<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In recent years the use of paradata for nonresponse investigations has risen significantly. One key question is how useful paradata, including call record data and interviewer observations, from the current and previous waves of a longitudinal study, as well as previous wave survey information, are in predicting response outcomes in a longitudinal context. This article aims to address this question. Final response outcomes and sequence length (the number of calls/visits to a household) are modelled both separately and jointly for a longitudinal study. Being able to predict length of call sequence and response can help to improve both adaptive and responsive survey designs and to increase efficiency and effectiveness of call scheduling. The article also identifies the impact of different methodological specifications of the models, for example different specifications of the response outcomes. Latent class analysis is used as one of the approaches to summarise call outcomes in sequences. To assess and compare the models in their ability to predict, indicators derived from classification tables, ROC (Receiver Operating Characteristic) curves, discrimination and prediction are proposed in addition to the standard approach of using the pseudo R<sup>2</sup> value, which is not a sufficient indicator on its own. The study uses data from Understanding Society, a large-scale longitudinal survey in the UK. The findings indicate that basic models (including geographic, design and survey data from the previous wave), although commonly used in predicting and adjusting for nonresponse, do not predict the response outcome well. Conditioning on previous wave paradata, including call record data, interviewer observation data and indicators of change, improve the fit of the models slightly. A significant improvement can be observed when conditioning on the most recent call outcome, which may indicate that the nonresponse process predominantly depends on the most current circumstances of a sample unit.</p></abstract>

In recent years the use of paradata for nonresponse investigations has risen significantly. One key question is how useful paradata, including call record data and interviewer observations, from the current and previous waves of a longitudinal study, as well as previous wave survey information, are in predicting response outcomes in a longitudinal context. This article aims to address this question. Final response outcomes and sequence length (the number of calls/visits to a household) are modelled both separately and jointly for a longitudinal study. Being able to predict length of call sequence and response can help to improve both adaptive and responsive survey designs and to increase efficiency and effectiveness of call scheduling. The article also identifies the impact of different methodological specifications of the models, for example different specifications of the response outcomes. Latent class analysis is used as one of the approaches to summarise call outcomes in sequences. To assess and compare the models in their ability to predict, indicators derived from classification tables, ROC (Receiver Operating Characteristic) curves, discrimination and prediction are proposed in addition to the standard approach of using the pseudo R2 value, which is not a sufficient indicator on its own. The study uses data from Understanding Society, a large-scale longitudinal survey in the UK. The findings indicate that basic models (including geographic, design and survey data from the previous wave), although commonly used in predicting and adjusting for nonresponse, do not predict the response outcome well. Conditioning on previous wave paradata, including call record data, interviewer observation data and indicators of change, improve the fit of the models slightly. A significant improvement can be observed when conditioning on the most recent call outcome, which may indicate that the nonresponse process predominantly depends on the most current circumstances of a sample unit.

Using Prior Wave Information and Paradata: Can They Help to Predict Response Outcomes and Call Sequence Length in a Longitudinal Study?

United Kingdom of Great Britain and Northern Ireland

Department of Social Statistics and Demography and ESRC National Centre for Research Methods (NCRM), School of Social Sciences

ESRC National Centre for Research Methods (NCRM), School of Social Sciences

Department of Social Statistics and Demography and ESRC Administrative Data Research Centre for England (ADRC-E), School of Social Sciences

Journal of Official Statistics

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

In recent years the use of paradata for nonresponse investigations has risen significantly. One key question is how useful paradata, including call record data...

Using Prior Wave Information and Paradata: Can They Help to Predict Response Outcomes and Call Sequence Length in a Longitudinal Study?

Publicado en línea: 09 sept 2017

Páginas: 801 - 833

Recibido: 01 abr 2016

Aceptado: 01 jun 2017

DOI: https://doi.org/10.1515/jos-2017-0037

Palabras clave
Survey nonresponse, interviewer call record data, paradata, call sequence, responsive and adaptive survey designs

© 2017 Gabriele B. Durrant et al., published by De Gruyter Open

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

Using Prior Wave Information and Paradata: Can They Help to Predict Response Outcomes and Call Sequence Length in a Longitudinal Study?

Publicado en línea: 09 sept 2017

Páginas: 801 - 833

Recibido: 01 abr 2016

Aceptado: 01 jun 2017

DOI: https://doi.org/10.1515/jos-2017-0037

Palabras claveSurvey nonresponse, interviewer call record data, paradata, call sequence, responsive and adaptive survey designs

© 2017 Gabriele B. Durrant et al., published by De Gruyter Open

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

Palabras clave
Survey nonresponse, interviewer call record data, paradata, call sequence, responsive and adaptive survey designs