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  1. Selection in Surveys
    Autor*in: Dutz, Deniz; Huitfeldt, Ingrid; Lacouture, Santiago; Mogstad, Magne; Torgovitsky, Alexander; Van Dijk, Winnie L.
    Erschienen: 2021
    Verlag:  National Bureau of Economic Research, Cambridge, Mass

    We evaluate how nonresponse affects conclusions drawn from survey data and consider how researchers can reliably test and correct for nonresponse bias. To do so, we examine a survey on labor market conditions during the COVID-19 pandemic that used... mehr

    Zugang:
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)

    Standort:
    Sächsische Landesbibliothek - Staats- und Universitätsbibliothek Dresden
    Fernleihe:
    keine Fernleihe

    Standort:
    Universitätsbibliothek Freiburg
    Fernleihe:
    keine Fernleihe

    Standort:
    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
    Fernleihe:
    keine Fernleihe

    Standort:
    Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky
    Fernleihe:
    keine Fernleihe

    Standort:
    Technische Informationsbibliothek (TIB) / Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek
    Fernleihe:
    keine Fernleihe

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Fernleihe:
    keine Fernleihe

     

    We evaluate how nonresponse affects conclusions drawn from survey data and consider how researchers can reliably test and correct for nonresponse bias. To do so, we examine a survey on labor market conditions during the COVID-19 pandemic that used randomly assigned financial incentives to encourage participation. We link the survey data to administrative data sources, allowing us to observe a ground truth for participants and nonparticipants. We find evidence of large nonresponse bias, even after correcting for observable differences between participants and nonparticipants. We apply a range of existing methods that account for nonresponse bias due to unobserved differences, including worst-case bounds, bounds that incorporate monotonicity assumptions, and approaches based on parametric and nonparametric selection models. These methods produce bounds (or point estimates) that are either too wide to be useful or far from the ground truth. We show how these shortcomings can be addressed by modeling how nonparticipation can be both active (declining to participate) and passive (not seeing the survey invitation). The model makes use of variation from the randomly assigned financial incentives, as well as the timing of reminder emails. Applying the model to our data produces bounds (or point estimates) that are narrower and closer to the ground truth than the other methods

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    Schriftenreihe: NBER working paper series ; no. w29549
    Schlagworte: Erhebungstechnik; Coronavirus; Wirtschaftslage; Befragung; USA
    Umfang: 1 Online-Ressource, illustrations (black and white)
    Bemerkung(en):

    Hardcopy version available to institutional subscribers
  2. Selection in surveys
    Autor*in: Dutz, Deniz; Huitfeldt, Ingrid; Lacouture, Santiago; Mogstad, Magne; Torgovitsky, Alexander; Van Dijk, Winnie L.
    Erschienen: December 2021
    Verlag:  Statistics Norway, Research Department, Oslo

    We evaluate how nonresponse affects conclusions drawn from survey data and consider how researchers can reliably test and correct for nonresponse bias. To do so, we examine a survey on labor market conditions during the COVID-19 pandemic that used... mehr

    Zugang:
    Verlag (kostenfrei)
    Verlag (kostenfrei)
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 619
    Fernleihe:
    keine Fernleihe

     

    We evaluate how nonresponse affects conclusions drawn from survey data and consider how researchers can reliably test and correct for nonresponse bias. To do so, we examine a survey on labor market conditions during the COVID-19 pandemic that used randomly assigned financial incentives to encourage participation. We link the survey data to administrative data sources, allowing us to observe a ground truth for participants and nonparticipants. We find evidence of large nonresponse bias, even after correcting for observable differences between participants and nonparticipants. We apply a range of existing methods that account for nonresponse bias due to unobserved differences, including worst-case bounds, bounds that incorporate monotonicity assumptions, and approaches based on parametric and nonparametric selection models. These methods produce bounds (or point estimates) that are either too wide to be useful or far from the ground truth. We show how these shortcomings can be addressed by modeling how nonparticipation can be both active (declining to participate) and passive (not seeing the survey invitation). The model makes use of variation from the randomly assigned financial incentives, as well as the timing of reminder emails. Applying the model to our data produces bounds (or point estimates) that are narrower and closer to the ground truth than the other methods.

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/250138
    Schriftenreihe: Discussion papers / Statistics Norway, Research Department ; 971
    Schlagworte: survey; nonresponse; nonresponse bias
    Umfang: 1 Online-Ressource (circa 95 Seiten), Illustrationen
  3. What Drives (Gaps in) Scientific Study Participation? Evidence from a COVID-19 Antibody Survey
    Autor*in: Dutz, Deniz; Greenstone, Michael; Hortaçsu, Ali; Lacouture, Santiago; Mogstad, Magne; Shaikh, Azeem M.; Torgovitsky, Alexander; Van Dijk, Winnie L.
    Erschienen: January 2023
    Verlag:  National Bureau of Economic Research, Cambridge, Mass

    Underrepresentation of minority and poor households in scientific studies undermines policy decisions and public health. We study data from a serological study that randomized participation incentives. Participation is low (6% at $0, 17% at $100, 29%... mehr

    Zugang:
    Verlag (lizenzpflichtig)
    Resolving-System (lizenzpflichtig)

    Standort:
    Sächsische Landesbibliothek - Staats- und Universitätsbibliothek Dresden
    Fernleihe:
    keine Fernleihe

    Standort:
    Universitätsbibliothek Freiburg
    Fernleihe:
    keine Fernleihe

    Standort:
    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
    Fernleihe:
    keine Fernleihe

    Standort:
    Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky
    Fernleihe:
    keine Fernleihe

    Standort:
    Technische Informationsbibliothek (TIB) / Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek
    Fernleihe:
    keine Fernleihe

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Fernleihe:
    keine Fernleihe

     

    Underrepresentation of minority and poor households in scientific studies undermines policy decisions and public health. We study data from a serological study that randomized participation incentives. Participation is low (6% at $0, 17% at $100, 29% at $500) and unequal: minority and poor households are underrepresented at low incentive levels. We develop a framework for disentangling non-contact and ``participation hesitancy'' in explaining non-participation. We find that underrepresentation occurs because poor and minority households are more hesitant, not because they are harder to contact. The $500 incentive appears to overcome differences in hesitancy and restore representativeness along observable dimensions

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Schriftenreihe: NBER working paper series ; no. w30880
    Schlagworte: Forschung; Coronavirus; Erhebungstechnik; Minderheit; Armut; Soziale Lage; USA; General; Survey Methods; Survey Methods; Sampling Methods; Health; Health and Inequality; General; Innovation and Invention: Processes and Incentives
    Umfang: 1 Online-Ressource, illustrations (black and white)
    Bemerkung(en):

    Hardcopy version available to institutional subscribers