Using Response Propensity Models to Improve the Quality of Response Data in Longitudinal Studies

Plewis Ian () and Shlomo Natalie ()
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Plewis Ian: Social Statistics, School of Social Sciences, University of Manchester, ManchesterM13 9PL, United Kingdom of Great Britain and Northern Ireland.
Shlomo Natalie: Social Statistics, School of Social Sciences, University of Manchester, ManchesterM13 9PL, United Kingdom of Great Britain and Northern Ireland.

Journal of Official Statistics, 2017, vol. 33, issue 3, 753-779

Abstract: 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 nonresponding cases from previous waves although re-issuing refusals might not be the best use of resources. Adapting the sample to target subgroups for re-issuing from wave to wave will improve the representativeness of response. However, in situations where discrimination between respondents and nonrespondents is not strong, it is doubtful whether specific interventions to reduce nonresponse will be cost effective.

Keywords: Representativity indicators; ROC curves; nonresponse; Millennium Cohort Study (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:33:y:2017:i:3:p:753-779:n:9

DOI: 10.1515/jos-2017-0035

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