IDEAS home Printed from https://ideas.repec.org/p/diw/diwsop/diw_sp855.html
   My bibliography  Save this paper

Using Personalized Feedback to Increase Data Quality and Respondents' Motivation in Web Surveys?

Author

Listed:
  • Simon Kühne
  • Martin Kroh

Abstract

*****Volltextdokument auf Wunsch der Autoren gelöscht***** Web surveys technically allow providing feedback to respondents based on their previous responses. This personalized feedback may not only beused to target follow-up questions, it also allows test results to be returned immediately to respondents. This paper argues that the possibility of learning something about themselves increases respondents’ motivation and possibly the accuracy of responses. While past studies mainly concentrate on the effects of providing study results on future response rates, thus far survey research lacks of theoretical and empirical contributions on the effects of personalized, immediate, feedback on response behavior. To test this, we implemented a randomized trial in the context of the Berlin Aging Study II (BASE-II) in 2014, providing feedback regarding the respondents’ personality tests (Big-Five personality inventory) to a subgroup of the sample. Results show moderate differences in response behavior between experimental and control group (item nonresponse, response styles, internal consistency, socially desirable responding, corrective answers, and response times). In addition, we find that respondents of the experimental group report higher levels of satisfaction with the survey.

Suggested Citation

  • Simon Kühne & Martin Kroh, 2016. "Using Personalized Feedback to Increase Data Quality and Respondents' Motivation in Web Surveys?," SOEPpapers on Multidisciplinary Panel Data Research 855, DIW Berlin, The German Socio-Economic Panel (SOEP).
    • Handle: RePEc:diw:diwsop:diw_sp855
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    References listed on IDEAS

    as
    1. Ivar Krumpal, 2013. "Determinants of social desirability bias in sensitive surveys: a literature review," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(4), pages 2025-2047, June.
    2. Matthias Schonlau & Arthur van Soest & Arie Kapteyn & Mick Couper, 2009. "Selection Bias in Web Surveys and the Use of Propensity Scores," Sociological Methods & Research, , vol. 37(3), pages 291-318, February.
    3. Anke Böckenhoff & Denise Saßenroth & Martin Kroh & Thomas Siedler & Peter Eibich & Gert G. Wagner, 2013. "The Socio-Economic Module of the Berlin Aging Study II (SOEP-BASE): Description, Structure, and Questionnaire," SOEPpapers on Multidisciplinary Panel Data Research 568, DIW Berlin, The German Socio-Economic Panel (SOEP).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Almut Schumann & Detlev Lück, 2023. "Better to ask online when it concerns intimate relationships? Survey mode differences in the assessment of relationship quality," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 48(22), pages 609-640.
    2. Crossley, Thomas F. & Fisher, Paul & Low, Hamish, 2021. "The heterogeneous and regressive consequences of COVID-19: Evidence from high quality panel data," Journal of Public Economics, Elsevier, vol. 193(C).
    3. Michael T Gastner & Károly Takács & Máté Gulyás & Zsuzsanna Szvetelszky & Beáta Oborny, 2019. "The impact of hypocrisy on opinion formation: A dynamic model," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-21, June.
    4. Babette Bühler & Katja Möhring & Andreas P. Weiland, 2022. "Assessing dissimilarity of employment history information from survey and administrative data using sequence analysis techniques," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4747-4774, December.
    5. Guzi, Martin & de Pedraza, Pablo, 2013. "A Web Survey Analysis of the Subjective Well-being of Spanish Workers," IZA Discussion Papers 7618, Institute of Labor Economics (IZA).
    6. Sjöstedt, Martin & Sundström, Aksel & Jagers, Sverker C. & Ntuli, Herbert, 2022. "Governance through community policing: What makes citizens report poaching of wildlife to state officials?," World Development, Elsevier, vol. 160(C).
    7. Tricia Koroknay†Palicz & Joao Montalvao, 2020. "Sex, Lies, and Surveys: The Role of Interviewer Characteristics," Economics Bulletin, AccessEcon, vol. 40(4), pages 3313-3324.
    8. Arni, Patrick & Dragone, Davide & Goette, Lorenz & Ziebarth, Nicolas R., 2021. "Biased health perceptions and risky health behaviors—Theory and evidence," Journal of Health Economics, Elsevier, vol. 76(C).
    9. Burke, Mary A. & Carman, Katherine G., 2017. "You can be too thin (but not too tall): Social desirability bias in self-reports of weight and height," Economics & Human Biology, Elsevier, vol. 27(PA), pages 198-222.
    10. Shinichi Kitano, 2021. "Estimation of Determinants of Farmland Abandonment and Its Data Problems," Land, MDPI, vol. 10(6), pages 1-17, June.
    11. Burgstaller, Lilith & Feld, Lars P. & Pfeil, Katharina, 2022. "Working in the shadow: Survey techniques for measuring and explaining undeclared work," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 661-671.
    12. Seres, Gyula & Balleyer, Anna Helen & Cerutti, Nicola & Danilov, Anastasia & Friedrichsen, Jana & Liu, Yiming & Süer, Müge, 2021. "Face masks increase compliance with physical distancing recommendations during the COVID-19 pandemic," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 7(2), pages 139-158.
    13. Yuning Wu & Ivan Y. Sun & Rong Hu, 2021. "Cooperation with Police in China: Surveillance Cameras, Neighborhood Efficacy and Policing," Social Science Quarterly, Southwestern Social Science Association, vol. 102(1), pages 433-453, January.
    14. Egon Dejonckheere & Brock Bastian, 2021. "Perceiving Social Pressure not to Feel Negative is Linked to a More Negative Self-concept," Journal of Happiness Studies, Springer, vol. 22(2), pages 667-679, February.
    15. Martin Korndörfer & Boris Egloff & Stefan C. Schmukle, 2015. "A Large Scale Test of the Effect of Social Class on Prosocial Behavior," Working Papers 1601, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    16. Maciej Berȩsewicz & Dagmara Nikulin, 2021. "Estimation of the size of informal employment based on administrative records with non‐ignorable selection mechanism," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 667-690, June.
    17. Chadi, Adrian, 2013. "Third Person Effects in Interview Responses on Life Satisfaction," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 133(2), pages 323-333.
    18. Michela Accerenzi & Pablo Brañas-Garza & Diego Jorrat, 2022. "Parents’ knowledge and predictions about the age of menarche: Experimental evidence from Honduras," Working Papers 132, Red Nacional de Investigadores en Economía (RedNIE).
    19. Stéphane Legleye & Géraldine Charrance & Nicolas Razafindratsima & Nathalie Bajos & Aline Bohet & Caroline Moreau, 2018. "The Use of a Nonprobability Internet Panel to Monitor Sexual and Reproductive Health in the General Population," Sociological Methods & Research, , vol. 47(2), pages 314-348, March.
    20. Guo, Shiau-Ling, 2023. "The governance implication of the geographic concentration of franchise activities for franchise relationships," Journal of Business Research, Elsevier, vol. 157(C).

    More about this item

    Keywords

    Personalized Feedback; Web Surveys; Online Surveys; Incentives; Respondent Motivation; Measurement Error; Survey Satisfaction; Big Five Personality Traits;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:diw:diwsop:diw_sp855. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Bibliothek (email available below). General contact details of provider: https://edirc.repec.org/data/sodiwde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.