1 The National Center for Health Statistics (NCHS)
271(1)
11.1.2 The NCHS Data Linkage Program
272(1)
11.1.3 Initial Linkages with NCHS Surveys
272(1)
11.2 Examples of NCHS Health Surveys that Were Enhanced Through Linkage
273(2)
11.2.1 National Health Interview Survey (NHIS)
273(1)
11.2.2 National Health and Nutrition Examination Survey (NHANES)
274(1)
11.2.3 National Health Care Surveys
274(1)
11.3 NCHS Health Surveys Linked with Vital Records and Administrative Data
275(3)
11.3.1 National Death Index (NDI)
276(1)
11.3.2 Centers for Medicare and Medicaid Services (CMS)
276(1)
11.3.3 Social Security Administration (SSA)
277(1)
11.3.4 Department of Housing and Urban Development (HUD)
277(1)
11.3.5 United States Renal Data System and the Florida Cancer Data System
278(1)
11.4 NCHS Data Linkage Program: Linkage Methodology and Processing Issues
278(6)
11.4.1 Informed Consent in Health Surveys
278(1)
11.4.2 Informed Consent for Child Survey Participants
279(1)
11.4.3 Adaptive Approaches to Linking Health Surveys with Administrative Data
280(1)
11.4.4 Use of Alternate Records
281(1)
11.4.5 Protecting the Privacy of Health Survey Participants and Maintaining Data Confidentiality
282(1)
11.4.6 Updates Over Time
283(1)
11.5 Enhancements to Health Survey Data Through Linkage
284(2)
11.6 Analytic Considerations and Limitations of Administrative Data
286(3)
11.6.1 Adjusting Sample Weights for Linkage-Eligibility
287(1)
11.6.2 Residential Mobility and Linkages to State Programs and Registries
288(1)
11.7 Future of the NCHS Data Linkage Program
289(2)
11.8 Exercises
291(6)
Acknowledgments
292(1)
Disclaimer
292(1)
References
292(5)
12 Combining Administrative And Survey Data To Improve Income Measurement
297(26)
Bruce D. Meyer
Nikolas Mittag
12.1 Introduction
297(2)
12.2 Measuring and Decomposing Total Survey Error
299(3)
12.3 Generalized Coverage Error
302(3)
12.4 Item Nonresponse and Imputation Error
305(2)
12.5 Measurement Error
307(4)
12.6 Illustration: Using Data Linkage to Better Measure Income and Poverty
311(1)
12.7 Accuracy of Links and the Administrative Data
312(3)
12.8 Conclusions
315(1)
12.9 Exercises
316(7)
Acknowledgments
317(1)
References
317(6)
13 Combining Data From Multiple Sources To Define A Respondent: The Case Of Education Data
323(26)
Peter Siegel
Darryl Creel
James Chromy
13.1 Introduction
323(3)
13.1.1 Options for Defining a Unit Respondent When Data Exist from Sources Instead of or in Addition to an Interview
324(1)
13.1.2 Concerns with Defining a Unit Respondent Without Having an Interview
325(1)
13.2 Literature Review
326(1)
13.3 Methodology
327(3)
13.3.1 Computing Weights for Interview Respondents and for Unit Respondents Who May Not Have Interview Data (Usable Case Respondents)
327(1)
13.3.1.1 How Many Weights Are Necessary?
328(1)
13.3.2 Imputing Data When All or Some Interview Data Are Missing
328(1)
13.3.3 Conducting Nonresponse Bias Analyses to Appropriately Consider Interview and Study Nonresponse
329(1)
13.4 Example of Defining a Unit Respondent for the National Postsecondary Student Aid Study (NPSAS)
330(10)
13.4.1 Overview of NPSAS
330(3)
13.4.2 Usable Case Respondent Approach
333(1)
13.4.2.1 Results
333(2)
13.4.3 Interview Respondent Approach
335(1)
13.4.3.1 Results
336(2)
13.4.4 Comparison of Estimates, Variances, and Nonresponse Bias Using Two Approaches to Define a Unit Respondent
338(2)
13.5 Discussion: Advantages and Disadvantages of Two Approaches to Defining a Unit Respondent
340(2)
13.5.1 Interview Respondents
340(1)
13.5.2 Usable Case Respondents
341(1)
13.6 Practical Implications for Implementation with Surveys and Censuses
342(1)
13.A Appendix
343(1)
13.A.1 NPSAS:08 Study Respondent Definition
343(1)
13.B Appendix
343(6)
References
348(1)
Index 349
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 Michaels 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.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97811192720696e.html
Keywords: