Técnicas e Instrumentos

Grounded theory has become a popular approach to data analysis, since Glaser and Strauss’s seminal work, ‘The Discovery of Grounded Theory’ in 1967, by providing systematic guidelines for analysing qualitative data. While initially a strategy for qualitative data analysis, over time great diversity has emerged within the approach and this has enabled authors to acknowledge its analytical potential for quantitative and mixed methods studies as well (Charmaz, 2006; Strauss and Corbin, 1998). Grounded theorists advocate a ‘bottom up’ strategy of analysis, utilising empirical data to generate theory rather than testing hypotheses derived from

established theories (Charmaz, 2006). The extent to which the researcher must keep their data and theory development separate from existing theoretical frameworks has, however, been a point of contention. In its initial iteration, grounded theory advocated that the literature review be postponed until after analysis, in order to prevent the influence of prior knowledge (Glaser and Strauss, 1967). While some, including Glaser (2002), continue to adopt this approach, many including Strauss have stressed that it is more important for grounded theorists to maintain ‘an open mind’ than ‘an empty head’ (Strauss and Corbin, 1998). With this, grounded theorists may start out with an understanding of existing theoretical

frameworks (as in this study with the framework of value), however, categories utilised must remain contingent and open to change during the analytical process, as was the case here.

78 This openness to change was reflected in the iterative process of coding adopted in this study. Coding, together with forming part of the analysis, aids the organisation and reduction of data (Cope, 2010). Within grounded theory, after a short time in the field, researchers are first encouraged to conduct ‘open coding’ or ‘memoing’ to construct preliminary theoretical categories which explain their data. However, during the course of further data collection researchers ask questions of their data, changing these ‘open codes’, and forming them into more defined codes as ideas are winnowed down. This form of analysis was conducted throughout the data collection phase of this study. According to this analytical approach, data collection also continues until theoretical saturation is reached, when the addition of further data does not alter the theory generated (Cope, 2010; Glaser and Strauss, 1967) and this delineated the number of interviews conducted as part of this research. Of course, this was also limited to some extent by logistical and temporal constraints and interviews could not have carried on indefinitely.

Charmaz (2006:14) has emphasised that grounded theory relies on a foundation of ‘rich data’ which are “detailed, focused and full”. She suggests that data can be classified as rich where they “reveal participants’ views, feelings, intentions and actions as well as the contexts and structures of their lives” (Charmaz, 2006:14). Evidently, an emphasis on views and feelings requires the inclusion of in depth qualitative data and the transcription of approximately 12 hours of conversation from Stage III interviews therefore formed the basis of this analysis. Alternative forms of data such as behaviour maps, statistical outputs and photographs were also coded and utilised to inform other aspects of data richness. For instance, observational data collected in Stage I provided contextual information for qualitative responses and quantitative data derived from Stage II offered insights into the actions of individuals. Stage II questionnaires did, however, provide a further source of qualitative insight as qualitative questions and comments sections were coded in the same manner as interview responses.

The coding structure of this study was organised into People, Place and Theme codes. People and Place codes were primarily utilised to enhance comparability in analysis. All survey respondents were, for instance, given a people code which was their questionnaire number e.g. MRS001. This enabled data provided by a single individual but drawn from different sources (i.e. questionnaire and interview) to be brought together, allowing the consistency of opinion expressed by a single person to be ascertained and ensuring that the perspectives of interview participants were not ‘double-counted’. Place nodes were created wherever

79 participants referenced a geographic entity and these enabled an understanding of the overall impression of a place to be ascertained. Here, invariably, ‘Manston Park’ and ‘Pudsey Park’ featured but other scales of space such as ‘Roundhay Park’ also had codes created. Other entities such as Crossgates, Pudsey, Leeds, Bradford and Yorkshire were also included.

The structure of theme codes derived in analysis is provided in Appendix 5. As noted above, this structure evolved over time, emerging out of a process of initial open coding and subsequent code refinement and amendment. This necessitated the movement of a number of different codes within the structure. ‘Space’, for instance, was initially coded under ‘Facilities’, however, once it became clear that participants attached significance to the opportunities it allowed for activity, rather than its mere presence, it was reclassified under affordances. As coding occurs over time, there is scope for confusion and consistency becomes paramount. Coding was therefore reviewed for consistency within individual transcripts but also across forms of data as new forms of data were added. Code descriptors represented a further strategy to enhance consistency. At first glance, codes such as ‘Attraction’ and ‘Attractiveness’ could seem similar, however, descriptors were used to note that ‘Attraction’ referred to the suggestion that the park could serve as an attraction, drawing people into the area, while ‘Attractiveness’ was noted as the appealing physical character of the space.

The themes that emerged form the basis for the analytical chapters that follow this. For the most part, participants expressed themselves succinctly in relation to points of interest and, given this, together with the applied nature of the study, the choice was made to present shorter sections of transcripts instead of more extended passages or vignettes of ‘whole people’ as this was felt to not impoverish the illustration of key points. The use of shorter quotations also facilitated the integration of methods during writing up as insights drawn from interviews were not privileged in terms of presentation over those drawn from other forms of data such as qualitative questionnaire sections.

It has been emphasised that the iterative process of coding and grounded theory analysis can be difficult to achieve with large quantities of data and mixed data sources can only make this more problematic. In this study, however, as discussed below, Computer Assisted Qualitative Data Analysis Software (CAQDAS) was utilised in order to facilitate this process and improve data management.

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