Abstract
ABSTRACTResearchers have become increasingly interested in better understanding the survey data collection process in interviewer-administered surveys. However, tools for analysing paradata capturing information about field processes, also called call record data, are still not yet fully explored. This paper introduces sequence analysis as a simple tool for investigating such data with the aim of better understanding and improving survey processes. A novel approach is to use sequence analysis within interviewers, which allows the identification of unusual interviewer calling behaviours, and may provide guidance on interviewer performance. Combining the technique with clustering, optimal matching and multidimensional scaling, the method offers a way of visualising, displaying and summarising complex call record data. The method is introduced to inform survey management and survey monitoring. The method is hence informative for adaptive survey designs and will help to identify unusual behaviour and outliers and to improve survey processes. Sequence analysis is applied to call record data from the UK Understanding Society survey. The findings inform further modelling of call record data to increase efficiency in call scheduling.
Highlights
Many survey agencies nowadays routinely collect survey process data, so-called paradata (Couper, 1998; Kreuter, 2013)
A novel approach is to use sequence analysis within interviewers, which allows the identification of unusual interviewer calling behaviours, and may provide guidance on interviewer performance
This paper introduces the use of sequence analysis for investigating call record data to inform survey monitoring and management processes
Summary
Many survey agencies nowadays routinely collect survey process data, so-called paradata (Couper, 1998; Kreuter, 2013). This paper introduces the use of sequence analysis for investigating call record data to inform survey monitoring and management processes. Sequence analysis plots are combined with optimal matching, cluster analysis, and multidimensional scaling (Kruskal and Wish, 1978; Bartholomew, Steele, Moustaki and Galbraith, 2008; Piccarreta and Lior, 2010) This allows finding similarities across the contact histories and identifying groups of sequences that are homogeneous. The paper provides some practical guidance on how to analyse call record data using sequence analysis, for example regarding details on how to implement the method into practice including the use of software, how to detect unusual calling behaviours, outliers, unproductive call sequences and coding errors, and highlights possibilities for cost and efficiency savings. The paper concludes with a summary of the main findings, limitations and implications for survey practice
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