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Survey Item Nonresponse and its Treatment

In: Modern Econometric Analysis

Author

Listed:
  • Susanne Rässler

    (IAB Institut für Arbeitsmarkt- und Berufsforschung)

  • Regina T. Riphahn

    (Universität Erlangen-Nürnberg)

Abstract

One of the most salient data problems empirical researchers face is the lack of informative responses in survey data. This contribution briefly surveys the literature on item nonresponse behavior and its determinants before it describes four approaches to address item nonresponse problems: Casewise deletion of observations, weighting, imputation, and model-based procedures. We describe the basic approaches, their strengths and weaknesses and illustrate some of their effects using a simulation study. The paper concludes with some recommendations for the applied researcher.

Suggested Citation

  • Susanne Rässler & Regina T. Riphahn, 2006. "Survey Item Nonresponse and its Treatment," Springer Books, in: Olaf Hübler & Jachim Frohn (ed.), Modern Econometric Analysis, chapter 15, pages 215-230, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-32693-9_15
    DOI: 10.1007/3-540-32693-6_15
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    Citations

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    Cited by:

    1. Bannier, Christina E. & Schwarz, Milena, 2017. "Skilled but unaware of it: Occurrence and potential long-term effects of females' financial underconfidence," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168188, Verein für Socialpolitik / German Economic Association.
    2. Thomas Y. Mathä & Alessandro Porpiglia & Michael Ziegelmeyer, 2012. "Income, Wealth and Consumption of Cross-Border Commuters to Luxembourg," BCL working papers 78, Central Bank of Luxembourg.
    3. Rässler, Susanne, 2006. "Der Einsatz von Missing Data Techniken in der Arbeitsmarktforschung des IAB," IAB-Forschungsbericht 200618, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    4. Sonja C. Kassenboehmer & Stefanie Schurer, 2018. "Survey item-response behavior as an imperfect proxy for unobserved ability: Theory and application," Working Papers 2018-035, Human Capital and Economic Opportunity Working Group.
    5. Bruno Moeremans & Michaël Dooms, 2021. "An Exploration of Social License to Operate (SLTO) Measurement in the Port Industry: The Case of North America," Sustainability, MDPI, vol. 13(5), pages 1-25, February.
    6. Bannier, Christina E. & Schwarz, Milena, 2018. "Gender- and education-related effects of financial literacy and confidence on financial wealth," Journal of Economic Psychology, Elsevier, vol. 67(C), pages 66-86.
    7. Daniel Schunk, 2008. "A Markov chain Monte Carlo algorithm for multiple imputation in large surveys," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(1), pages 101-114, February.
    8. Ziegelmeyer, Michael, 2009. "Documentation of the logical imputation using the panel structure of the 2003-2008 German SAVE Survey," Sonderforschungsbereich 504 Publications 08-41, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    9. Neubert, Milena & Bannier, Christina E., 2016. "Actual and perceived financial sophistication and wealth accumulation: The role of education and gender," VfS Annual Conference 2016 (Augsburg): Demographic Change 145593, Verein für Socialpolitik / German Economic Association.
    10. Araceli Mateos & Margarita Corral, 2022. "Partial non-response in political elite studies: an approach to parliamentary elites in Latin America," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4089-4106, December.
    11. Bernd Hayo & Edith Neuenkirch, 2018. "Survey on Germans’ Attitudes Towards and Knowledge of Monetary Policy Issues: Documentation of Survey Methodology and Descriptive Results," MAGKS Papers on Economics 201821, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    12. Michael Ziegelmeyer, 2013. "Illuminate the unknown: evaluation of imputation procedures based on the SAVE survey," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(1), pages 49-76, January.
    13. Daniel Schunk, 2007. "A Markov Chain Monte Carlo Multiple Imputation Procedure for Dealing with Item Nonresponse in the German SAVE Survey," MEA discussion paper series 07121, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    14. Hübler, Olaf, 2013. "Methods in empirical economics - a selective review with applications," Hannover Economic Papers (HEP) dp-513, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    15. Romina Boarini & Margherita Comola & Femke Keulenaer & Robert Manchin & Conal Smith, 2013. "Can Governments Boost People’s Sense of Well-Being? The Impact of Selected Labour Market and Health Policies on Life Satisfaction," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 114(1), pages 105-120, October.
    16. Anders Oskar Kjøller‐Hansen & Lena Lindbjerg Sperling, 2020. "Measuring inclusive growth experiences: Five criteria for productive employment," Review of Development Economics, Wiley Blackwell, vol. 24(4), pages 1413-1429, November.
    17. Uwe Jensen & Hermann Gartner & Susanne Rässler, 2010. "Estimating German overqualification with stochastic earnings frontiers," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 94(1), pages 33-51, March.
    18. Joachim R. Frick & Markus M. Grabka, 2007. "Item Non-response and Imputation of Annual Labor Income in Panel Surveys from a Cross-National Perspective," Discussion Papers of DIW Berlin 736, DIW Berlin, German Institute for Economic Research.

    More about this item

    Keywords

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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other

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