Total survey error in practice / edited by Paul P. Biemer [and seven others].

Format:

Book

Contributor:

Biemer, Paul P., editor.

Series:

Wiley series in survey methodology.

THEi Wiley ebooks.

Wiley Series in Survey Methodology

THEi Wiley ebooks

Language:

English

Subjects (All):

Error analysis (Mathematics).

Surveys.

Physical Description:

1 online resource (627 pages) : illustrations, tables.

Edition:

1st ed.

Place of Publication:

Hoboken, New Jersey : Wiley, 2017.

System Details:

Access using campus network via VPN at home (THEi Users Only).

Summary:

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.

Contents:

Intro

Title Page

Copyright Page

Contents

Notes on Contributors

Preface

Section 1 The Concept of TSE and the TSE Paradigm

Chapter 1 The Roots and Evolution of the Total Survey Error Concept

1.1 Introduction and Historical Backdrop

1.2 Specific Error Sources and Their Control or Evaluation

1.3 Survey Models and Total Survey Design

1.4 The Advent of More Systematic Approaches Toward Survey Quality

1.5 What the Future Will Bring

References

Chapter 2 Total Twitter Error: Decomposing Public Opinion Measurement on Twitter from a Total Survey Error Perspective

2.1 Introduction

2.1.1 Social Media: A Potential Alternative to Surveys?

2.1.2 TSE as a Launching Point for Evaluating Social Media Error

2.2 Social Media: An Evolving Online Public Sphere

2.2.1 Nature, Norms, and Usage Behaviors of Twitter

2.2.2 Research on Public Opinion on Twitter

2.3 Components of Twitter Error

2.3.1 Coverage Error

2.3.2 Query Error

2.3.3 Interpretation Error

2.3.4 The Deviation of Unstructured Data Errors from TSE

2.4 Studying Public Opinion on the Twittersphere and the Potential Error Sources of Twitter Data: Two Case Studies

2.4.1 Research Questions and Methodology of Twitter Data Analysis

2.4.2 Potential Coverage Error in Twitter Examples

2.4.3 Potential Query Error in Twitter Examples

2.4.3.1 Implications of Including or Excluding RTs for Error

2.4.3.2 Implications of Query Iterations for Error

2.4.4 Potential Interpretation Error in Twitter Examples

2.5 Discussion

2.5.1 A Framework That Better Describes Twitter Data Errors

2.5.2 Other Subclasses of Errors to Be Investigated

2.6 Conclusion

2.6.1 What Advice We Offer for Researchers and Research Consumers

2.6.2 Directions for Future Research

Chapter 3 Big Data: A Survey Research Perspective.

3.1 Introduction

3.2 Definitions

3.2.1 Sources

3.2.2 Attributes

3.2.2.1 Volume

3.2.2.2 Variety

3.2.2.3 Velocity

3.2.2.4 Veracity

3.2.2.5 Variability

3.2.2.6 Value

3.2.2.7 Visualization

3.2.3 The Making of Big Data

3.3 The Analytic Challenge: From Database Marketing to Big Data and Data Science

3.4 Assessing Data Quality

3.4.1 Validity

3.4.2 Missingness

3.4.3 Representation

3.5 Applications in Market, Opinion, and Social Research

3.5.1 Adding Value through Linkage

3.5.2 Combining Big Data and Surveys in Market Research

3.6 The Ethics of Research Using Big Data

3.7 The Future of Surveys in a Data-Rich Environment

Chapter 4 The Role of Statistical Disclosure Limitation in Total Survey Error

4.1 Introduction

4.2 Primer on SDL

4.3 TSE-Aware SDL

4.3.1 Additive Noise

4.3.2 Data Swapping

4.4 Edit-Respecting SDL

4.4.1 Simulation Experiment

4.4.2 A Deeper Issue

4.5 SDL-Aware TSE

4.6 Full Unification of Edit, Imputation, and SDL

4.7 ``Big Data´´ Issues

4.8 Conclusion

Acknowledgments

Section 2 Implications for Survey Design

Chapter 5 The Undercoverage-Nonresponse Tradeoff

5.1 Introduction

5.2 Examples of the Tradeoff

5.3 Simple Demonstration of the Tradeoff

5.4 Coverage and Response Propensities and Bias

5.5 Simulation Study of Rates and Bias

5.5.1 Simulation Setup

5.5.2 Results for Coverage and Response Rates

5.5.3 Results for Undercoverage and Nonresponse Bias

5.5.3.1 Scenario 1

5.5.3.2 Scenario 2

5.5.3.3 Scenario 3

5.5.3.4 Scenario 4

5.5.3.5 Scenario 7

5.5.4 Summary of Simulation Results

5.6 Costs

5.7 Lessons for Survey Practice

Chapter 6 Mixing Modes: Tradeoffs Among Coverage, Nonresponse, and Measurement Error Roger Tourangeau

6.1 Introduction.

6.2 The Effect of Offering a Choice of Modes

6.3 Getting People to Respond Online

6.4 Sequencing Different Modes of Data Collection

6.5 Separating the Effects of Mode on Selection and Reporting

6.5.1 Conceptualizing Mode Effects

6.5.2 Separating Observation from Nonobservation Error

6.5.2.1 Direct Assessment of Measurement Errors

6.5.2.2 Statistical Adjustments

6.5.2.3 Modeling Measurement Error

6.6 Maximizing Comparability Versus Minimizing Error

6.7 Conclusions

Chapter 7 Mobile Web Surveys: A Total Survey Error Perspective

7.1 Introduction

7.2 Coverage

7.3 Nonresponse

7.3.1 Unit Nonresponse

7.3.2 Breakoffs

7.3.3 Completion Times

7.3.4 Compliance with Special Requests

7.4 Measurement Error

7.4.1 Grouping of Questions

7.4.1.1 Question-Order Effects

7.4.1.2 Number of Items on a Page

7.4.1.3 Grids versus Item-By-Item

7.4.2 Effects of Question Type

7.4.2.1 Socially Undesirable Questions

7.4.2.2 Open-Ended Questions

7.4.3 Response and Scale Effects

7.4.3.1 Primacy Effects

7.4.3.2 Slider Bars and Drop-Down Questions

7.4.3.3 Scale Orientation

7.4.4 Item Missing Data

7.5 Links Between Different Error Sources

7.6 The Future of Mobile web Surveys

Chapter 8 The Effects of a Mid-Data Collection Change in Financial Incentives on Total Survey Error in the National Survey of Famil...

8.1 Introduction

8.2 Literature Review: Incentives in Face-to-Face Surveys

8.2.1 Nonresponse Rates

8.2.2 Nonresponse Bias

8.2.3 Measurement Error

8.2.4 Survey Costs

8.2.5 Summary

8.3 Data and Methods

8.3.1 NSFG Design: Overview

8.3.2 Design of Incentive Experiment

8.3.3 Variables

8.3.4 Statistical Analysis

8.4 Results

8.4.1 Nonresponse Error

8.4.2 Sampling Error and Costs

8.4.3 Measurement Error.

8.5 Conclusion

8.5.1 Summary

8.5.2 Recommendations for Practice

Chapter 9 A Total Survey Error Perspective on Surveys in Multinational, Multiregional, and Multicultural Contexts

9.1 Introduction

9.2 TSE in Multinational, Multiregional, and Multicultural Surveys

9.3 Challenges Related to Representation and Measurement Error Components in Comparative Surveys

9.3.1 Representation Error

9.3.1.1 Coverage Error

9.3.1.2 Sampling Error

9.3.1.3 Unit Nonresponse Error

9.3.1.4 Adjustment Error

9.3.2 Measurement Error

9.3.2.1 Validity

9.3.2.2 Measurement Error - The Response Process

9.3.2.3 Processing Error

9.4 QA and QC in 3MC Surveys

9.4.1 The Importance of a Solid Infrastructure

9.4.2 Examples of QA and QC Approaches Practiced by Some 3MC Surveys

9.4.3 QA/QC Recommendations

Chapter 10 Smartphone Participation in Web Surveys: Choosing Between the Potential for Coverage, Nonresponse, and Measurement Error

10.1 Introduction

10.1.1 Focus on Smartphones

10.1.2 Smartphone Participation: Web-Survey Design Decision Tree

10.1.3 Chapter Outline

10.2 Prevalence of Smartphone Participation in Web Surveys

10.3 Smartphone Participation Choices

10.3.1 Disallowing Smartphone Participation

10.3.2 Discouraging Smartphone Participation

10.4 Instrument Design Choices

10.4.1 Doing Nothing

10.4.2 Optimizing for Smartphones

10.5 Device and Design Treatment Choices

10.5.1 PC/Legacy versus Smartphone Designs

10.5.2 PC/Legacy versus PC/New

10.5.3 Smartphone/Legacy versus Smartphone/New

10.5.4 Device and Design Treatment Options

10.6 Conclusion

10.7 Future Challenges and Research Needs

Appendix 10.A: Data Sources

A.1 Market Strategies (17 studies)

A.2 Experimental Data from Market Strategies International.

A.3 Sustainability Cultural Indicators Program (SCIP)

A.4 Army Study to Assess Risk and Resilience in Service members (STARRS)

A.5 Panel Study of Income Dynamics Childhood Retrospective Circumstances Study (PSID-CRCS)

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

Chapter 11 Survey Research and the Quality of Survey Data Among Ethnic Minorities

11.1 Introduction

11.2 On the Use of the Terms Ethnicity and Ethnic Minorities

11.3 On the Representation of Ethnic Minorities in Surveys

11.3.1 Coverage of Ethnic Minorities

11.3.2 Factors Affecting Nonresponse Among Ethnic Minorities

11.3.3 Postsurvey Adjustment Issues Related to Surveys Among Ethnic Minorities

11.4 Measurement Issues

11.4.1 The Tradeoff When Using Response-Enhancing Measures

11.5 Comparability, Timeliness, and Cost Concerns

11.5.1 Comparability

11.5.2 Timeliness and Cost Considerations

11.6 Conclusion

Section 3 Data Collection and Data Processing Applications

Chapter 12 Measurement Error in Survey Operations Management: Detection, Quantification, Visualization, and Reduction

12.1 TSE Background on Survey Operations

12.2 Better and Better: Using Behavior Coding (CARIcode) and Paradata to Evaluate and Improve Question (Specification) Erro...

12.2.1 CARI Coding at Westat

12.2.2 CARI Experiments

12.3 Field-Centered Design: Mobile App for Rapid Reporting and Management

12.3.1 Mobile App Case Study

12.3.2 Paradata Quality

12.4 Faster and Cheaper: Detecting Falsification With GIS Tools

12.5 Putting It All Together: Field Supervisor Dashboards

12.5.1 Dashboards in Operations

12.5.2 Survey Research Dashboards.

12.5.2.1 Dashboards and Paradata.

Notes:

Includes bibliographical references at the end of each chapters and index.

Description based on print version record.

ISBN:

9781119041696

1119041694

9781119041702

1119041708

OCLC:

973835450