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Missing ordinal covariates with informative selection

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Author Info

  • Alfonso Miranda

    ()
    (Department of Quantitative Social Science, Institute of Education, University of London. 20 Bedford Way, London WC1H 0AL, UK.)

  • Sophia Rabe-Hesketh

    ()
    (Graduate School of Education and Graduate Group in Biostatistics, University of California, Berkeley, USA. Institute of Education, University of London, London, UK.)

Abstract

This paper considers the problem of parameter estimation in a model for a continuous response variable y when an important ordinal explanatory variable x is missing for a large proportion of the sample. Non-missingness of x, or sample selection, is correlated with the response variable and/or with the unobserved values the ordinal explanatory variable takes when missing. We suggest solving the endogenous selection, or 'not missing at random' (NMAR), problem by modelling the informative selection mechanism, the ordinal explanatory variable, and the response variable together. The use of the method is illustrated by re-examining the problem of the ethnic gap in school achievement at age 16 in England using linked data from the National Pupil database (NPD), the Longitudinal Study of Young People in England (LSYPE), and the Census 2001.

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Bibliographic Info

Paper provided by Department of Quantitative Social Science - Institute of Education, University of London in its series DoQSS Working Papers with number 10-16.

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Date of creation: 14 Jul 2010
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Handle: RePEc:qss:dqsswp:1016

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Postal: Department of Quantitative Social Science. 20 Bedford Way London WC1H 0AL
Phone: (44) (0)20 7612 6654. Eliminate (44) and add (0) if calling from inside the UK. Add (44) and eliminate (0) if calling from abroad.
Fax: (44) (0)20 7612 6686
Web page: http://www.ioe.ac.uk/research/departments/qss/35445.html
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Related research

Keywords: Missing covariate; sample selection; latent class models; ordinal variables; NMAR;

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Cited by:
  1. Jake Anders, 2012. "Using the Longitudinal Study of Young People in England for research into Higher Education access," DoQSS Working Papers 12-13, Department of Quantitative Social Science - Institute of Education, University of London.

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