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Advances in technology have increased the way in which questionnaire data can be collected. In particular, the rise of the internet and considerable market penetration of tablet computers and smart phones has increased the ease by which data can be collected and potentially the ease with which surveys can be completed by respondents. However, when online forms are used to replicate paper-based measures it is essential that electronic versions are read and understood in the same way as the original ‘parent’ document. Recommendations have been made to ensure that electronic forms retain their readability and meaning in the electronic context. However, what is less clear is whether different methods of collecting data electronically can have an influence on responses, data quality, and completeness. Thus, electronic data capture systems typically are built on the assumption that responses will not be influenced whether they are completed on a smart phone, tablet or PC/laptop. However, there is little evidence to support this assumption in the health context. The purpose of this thesis is to evaluate different methods of data collection and to determine if device type can influence data quality.


A structured review will be undertaken to collate available evidence on the impact of electronic data capture systems and their potential impact on responses. This review will not, at least in the first instance, be limited to health surveys. A preliminary scoping review suggests there is limited empirical evidence in this area. Any substantive findings from the structured review will influence subsequent analyses, proposals for which are detailed in the outline below.

The department is fortunate to have access to the Biobank questionnaire survey data. This resource is substantial with over half a million respondents, and a wide variety of data collected on individuals, including demographics, health status across a wide range of aspects of functioning and well-being, as well as information on how respondents provide their responses (i.e. whether via smart phone, tablet or laptop/PC) and the time respondents take to complete the questionnaires. It is proposed to assess whether there exist systematic differences in responses and data quality between devices on a range of health measures. Furthermore, the study will examine if particular device types are more appropriate (in terms of gaining high quality data) for certain demographic groups (e.g. age group, ethnicity gender).

Any systematic differences between device type and data gained will be explored in qualitative interviews, and an attempt will be made to determine if a manner of presenting questions can be developed that are independent of platform. This will be trialled in a small scale survey across demographic groups and device types.


Training course(s) in relevant psychometrics (e.g. Rasch analysis). Training in qualitative interviewing and data analyses if necessary. Other training as determined by the background of the successful candidate.


The successful candidate should have experience of using data analytic software (e.g. SPSS, SAS, STATA, any Rasch analysis software) and statistics, and some knowledge of epidemiology/medical sociology/health psychology and/or health services research. Ideally some knowledge and/or experience of qualitative research methods, especially in-depth interviewing.