In preparation for the extensive (quantitative) phase of my research, I’ve put together a conceptual model of the determinants of young people’s occupational aspirations (at this stage, it’s essentially just a literature review). Although my research is focussed on the role of area effects, I need to know which other factors to include in my statistical model when I come to examine the BHPS/Understanding Society data. There are lots of candidates – from family background and social networks to academic ability and involvement in extracurricular activities. Even when it comes to the question of how area-based factors shape occupational aspirations, there are myriad ways of considering these, and indeed there are some contradictory findings in the literature – about the importance of deprivation, and local labour markets, for instance – which are due in part to the fact that studies consider these factors in different ways. Often these disparities are due to differences in research design, with large scale quantitative studies (which define differences between areas using metrics like the IMD) tending to find less evidence for area effects on aspirations than more localised case studies (which have the capacity to examine class-based norms, social networks and perceptions of place in deprived settings, for instance).
So my operationalisation of ‘area’ in the data needs to be as responsive to these nuances in the literature as possible. For instance, if those studies that identify a significant role for area-level deprivation focus on a particular aspect of deprivation, I may want to use specific domain scores from the IMD in my model, rather than overall scores, to capture the aspects of deprivation that are actually significant in relation to aspirations.