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?
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DOI：
10.1515/jos-2017-0037
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发表时间：
2017-09-01
影响因子：
1.1
通讯作者：
Smith, Peter W. F.
中科院分区：
文献类型：
--
作者：
Durrant, Gabriele B.;Maslovskaya, Olga;Smith, Peter W. F.
近年来，在无应答调查中，para - ata的使用显著增加。一个关键问题是，从当前和前一波纵向研究以及前一波调查信息中获得的包括电话记录数据和访谈者观察结果在内的para - ata在预测纵向背景下的响应结果方面有多大用处。本文旨在解决这个问题。最终响应结果和序列长度（对家庭的电话/访问次数）分别和联合建模进行纵向研究。能够预测呼叫序列和响应的长度可以帮助改进适应性和响应性调查设计，并提高呼叫调度的效率和有效性。本文还确定了模型的不同方法规范的影响，例如响应结果的不同规范。潜在类分析被用作总结序列呼叫结果的方法之一。为了评估和比较模型的预测能力，除了使用伪R-2值的标准方法外，还提出了从分类表、ROC （Receiver Operating Characteristic）曲线、判别和预测中得出的指标，伪R-2值本身并不是一个充分的指标。这项研究使用了英国大型纵向调查“理解社会”的数据。研究结果表明，基本模型（包括前一波的地理、设计和调查数据）虽然通常用于预测和调整无响应，但不能很好地预测响应结果。对前一波数据（包括通话记录数据、访谈者观察数据和变化指标）进行调节，可以略微提高模型的拟合程度。当对最近的呼叫结果进行调节时，可以观察到显著的改进，这可能表明非响应过程主要取决于样本单元的最新情况。
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-2 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.