Handling attrition and non-response in longitudinal data with an application to a study of Australian youth

Joy Cumming, Harvey Goldstein

Research output: Contribution to journalArticle (Academic Journal)peer-review

8 Citations (Scopus)
256 Downloads (Pure)

Abstract

A standard concern with long term longitudinal studies is that of attrition over time. Together with initial non-response this typically leads to biased model estimates unless a suitable form of adjustment is carried out. The standard approach to this has been to compute weights based upon the propensity to respond and to drop out and then carry out weighted analyses to compensate for response bias. In the present paper we argue that this approach is statistically inefficient, because it drops incomplete data records, is inflexible, and in practice gives rise to undue complexity involving a proliferation of weighting systems for different analyses. Instead we set out an alternative approach that makes use of recently developed imputation procedures for handling missing data and show how this can be used to improve the quality of the statistical analysis. An example analysis is given using the Longitudinal Study of Australian Youth (LSAY).

Original languageEnglish
Pages (from-to)53-63
Number of pages11
JournalLongitudinal and Life Course Studies
Volume7
Issue number1
DOIs
Publication statusPublished - 26 Jan 2016

Keywords

  • Attrition
  • Further education
  • Imputation
  • Life course
  • Longitudinal data
  • Missing data
  • Weighting

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  • Lemma 3

    Rintoul, D. A.

    1/10/1130/09/14

    Project: Research

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