feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    UID:
    b3kat_BV048269089
    Format: 1 Online-Ressource (33 p)
    Series Statement: World Bank E-Library Archive
    Content: The collection of survey data from war zones or other unstable security situations is vulnerable to error because conflict often limits the implementation options. Although there are elevated risks throughout the process, this paper focuses specifically on challenges to frame construction and sample selection. The paper uses simulations based on data from the Mogadishu High Frequency Survey Pilot to examine the implications of the choice of second-stage selection methodology on bias and variance. Among the other findings, the simulations show the bias introduced by a random walk design leads to the underestimation of the poverty headcount by more than 10 percent. The paper also discusses the experience of the authors in the time required and technical complexity of the associated back-office preparation work and weight calculations for each method. Finally, as the simulations assume perfect implementation of the design, the paper also discusses practicality, including the ease of implementation and options for remote verification, and outlines areas for future research and pilot testing
    Additional Edition: Erscheint auch als Druck-Ausgabe Himelein, Kristen Second-Stage Sampling for Conflict Areas: Methods and Implications Washington, D.C : The World Bank, 2016
    Language: English
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: Volltext  (Deutschlandweit zugänglich)
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 2
    UID:
    b3kat_BV048266018
    Format: 1 Online-Ressource (25 p)
    Content: Livestock are an important component of rural livelihoods in developing countries, but data about this source of income and wealth are difficult to collect because of the nomadic and semi-nomadic nature of many pastoralist populations. Most household surveys exclude those without permanent dwellings, leading to undercoverage. This study explores the use of a random geographic cluster sample as an alternative to the household-based sample. In this design, points are randomly selected and all eligible respondents found inside circles drawn around the selected points are interviewed. This approach should eliminate undercoverage of mobile populations. The results of a random geographic cluster sample survey are presented with a total sample size of 784 households to measure livestock ownership in the Afar region of Ethiopia in 2012. The paper explores the data quality of the random geographic cluster sample relative to a recent household survey and discusses the implementation challenges
    Additional Edition: Himelein, Kristen The Use of Random Geographic Cluster Sampling to Survey Pastoralists
    Language: English
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: Volltext  (Deutschlandweit zugänglich)
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 3
    UID:
    almafu_9959328402502883
    Format: 1 online resource
    ISBN: 9781119041689 , 1119041686 , 9781119041702 , 1119041708
    Note: Includes index.
    Additional Edition: Print version: Total survey error in practice. Hoboken, New Jersey : John Wiley & Sons, 2016 ISBN 9781119041672
    Language: English
    Keywords: Electronic books. ; Electronic books. ; Electronic books.
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 4
    UID:
    kobvindex_INT58905
    Format: 1 online resource (627 pages)
    Edition: 1st ed.
    ISBN: 9781119041689
    Series Statement: Wiley Series in Survey Methodology Series
    Note: 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 -- References -- Chapter 3 Big Data: A Survey Research Perspective , 12.5.2.1 Dashboards and Paradata , 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 -- References -- 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 -- References -- 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 -- References -- 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 -- References -- 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 -- References -- 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 -- References -- 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 -- References -- 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 and others (2013) Experiment -- Appendix 10.D: Survey Questions Used in the Analysis of the Peterson and others (2013) Experiment -- References -- 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 -- References -- 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
    Additional Edition: Print version Biemer, Paul P. Total Survey Error in Practice Newark : John Wiley & Sons, Incorporated,c2017 ISBN 9781119041672
    Language: English
    Keywords: Electronic books ; Electronic books
    URL: FULL  ((OIS Credentials Required))
    URL: FULL  ((OIS Credentials Required))
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 5
    UID:
    gbv_870009788
    Format: xxvii, 593 Seiten , Illustrationen, Diagramme
    ISBN: 9781119041672
    Series Statement: Wiley series in survey methodology
    Note: Includes bibligraphical references and index
    Additional Edition: ISBN 9781119041696
    Additional Edition: Erscheint auch als Online-Ausgabe Total survey error in practice Hoboken, New Jersey : Wiley, 2017 ISBN 9781119041689
    Additional Edition: ISBN 1119041686
    Additional Edition: ISBN 9781119041702
    Additional Edition: ISBN 1119041708
    Additional Edition: Erscheint auch als Online-Ausgabe Total survey error in practice Hoboken, New Jersey : Wiley, 2017 ISBN 9781119041689
    Language: English
    Subjects: Sociology
    RVK:
    Keywords: Demoskopie ; Umfrage ; Fehler ; Qualität ; Empirische Sozialforschung
    URL: Cover
    Author information: Kreuter, Frauke
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 6
    UID:
    gbv_667588833
    Format: Online-Ressource (PDF-Datei: 12 S., 123,05 KB)
    ISSN: 1864-3361
    Note: Literaturverz. S. 136 - 138
    In: Survey research methods, Konstanz : [Verlag nicht ermittelbar], 2007, 4, 3, Seite 127-138, 1864-3361
    In: volume:4
    In: number:3
    In: pages:127-138
    Language: English
    Author information: Watermann, Rainer
    Author information: Maaz, Kai 1972-
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 7
    UID:
    gbv_61300650X
    ISSN: 1079-5014
    In: The journals of gerontology / B, Cary, NC : Oxford Univ. Pr., 1995, (2009), Seite 12-19, 1079-5014
    In: The national social life, health, and aging project, Washington, DC : Oxford Univ. Press, 2009, (2009), Seite 12-19
    In: year:2009
    In: pages:12-19
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 8
    UID:
    gbv_797610499
    Format: Online-Ressource
    Series Statement: Policy Research Working Paper 6589
    Content: Livestock are an important component of rural livelihoods in developing countries, but data about this source of income and wealth are difficult to collect because of the nomadic and semi-nomadic nature of many pastoralist populations. Most household surveys exclude those without permanent dwellings, leading to undercoverage. This study explores the use of a random geographic cluster sample as an alternative to the household-based sample. In this design, points are randomly selected and all eligible respondents found inside circles drawn around the selected points are interviewed. This approach should eliminate undercoverage of mobile populations. The results of a random geographic cluster sample survey are presented with a total sample size of 784 households to measure livestock ownership in the Afar region of Ethiopia in 2012. The paper explores the data quality of the random geographic cluster sample relative to a recent household survey and discusses the implementation challenges.
    Note: English , en_US
    Language: English
    URL: Volltext  (kostenfrei)
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 9
    UID:
    edocfu_9958246587002883
    Format: 1 online resource (25 pages)
    Series Statement: Policy research working papers.
    Content: Livestock are an important component of rural livelihoods in developing countries, but data about this source of income and wealth are difficult to collect because of the nomadic and semi-nomadic nature of many pastoralist populations. Most household surveys exclude those without permanent dwellings, leading to undercoverage. This study explores the use of a random geographic cluster sample as an alternative to the household-based sample. In this design, points are randomly selected and all eligible respondents found inside circles drawn around the selected points are interviewed. This approach should eliminate undercoverage of mobile populations. The results of a random geographic cluster sample survey are presented with a total sample size of 784 households to measure livestock ownership in the Afar region of Ethiopia in 2012. The paper explores the data quality of the random geographic cluster sample relative to a recent household survey and discusses the implementation challenges.
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 10
    UID:
    edoccha_9958246587002883
    Format: 1 online resource (25 pages)
    Series Statement: Policy research working papers.
    Content: Livestock are an important component of rural livelihoods in developing countries, but data about this source of income and wealth are difficult to collect because of the nomadic and semi-nomadic nature of many pastoralist populations. Most household surveys exclude those without permanent dwellings, leading to undercoverage. This study explores the use of a random geographic cluster sample as an alternative to the household-based sample. In this design, points are randomly selected and all eligible respondents found inside circles drawn around the selected points are interviewed. This approach should eliminate undercoverage of mobile populations. The results of a random geographic cluster sample survey are presented with a total sample size of 784 households to measure livestock ownership in the Afar region of Ethiopia in 2012. The paper explores the data quality of the random geographic cluster sample relative to a recent household survey and discusses the implementation challenges.
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. Further information can be found on the KOBV privacy pages