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On the fade-away of an initial bias in longitudinal surveys

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  • Rendtel, Ulrich
  • Alho, Juha M.

Abstract

We propose a novel view of selection bias in longitudinal surveys. Such bias may arise from initial nonresponse in a probability sample, or it may be caused by self-selection in an internet survey. A contraction theorem from mathematical demography is used to show that an initial bias can "fade-away" in later panel waves, if the transition laws in the observed sample and the population are identical. Panel attrition is incorporated into the Markovian framework. Extensions to Markov chains of higher order are given, and the limitations of our approach under population heterogeneity are discussed. We use empirical data from a German Labour Market Panel to demonstrate the extend and speed of the fade-away effect. The implications of the new approach on the treatment of nonresponse, and attrition weighting, are discussed.

Suggested Citation

  • Rendtel, Ulrich & Alho, Juha M., 2022. "On the fade-away of an initial bias in longitudinal surveys," Discussion Papers 2022/4, Free University Berlin, School of Business & Economics.
    • Handle: RePEc:zbw:fubsbe:20224
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    References listed on IDEAS

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    1. John Fitzgerald & Peter Gottschalk & Robert Moffitt, 1998. "An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of Income Dynamics," Journal of Human Resources, University of Wisconsin Press, vol. 33(2), pages 251-299.
    2. Christian Dudel, 2021. "Expanding the Markov Chain Toolbox: Distributions of Occupation Times and Waiting Times," Sociological Methods & Research, , vol. 50(1), pages 401-428, February.
    3. Jelke Bethlehem, 2010. "Selection Bias in Web Surveys," International Statistical Review, International Statistical Institute, vol. 78(2), pages 161-188, August.
    4. Magdalena Smyk & Joanna Tyrowicz & Lucas van der Velde, 2021. "A Cautionary Note on the Reliability of the Online Survey Data: The Case of Wage Indicator," Sociological Methods & Research, , vol. 50(1), pages 429-464, February.
    5. Rendtel, Ulrich & Basic, Edin, 2007. "Assessing the bias due to non-coverage of residential movers in the German microcensus panel: an evaluation using data from the socio-economic panel," Discussion Papers 2007/6, Free University Berlin, School of Business & Economics.
    6. Niels Keiding & Thomas A. Louis, 2016. "Perils and potentials of self-selected entry to epidemiological studies and surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(2), pages 319-376, February.
    7. Edin Basic & Ulrich Rendtel, 2007. "Assessing the bias due to non-coverage of residential movers in the German Microcensus Panel: an evaluation using data from the Socio-Economic Panel," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 91(3), pages 311-334, October.
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