Developing a 'Semi-Systematic' Approach to Using Large-Scale Data-Sets for Small-Scale Interventions: The 'Baby Matterz' Initiative as a Case Study

The appropriateness of using statistical data to inform the design of any given service development or initiative often depends upon judgements regarding scale. Large-scale data sets, perhaps national in scope, whilst potentially important in informing the design, implementation and roll-out of experimental initiatives, will often remain unused for small-scale interventions involving only small numbers of service users or participants. This can be the result of legitimate concerns regarding representativity and rigour. Whilst large data sets may allow for strong claims to made regarding trends and tendencies with the population, a small scale intervention involving perhaps only tens of participants will not offer the opportunity to devise developmental rationales that are supported by such statistical rigour. This creates a situation in which important pilot studies and "suck-it-and-see" interventions are conducted in a basic empirical fashion that does not draw upon the informed reflection that large scale data makes possible. This article will argue that such a situation is not necessary. Through graduated and selective engagement with large-scale data sets it is possible to make good use of what they offer to inform the design and up-scaling of small pilot interventions. The discussion will focus on the case of a small intervention involving just nine parents and their babies conducted in Liverpool schools. More specifically it will consider the ways in which one national data-set, the UK Millennium Cohort Study (MCS), can be used to develop this project. It will do this by way of profiling the MCS in order to both select out those aspects that could or should not be used and those aspects that could be used beneficially to inform the development of the initiative. In the process it will devise a set of criteria that could usefully be adapted for use by others in relation to service situations in which similar issues of scalar mis-match between available data-sets and service design exist.