Using response propensity models to improve the quality of response data in longitudinal studies

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We review two approaches for improving the response in longitudinal (birth cohort) studies based on response propensity models: strategies for sample maintenance in longitudinal studies and improving the representativeness of the respondents over time through interventions. Based on estimated response propensities, we examine the effectiveness of different re-issuing strategies using representativity indicators (R-indicators). We also combine information from the Receiver Operating Characteristic (ROC) curve with a cost function to determine an optimal cut point for the propensity not to respond in order to target interventions efficiently at cases least likely to respond. We use the first four waves of the UK Millennium Cohort Study to illustrate these methods. Our results suggest that it is worth re-issuing to the field non-responding cases from previous waves although re-issuing refusals might not be the best use of resources. Adapting the sample to target sub-groups for re-issuing from wave to wave will improve the representativeness of response. However, in situations where discrimination between respondents and non-respondents is not strong, it is doubtful whether specific interventions to reduce non-response will be cost effective.
Original languageEnglish
Pages (from-to)753-779
Number of pages27
JournalJournal of Official Statistics
Issue number3
Early online date9 Sept 2017
Publication statusPublished - 2017


  • Millennium Cohort Study
  • Nonresponse
  • Representativity indicators
  • ROC curves

Research Beacons, Institutes and Platforms

  • Cathie Marsh Institute


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