<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

United Kingdom of Great Britain and Northern Ireland

Social Statistics, School of Social Sciences

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

We review two approaches for improving the response in longitudinal (birth cohort) studies based on response propensity models: strategies for sample...

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

Online veröffentlicht: 09. Sept. 2017

Seitenbereich: 753 - 779

Eingereicht: 01. März 2016

Akzeptiert: 01. Juni 2017

DOI: https://doi.org/10.1515/jos-2017-0035

Schlüsselwörter
Representativity indicators, ROC curves, nonresponse, Millennium Cohort Study

© 2017 Ian Plewis et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Online veröffentlicht: 09. Sept. 2017

Seitenbereich: 753 - 779

Eingereicht: 01. März 2016

Akzeptiert: 01. Juni 2017

DOI: https://doi.org/10.1515/jos-2017-0035

SchlüsselwörterRepresentativity indicators, ROC curves, nonresponse, Millennium Cohort Study

© 2017 Ian Plewis et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Representativity indicators, ROC curves, nonresponse, Millennium Cohort Study