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Qualitative Interviewing of Respondents in Large Representative Surveys

Author

Listed:
  • Olaf Groh-Samberg
  • Ingrid Tucci

Abstract

Large representative surveys are using mixed methods to an ever-increasing degree. Biomarkers, register data, and experiments, for example, provide different types of data that can be linked with survey data. The use of qualitative interviewing of participants in longitudinal surveys is still rare, however, in the social sciences. Yet qualitative methods have proven just as valuable as quantitative methods in providing insights into social reality by reflecting the multidimensionality of individual life courses and lived realities. Furthermore, indepth interviews can provide a better understanding of individual decision-making processes and behavior resulting from more or less unconscious strategies. They also provide insights into decisive turning points in people’s lives. Finally, by linking quantitative and qualitative data, the reliability of longitudinal information can be analyzed thoroughly in terms of accuracy as well as meaningfulness.

Suggested Citation

  • Olaf Groh-Samberg & Ingrid Tucci, 2010. "Qualitative Interviewing of Respondents in Large Representative Surveys," RatSWD Working Papers 143, German Data Forum (RatSWD).
    • Handle: RePEc:rsw:rswwps:rswwps143
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    References listed on IDEAS

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    1. Kristin S. Seefeldt, 2008. "Working after Welfare: How women Balance Jobs and Family in the Wake of Welfare Reform," Books from Upjohn Press, W.E. Upjohn Institute for Employment Research, number ww.
    2. Adato, Michelle & Lund, Francie & Mhlongo, Phakama, 2007. "Methodological Innovations in Research on the Dynamics of Poverty: A Longitudinal Study in KwaZulu-Natal, South Africa," World Development, Elsevier, vol. 35(2), pages 247-263, February.
    3. Thomas Dohmen & Armin Falk & David Huffman & Uwe Sunde & Jürgen Schupp & Gert G. Wagner, 2005. "Individual Risk Attitudes: New Evidence from a Large, Representative, Experimentally-Validated Survey," Discussion Papers of DIW Berlin 511, DIW Berlin, German Institute for Economic Research.
    4. Nancy Leech & Anthony Onwuegbuzie, 2009. "A typology of mixed methods research designs," Quality & Quantity: International Journal of Methodology, Springer, vol. 43(2), pages 265-275, March.
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    Cited by:

    1. Nicolas Legewie & Ingrid Tucci, 2016. "Panel-basierte Mixed-Methods-Studien: Design, Feldzugang, Potentiale und Herausforderungen am Beispiel der Studie "Das Erwachsenwerden der Nachkommen von GastarbeiterInnen in Deutschland"," SOEPpapers on Multidisciplinary Panel Data Research 872, DIW Berlin, The German Socio-Economic Panel (SOEP).
    2. repec:bcp:journl:v:8:y:2024:i:9:p:3799-3812 is not listed on IDEAS

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    More about this item

    Keywords

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    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • Z13 - Other Special Topics - - Cultural Economics - - - Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification

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