Analysis of Data

Critical realism supports the idea that meaningful and agreed statements can be made about humanity based on the concept of an underlying social reality that contains points of agreement identifiable through naturalistic research (Bhaskar: 1979). The approach adopted during this project was fundamentally based on high levels of analysis and criticality, particularly necessary due to the qualitative nature of the evidence. Critical realism also addressed some of the main problems associated with naive realism, not least the importance of the relationship between prior knowledge and theory when analysing empirical data, and it also addressed the weaknesses associated with research that is too theory driven and not based on real world settings (Platt: 1988). This research project’s approach certainly had elements of grounded theory (Glaser and Strauss: 1967; Coffey and Atkinson: 1996; Gibbs: 2007), notably grounded theory’s advocation of the inductive construction of theories derived from empirical data. Critical realism, by contrast, arguably places far more emphasis on the interplay between theory and evidence, uniting inductive and deductive reasoning to create a form of abductive reasoning (Bulmer: 1984c), and high levels of criticality regarding what can legitimately be claimed, something Thomas (2009: 42) described as critical awareness or the ‘duty of doubt’. However, certain techniques used in the analysis of data, for example coding, are certainly associated with grounded theory and this should be acknowledged.

The first analytical technique used as part of field work and observations was analytic memoing (Saldana: 2013: 41-57). This technique is also associated with grounded theory (Glaser and Strauss: 1967: 112), but it is commonly used by researchers from all academic disciplines, associated as it is with note taking in all its forms. Essentially it is a form of immediate analysis and categorization, defined by Miles and Huberman (1994: 72) as a ‘theorising write up of ideas about codes and their relationship as they strike the analyst’. Certainly the increasing use of field notes for this study resulted in extensive memos. They were almost certainly a form of ‘initial’ or ‘preliminary’ coding

90 | P a g e (Robson: 1993: 386) for they contained an initial attempt at analysis and development of theoretical perspectives (Punch: 2009: 180).

During field work the lesson observation pro forma included predetermined categories. These clearly matched Robson’s (1993: 386-7) description of ‘organising’ or ‘summary’ memos, and they were virtually always completed either during the lesson, or immediately following it. An argument can be made that predetermined categories reduced the potential for more creative analysis, although many lesson observations ended with other thoughts and observations, including summary memos, and occasionally these were recorded in a field work diary or on the back of the lesson observations. In practice field work resulted in the creation of far more memos than had originally been anticipated, and not solely because of the increased use of field notes, but due to an increased respect for the importance of immediate reflection and analysis.

Follow up work incorporated the concept of ‘comparative analysis’ (Glaser and Strauss: 1967: 21-23)11, based on the principle of comparison and the identification of the ‘distinctive elements’ of the phenomenon under review, and was adopted to support the project’s aim of generating codes, categories, and eventually theoretical models. It was also preferred to the ‘analytical induction’ method (Glaser and Strauss: 1967: 104-5; Lincoln and Guba: 1985: 335) which tends to be associated with verification rather than generation of theory. Overall a priori ‘concept driven’ codes (Gibbs: 2007: 45-6) were the most commonly employed because of the identification of the elements that defined the nature of history from the literature review. Nevertheless, many examples of ‘open coding’ (Strauss and Corbin: 1998: 101-22), or ‘first-cycle’ codes (Saldana: 2013: 58- 66), were applied, particularly with interview transcriptions because of the detailed and unpredictable responses about issues such as curriculum decision making, managing curriculum development and reviewing the planning process. ‘In vivo’ (Coffey and

11 _{Also termed ‘systematic comparison’ (Strauss and Corbin: 1998: 93-4), or the ‘constant comparative} method’ (Flick: 2011: 211; Gibbs: 2007: 50; Thomas: 2009: 198)

91 | P a g e Atkinson: 1996: 32; Saldana: 2013: 91-6) codes were selected from particularly insightful or interesting quotations from interviews and ethnographic conversations. Much of this very rich data had not been accounted for in the literature review of cross- curricular and thematic teaching, and was therefore less easy to place in the project’s theoretical framework; it also had important implications for carrying out post-hoc further reading, and arguably became the most intriguing and original element of the empirical data collected. The project adopted Glaser’s and Strauss’ (1967: 37) advice to recognise diversity when using open coding rather than trying to force similarities in the data, and consideration was also given to their recommendation (1967: 105-13) to generate as many codes as possible before natural forms of limitation occurred such as integration and ‘theoretical saturation’ (1967: 111-2). In practice saturation levels occurred quite naturally. Ultimately some coding was concept driven, and some codes appeared inductively from the process of analysis.

Further levels of conceptual analysis were conducted through hierarchical techniques such as ‘branching’ (Gibbs: 2007: 73-5) and ‘laddering’ (Cohen et al: 2007: 439) to create ‘second-cycle’ (Saldana: 2012: 207-213) codes. ‘Axial’ codes (Strauss and Corbin: 1998: 123-4; Gibbs: 2007: 86-8; Saldana: 2013: 218-223) were then identified to make links between second-cycle codes and to account for the relationship and the hierarchies between them; these were then employed to generate flow diagrams. In the discussion chapter second-cycle and axial codes were incorporated in further levels of analysis resulting in the generation of theoretical categories (Saldana: 2012: 249-254) for each case-study school. Concept maps were developed to support the emergence of the three models of cross-curricularity than emerged from analysis at the level of each case-study. An example of this process is included in Appendix C.

Due to the multi case-study strategy, it was anticipated that some form of metrical analysis would be carried out for data presentation and cross-case analysis. The principal theorists behind the research plan were Miles and Huberman (1994) and Yin (1993; 2003). As further anticipated, the multi-case model that was used in the analysis

92 | P a g e chapter to summarise historical learning has been variously described as a ‘case ordered meta-matrix’ (Miles and Huberman: 1999: 188-93) or the ‘cross-case synthesis’ model (Yin: 2003: 133-136). In practice there are very few differences between both models and essentially they were treated as the same design. The use of a time-ordered matrix proved invaluable in presenting an overview of the analysis of historical learning (table 4.2) in the '3+ pilot' model, and also to present points of convergence and divergence. Ultimately it was not easy to order the three cases, as Miles and Huberman (1994) had recommended, because there was not a definitive hierarchy of either subject integration or subject integrity. Instead, almost from the beginning of the field work, construction of three theoretical models based on each school’s approach to curriculum integration had begun. These developed into arguably more interesting and subtle forms of analysis, but one model did appear to be superior and so analysis began with this. Thus the final matrix can be described, with qualifications, as a ‘partially ordered meta-matrix’ (table 4.8) (Miles and Huberman: 1994: 177-181).

This meant deviating slightly from Miles’ and Huberman’s (1994) advice to fully complete the analysis of each case-study thoroughly and separately before attempting cross-case presentation, organisation and analysis, but in practice since no definitive hierarchy had been identified, this did not make a significant difference to the eventual outcome. Indeed, Yin (2003) warned against trying to fit cases together where the analysis did not support this. A similar point can be made about attempts to overemphasise differences to aid the presentation of results. This study almost certainly did not fall into either form of misrepresentation; the case-studies selected through purposive sampling each operated through quite a different model of cross- curricular teaching and learning and it was comparatively easy to demonstrate the differences as well as the areas of commonality without traducing the evidence.

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