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Using prior wave information and paradata: Can they help to predict response outcomes and call sequence length in a longitudinal study?

Using prior wave information and paradata: Can they help to predict response outcomes and call sequence length in a longitudinal study?
Using prior wave information and paradata: Can they help to predict response outcomes and call sequence length in a longitudinal study?
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 paper aims to address this question. Final response outcome 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 paper 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 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. 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.
University of Southampton
Durrant, Gabriele
14fcc787-2666-46f2-a097-e4b98a210610
Maslovskaya, Olga
9c979052-e9d7-4400-a657-38f1f9cd74d0
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940
Durrant, Gabriele
14fcc787-2666-46f2-a097-e4b98a210610
Maslovskaya, Olga
9c979052-e9d7-4400-a657-38f1f9cd74d0
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940

Durrant, Gabriele, Maslovskaya, Olga and Smith, Peter W.F. (2016) Using prior wave information and paradata: Can they help to predict response outcomes and call sequence length in a longitudinal study? Southampton, GB. University of Southampton 43pp.

Record type: Monograph (Working Paper)

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 paper aims to address this question. Final response outcome 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 paper 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 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. 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.

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More information

Published date: 11 May 2016
Organisations: Social Statistics & Demography

Identifiers

Local EPrints ID: 394086
URI: https://eprints.soton.ac.uk/id/eprint/394086
PURE UUID: 39a89d1a-d5d8-4bd1-89ba-3466b2f680cd
ORCID for Olga Maslovskaya: ORCID iD orcid.org/0000-0003-3814-810X
ORCID for Peter W.F. Smith: ORCID iD orcid.org/0000-0003-4423-5410

Catalogue record

Date deposited: 11 May 2016 10:30
Last modified: 30 Jul 2019 00:35

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