Innovations in Digital Research Methods. Группа авторов
blood components. The data is geo-coded to Government Office Region (GOR) level.
ELSA is a longitudinal survey of around 11,000 people aged 50+ in England, which began in 2002. It includes information on key demographics, income, health and cognitive function. Both HSE and ELSA data are freely available and more detailed versions including additional variables and more detailed geographic information are available as restricted access via the Secure Data Service under strict terms of use.
Other sources of health data include consequential data such as General Practitioner (GP) prescribing records. The ADLS is facilitating access to such data by building links with data holders, developing standards and good practice for data sharing and providing training for researchers in safe handling, analysis and publication from such data.53 Real time prescription data would be a very powerful tool for mapping changes in health and well-being.
The UK Biobank has collated for research purposes genetic and other physiological and behaviour data donated by over half a million citizens for research purposes.54 Genomic data are a potentially invaluable research tool for the social sciences as well as health sciences. This includes studies where researchers use surveys of twins to try to identify the impacts of both contextual and inherited covariates. For example, research by Sturgis et al. (2010), which involved combining attitude data and physiological information, examined the genetic basis for social trust. In the Millennium Cohort Study,55 the collection of DNA from data subjects linked to the survey data is becoming more common. This resource has great potential as it allows the possibility of tracking genetic and environmental influences across the life course. As well as these intentional research resources, several commercial DNA profiling organizations have been set up, for example, Britain’s DNA, where the public are asked to donate a DNA sample.56
Other sources of health data include data traces of online searches recording patterns of health-related queries. Though there is some debate about the accuracy of such methods, the content of tweets and volumes has been shown to be of value in monitoring the spread of flu outbreaks,57 as have Wikipedia searches (see Ortiz et al., 2011; McIver and Brownstein, 2014).
Similarly, social media postings on health forums can also be collated and analysed. For example, in this anonymized Mumsnet post, a contributor seeks advice about what to do about a potential case of flu:
Not sure if this is just a bad cold or something worse but daughter and I both have it and husband, who’s had the flu jab, is fine. Is this risky to the baby? Do I go to the GP in the morning? I thought you weren’t supposed to go to the GP with flu symptoms in case you spread it about? Should I look out for reduced movements? argh don’t know what I’m supposed to do! Someone please advise as NHS Direct is on a four-hour call back. (‘ISS’, 2011)
Here a social science researcher could code for information including: language, location references, health, flu, use of services, anxiety levels, gender, responsibility for health within the family and health communication issues. The researcher could also look at replies and related posts. Such self-published data are rich in detail and of great potential value in providing examples of individual experiences. Innovation in research design and sampling techniques could aid the development of research that could go beyond qualitative data and individual stories.
It is notable that in health research, crowdsourced data gathering techniques have also been used as part of structured research projects. Examples include researchers using social media to identify people with specific diseases and using online discussion groups to identify examples of side effects of particular drugs. People identified in this way may then be asked to take part in follow-up studies. These developments have been described as akin to an ‘eBay for health research’, where researchers seek to recruit participants or where people might put themselves forward for participation in studies (Swan, 2012).58 For health research, a link could also be made to administrative data and FOI requests, such as for data held on government meeting notes in relation to disease monitoring and decisions on vaccination programmes and drug stock piling.
2.3.4 Data on Education, Training and Employment
Key data sources in the UK for examining patterns in education and training include the Census and the Labour Force Survey as discussed above. The cohort studies in the UK also provide access to information on education and training and its role in people’s lives in the longer term. There are three large UK cohort studies, which each include over 17,000 people in their samples: the 1958 National Child Development Study (NCDS), the 1970 British Cohort Study (BCS) and the Millennium Cohort Study (MCS).59 Participants are surveyed usually every 6–8 years, although the MCS is surveying at higher frequency during the early years. Each of these studies includes questions on family background, education, socio-economic circumstances, attitudes, life transitions and health. Both the 1958 cohort and the 1970 cohort are now very rich data sources with detailed information on substantial periods of people’s lives. Access to such data is free.
The Longitudinal Study of Young People in England60 includes questions on key demographics and covers such issues as school course options, extra-curricular classes, parental expectations and aspirations, household responsibilities, resources, absences, truancy, police contact and bullying.
Consequential administrative record data are also available for examining qualification levels of school pupils. The School Census61 is an annual exercise collated from UK school records. As well as school performance scores, the data includes information on each pupil’s: home postcode, school name, Free School Meal (FSM) entitlement, Special Educational Needs (SEN) status, gender, ethnicity and mother tongue. Access to such data is free, though researcher approval is required and the data have to be used under certain confidentiality conditions.
Other sources of data on education, training and employment might include: online discussion boards of career changes, training feedback, returning to work and individuals searching for work who create their own blogs. One anonymous example reads:
Hard working 19 year old farm worker looking for work placement on a mixed farm to progress onto an advanced apprenticeship level 3 ideally in the South/SW. (Anonymous, 2011)
Here a social science researcher could code for: type of job, age, gender and geography as well as language use and skills. They could also follow the blogger’s Twitter feed to see if the person’s circumstances change. Of course, as discussed in Chapter 12 (section 12.6), this does raise ethical issues concerning identification and disclosure. An anonymous example from a discussion forum reads:
The chances of getting another job with a company that will allow me to work part time and are understanding of the caring issues (so emergency time off when carers don’t come) is zero. And I am 45. (Anonymous, 2011)
A social science researcher could code for: age, caring roles, type of employment desired as well as linking with other posts. The data has similarities with ‘vox pop’ interviews where there is no structured sample frame. But the nature of social media provides the tools for follow-up contact, and linking to other information and sources. However, as has been outlined above, without a sample frame the value of such data is predominantly in terms of the details of individual experiences rather than as a basis for generalizing to a wider population. Moreover, blog posts need to be checked for being accurate representations, as far as that is possible.
2.3.5