cy of the global financial crash;
• The construction of ‘good’ and ‘bad’ consumerism;
• In-borough inequality – with regards to income and resources;
• The competition for borough resources by participants – chiefly private housing;
• The construction of deviance – from criminal deviance to ‘cronyism’;
• Participant’s self-defined ‘liberalism’;
• Participant’s reported narrow engagement with public sector services & local issues

I selected the central theme of imagining the economic as one which would allow me to explore the central phenomenon of the legacy of the global financial crash through the lens of the remaining six themes. The data set included participants employed in both private sector and public sector making this a potentially rich set of cases to draw from.

I poised the research questions:
• how recession is factored into participant’s description of the present and projection of the future;
• can participant’s attitudes to austerity policies be gauged through the transcripts.

I had a number of hypotheses or hunches:
• That respondents’ language has traces of the public discourse of austerity and/ ‘big society’.
• That respondents who report being more financially secure may reference wealth inequality in the borough less
• That respondents see local council provision as not for them, comparative to their consideration of other residents in the borough
• That deviance might be associated, though not exclusively, with certain forms of consumerism

On a second read I open coded the text before grouping codes under parent nodes. Corbin and Strauss call this stage ‘the process of breaking down, examining, comparing, conceptualising and categorising data’ (Strauss and. Corbin 1990:61). At this stage I tried to code a number as from participant’s own wording (‘invivo’) – to remain as close to the text as possible, in line with a grounded approach. A number of codes were later grouped under the ‘priori’ code of deviance one with an antecedent sociological theoretical framework. My coding framework shifted throughout the iterative process between coding and analysis to settle on a coding framework.

Grounded theory is characterised by the risk of data fragmentation. To mitigate against this I coded chunks of text (typically full participant answers) rather than partial text. I further formed a grid to account for participants coded answers under different nodes to prevent decontextualisation from their overall interview and Excel demographic data.

When coding I used the two techniques Strauss & Corbin refer to as the ‘mainstay of an