Book description
Featuring a timely presentation of total survey error (TSE), this edited volume introduces valuable tools for understanding and improving survey data quality in the context of evolving large-scale data sets
This book provides an overview of the TSE framework and current TSE research as related to survey design, data collection, estimation, and analysis. It recognizes that survey data affects many public policy and business decisions and thus focuses on the framework for understanding and improving survey data quality. The book also addresses issues with data quality in official statistics and in social, opinion, and market research as these fields continue to evolve, leading to larger and messier data sets. This perspective challenges survey organizations to find ways to collect and process data more efficiently without sacrificing quality. The volume consists of the most up-to-date research and reporting from over 70 contributors representing the best academics and researchers from a range of fields. The chapters are broken out into five main sections: The Concept of TSE and the TSE Paradigm, Implications for Survey Design, Data Collection and Data Processing Applications, Evaluation and Improvement, and Estimation and Analysis. Each chapter introduces and examines multiple error sources, such as sampling error, measurement error, and nonresponse error, which often offer the greatest risks to data quality, while also encouraging readers not to lose sight of the less commonly studied error sources, such as coverage error, processing error, and specification error. The book also notes the relationships between errors and the ways in which efforts to reduce one type can increase another, resulting in an estimate with larger total error.
This book:
• Features various error sources, and the complex relationships between them, in 25 high-quality chapters on the most up-to-date research in the field of TSE
• Provides comprehensive reviews of the literature on error sources as well as data collection approaches and estimation methods to reduce their effects
• Presents examples of recent international events that demonstrate the effects of data error, the importance of survey data quality, and the real-world issues that arise from these errors
• Spans the four pillars of the total survey error paradigm (design, data collection, evaluation and analysis) to address key data quality issues in official statistics and survey research
Total Survey Error in Practice is a reference for survey researchers and data scientists in research areas that include social science, public opinion, public policy, and business. It can also be used as a textbook or supplementary material for a graduate-level course in survey research methods.
Paul P. Biemer, PhD, is distinguished fellow at RTI International and associate director of Survey Research and Development at the Odum Institute, University of North Carolina, USA.
Edith de Leeuw, PhD, is professor of survey methodology in the Department of Methodology and Statistics at Utrecht University, the Netherlands.
Stephanie Eckman, PhD, is fellow at RTI International, USA.
Brad Edwards is vice president, director of Field Services, and deputy area director at Westat, USA.
Frauke Kreuter, PhD, is professor and director of the Joint Program in Survey Methodology, University of Maryland, USA; professor of statistics and methodology at the University of Mannheim, Germany; and head of the Statistical Methods Research Department at the Institute for Employment Research, Germany.
Lars E. Lyberg, PhD, is senior advisor at Inizio, Sweden.
N. Clyde Tucker, PhD, is principal survey methodologist at the American Institutes for Research, USA.
Brady T. West, PhD, is research associate professor in the Survey Resea
Table of contents
- Cover
- Title Page
- Notes on Contributors
- Preface
-
Section 1: The Concept of TSE and the TSE Paradigm
- 1 The Roots and Evolution of the Total Survey Error Concept
- 2 Total Twitter Error
-
3 Big Data
- 3.1 Introduction
- 3.2 Definitions
- 3.3 The Analytic Challenge: From Database Marketing to Big Data and Data Science
- 3.4 Assessing Data Quality
- 3.5 Applications in Market, Opinion, and Social Research
- 3.6 The Ethics of Research Using Big Data
- 3.7 The Future of Surveys in a Data‐Rich Environment
- References
- 4 The Role of Statistical Disclosure Limitation in Total Survey Error
-
Section 2: Implications for Survey Design
- 5 The Undercoverage–Nonresponse Tradeoff
- 6 Mixing Modes
- 7 Mobile Web Surveys
- 8 The Effects of a Mid‐Data Collection Change in Financial Incentives on Total Survey Error in the National Survey of Family Growth
- 9 A Total Survey Error Perspective on Surveys in Multinational, Multiregional, and Multicultural Contexts
-
10 Smartphone Participation in Web Surveys
- 10.1 Introduction
- 10.2 Prevalence of Smartphone Participation in Web Surveys
- 10.3 Smartphone Participation Choices
- 10.4 Instrument Design Choices
- 10.5 Device and Design Treatment Choices
- 10.6 Conclusion
- 10.7 Future Challenges and Research Needs
- Appendix 10.A: Data Sources
- Appendix 10.B: Smartphone Prevalence in Web Surveys
- Appendix 10.C: Screen Captures from Peterson et al. (2013) Experiment
- Appendix 10.D: Survey Questions Used in the Analysis of the Peterson et al. (2013) Experiment
- References
- 11 Survey Research and the Quality of Survey Data Among Ethnic Minorities
-
Section 3: Data Collection and Data Processing Applications
-
12 Measurement Error in Survey Operations Management
- 12.1 TSE Background on Survey Operations
- 12.2 Better and Better: Using Behavior Coding (CARIcode) and Paradata to Evaluate and Improve Question (Specification) Error and Interviewer Error
- 12.3 Field‐Centered Design: Mobile App for Rapid Reporting and Management
- 12.4 Faster and Cheaper: Detecting Falsification With GIS Tools
- 12.5 Putting It All Together: Field Supervisor Dashboards
- 12.6 Discussion
- References
- 13 Total Survey Error for Longitudinal Surveys
- 14 Text Interviews on Mobile Devices
- 15 Quantifying Measurement Errors in Partially Edited Business Survey Data
-
12 Measurement Error in Survey Operations Management
-
Section 4: Evaluation and Improvement
- 16 Estimating Error Rates in an Administrative Register and Survey Questions Using a Latent Class Model
- 17 ASPIRE
-
18 Classification Error in Crime Victimization Surveys
- 18.1 Introduction
- 18.2 Background
- 18.3 Analytic Approach
- 18.4 Model Selection
- 18.5 Results
- 18.6 Discussion and Summary of Findings
- 18.7 Conclusions
- Appendix 18.A: Derivation of the Composite False‐Negative Rate
- Appendix 18.B: Derivation of the Lower Bound for False‐Negative Rates from a Composite Measure
- Appendix 18.C: Examples of Latent GOLD Syntax
- References
- 19 Using Doorstep Concerns Data to Evaluate and Correct for Nonresponse Error in a Longitudinal Survey
- 20 Total Survey Error Assessment for Sociodemographic Subgroups in the 2012 U.S. National Immunization Survey
-
21 Establishing Infrastructure for the Use of Big Data to Understand Total Survey Error
- Overview
- Part 1 Big Data Infrastructure at the Institute for Employment Research (IAB)
- References
- Part 2 Using Administrative Records Data at the U.S. Census Bureau: Lessons Learned from Two Research Projects Evaluating Survey Data
- References
- Part 3 Statistics New Zealand’s Approach to Making Use of Alternative Data Sources in a New Era of Integrated Data
- References
- Part 4 Big Data Serving Survey Research: Experiences at the University of Michigan Survey Research Center
- References
- Section 5: Estimation and Analysis
- Wiley Series in Survey Methodology
- Index
- End User License Agreement
Product information
- Title: Total Survey Error in Practice
- Author(s):
- Release date: February 2017
- Publisher(s): Wiley
- ISBN: 9781119041672
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