Publication type
Journal Article
Authors
Publication date
September 15, 2015
Summary:
Survey practitioners are increasingly interested in how best to use paradata to improve data collection processes. One particular question is whether it is possible to identify early during fieldwork those sample cases that may require a long time, and therefore a lot of financial and staff resources, until interviewing is completed. More specifically, we aim to identify
cases with long unsuccessful call sequences. This paper models call record data predicting final call outcome and length of a
call sequence. Separate binary and joint multinomial logistic models for the two outcomes are presented, accounting for the clustering of households within interviewers. Of particular interest is to identify explanatory variables that predict final outcome and length of a call sequence. The study uses data from Understanding Society, a large-scale UK longitudinal survey. The
work has implications for responsive and adaptive survey designs. The results indicate that modeling outcome and length of a
call sequence jointly improves the fit of the model. Outcomes of previous calls, in particular from the most recent call, are highly predictive. The timing of calls and interviewer observation variables, although significant in the models, only slightly improve the predictive power.
Published in
Journal of Survey Statistics and Methodology
Volume and page numbers
Volume: 3 , p.397 -424
DOI
http://dx.doi.org/10.1093/jssam/smv008
ISSN
23250984
Subject
Link
http://eprints.soton.ac.uk/375803/
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