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Modelling non‐response in the National Child Development Study

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  • Denise Hawkes
  • Ian Plewis

Abstract

Summary. There is widespread concern that the cumulative effects of the non‐response that is bound to affect any long‐running longitudinal study will lead to mistaken inferences about change. We focus on the National Child Development Study and show how non‐response has accumulated over time. We distinguish between attrition and wave non‐response and show how these two kinds of non‐response can be related to a set of explanatory variables. We model the discrete time hazard of non‐response and also fit a set of multinomial logistic regressions to the probabilities of different kinds of non‐response at a particular sweep. We find that the best predictors of non‐response at any sweep are generally variables that are measured at the previous sweep but, although non‐response is systematic, much of the variation in it remains unexplained by our models. We consider the implications of our results for both design and analysis.

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  • Denise Hawkes & Ian Plewis, 2006. "Modelling non‐response in the National Child Development Study," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 479-491, July.
    • Handle: RePEc:bla:jorssa:v:169:y:2006:i:3:p:479-491
    DOI: 10.1111/j.1467-985X.2006.00401.x
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    1. Duncan Thomas & Elizabeth Frankenberg & James P. Smith, 2001. "Lost but Not Forgotten: Attrition and Follow-up in the Indonesia Family Life Survey," Journal of Human Resources, University of Wisconsin Press, vol. 36(3), pages 556-592.
    2. 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.
    3. Lee A. Lillard & Constantijn W. A. Panis, 1998. "Panel Attrition from the Panel Study of Income Dynamics: Household Income, Marital Status, and Mortality," Journal of Human Resources, University of Wisconsin Press, vol. 33(2), pages 437-457.
    4. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    5. Nicoletti, Cheti & Peracchi, Franco, 2002. "A cross-country comparison of survey nonparticipation in the ECHP -ISER working paper-," ISER Working Paper Series 2002-32, Institute for Social and Economic Research.
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