Making the connection


In the face of survey fatigue, how can researchers gather quality information? Data linking could not only add high value to existing surveys, but also create new data resources. By Professor Peter Elias


S


urvey-based data collection methods underlie much empirical research in the social and economic sciences. Large- scale sampling and surveying of known


populations is a tried and tested method used to create many of the major data resources supported by the ESRC. Understanding Society is a good example of such a resource, as are the Birth Cohort Studies and, at an international level, the European Social Survey. Data collected by sample surveys are relatively


expensive to compile. Whether through face-to-face or telephone interviews, the process is both time- consuming and costly. Cheaper web surveys are becoming increasingly popular, but for high-quality research in the social sciences these do not provide the same degree of control over the information collected that can be obtained via an interviewer- administered approach. Response rates across all major surveys are


falling, possibly because people are leading increasingly busy lives but also because of ‘survey fatigue’ – a lower willingness to co-operate as more organisations attempt to gather information from individuals by survey methods.


Data linking provides another approach that


has the potential not just to add high value to existing surveys, but also as a means of creating new data resources which place no reliance whatsoever on survey-based methods. It takes advantage of our ‘digital footprint’ – the records we


22 SOCIETY NOW AUTUMN 2011


create as we go about our daily lives. These may record transactions we engage in, registrations we undertake, our communications with each


“ Issues must be resolved before


data linkage is a regular part of developing data for research


other and web-based activity. Examples include administrative records (eg, social security payments, income tax payments, school and college enrolments, hospitalisation and GP records), transaction records (eg, mortgage payments, electricity billing, use of loyalty cards when shopping), internet activity such as web searching and use of social media, and remote sensing records (eg, road transport sensing devices). While these examples reveal the diversity of the electronic information we generate, they share certain common characteristics: • they are, in most cases, personal records – they may contain detailed information about an individual that could be misused if placed in the wrong hands;


• they are not designed specifically as research resources even though the information they contain might have research value;


• they belong, in general, to big datasets covering large segments of the population.


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