Investigating call record data using sequence analysis to inform adaptive survey designs

Publication type

Journal Article

Published in

International Journal of Social Research Methodology


Gabriele B. Durrant, Olga Maslovskaya and Peter W.F. Smith

Publication date


Researchers 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.

Volume and page numbers

22, 37-54





Open Access; © 2018 The Author(s).; This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.

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