Data analysis methods

Qualitative data analysis is often inductive and seeks patterns emerging from the data. It is shaped by the research questions, which guide the focus of the data, yet qualitative analysis can be messy and confounding. Yin (2002) states that analysing case study evidence is difficult as the strategies and techniques are not clearly defined. He advises that much practice, starting modestly and working thoroughly, is necessary in order to produce powerful analysis and compelling case studies.

I commenced data analysis while still gathering data from other schools. Johnson & Christensen (2008) report that the process of data collection and analysis occur simultaneously, with data analysis beginning early in qualitative research. They describe it as a cyclical process of gathering and analysing data, referred to as interim analysis. Creswell (2007) lists three main steps involved in the data analysis of qualitative research:

1. preparing and organising the data 2. condensing the data into themes 3. representing the data.

Once I had begun organising the data I embarked on searching for themes. I developed a provisional coding frame soon after the focus groups and discussions had taken place, as suggested by Barbour (2008), noting the main themes and grouping them under initial subcategories. I then transcribed all of the interview data verbatim, followed by a description of each of the cases and their contexts. Barbour notes the importance of avoiding the quantitative approach of pre-determining coding categories prior to data collection and analysis. This can lead to over-reliance on themes stimulated by the focus-group and interview questions, rather than allowing flexibility to incorporate themes introduced by the participants and other data collection methods. She also advises the analyst to question their disciplinary assumptions in order that they do not distort analysis. Biases in the researcher’s analysis, for example having a pre- conceived idea of the findings due to expertise in the research area, can influence the themes identified. Barbour also highlights the importance of being alert to the language, sentence structure and rhetorical styles, noting any tensions and expression of beliefs as polarities or continua. An important distinction is made by Burton, Brundrett, & Jones (2008) between a priori codes (themes anticipated prior to data collection) and in-vivo

codes (less obvious themes likely to require exposition and clarification by the researcher, and notably developed in focus group data), which I was deliberately mindful of throughout my data analysis. Once several detailed codes were identified, categorical aggregation ensued to establish themes or patterns. Barbour (2008) describes how some people like to go from very detailed codes, which they then group into broader themes, whereas others prefer to conceptualise in broad themes followed by separation into narrower codes - I have employed the first preference.

In order to avoid impressionistic evaluations in my analysis I generated a grid using the themes and codes to frame patterns in the data, which enabled me to identify more closely the features of the data that provided the greatest insights for my research questions, which in turn helped to identify the themes. As I identified subcategories it became apparent that some of them were appearing under multiple themes. For example, professional development was a subcategory under support from senior management, barriers to integration, and teaching as inquiry. The New Zealand Curriculum emerged under history, and reasons for implementing integration. The relatedness of subcategories grouped under different broad themes is viewed by Barbour as unproblematic and a reflection of the complex and inclusive nature of qualitative data. She suggests the production of coding diagrams of broad themes to show how subcategories are related, helping to develop a deeper understanding of the means through which participants’ viewpoints and shared identities are formed.

The intention of employing focus groups is to portray such shared identities, through the interaction between the participants, summarised by Barbour (2008):

Rather than simply extracting the comments made by individuals, huge dividends can be gained by paying due attention to what is happening during a piece of interaction, as the whole can be infinitely greater than the sum of the parts. (p.130)

Barbour warns of the overemphasis of concurrence in focus groups and to guard against attributing apparent group consensus to individuals’ opinions, as well as imparting an over-simplified representation of complex discussions. Systematic application of constant comparison requires focus on inter- and intra-group variation, so I have analysed the focus group data at both group and individual levels to ascertain the collectivity of perspectives and to avoid giving simply a descriptive report.

Once I had analysed the focus group data for each case study school, I incorporated the observation data and documentation from each school to explore whether the perceptions of the teachers were also evident in their practice. The combination of different data sources requires engagement in critical analysis in order to understand and explain underlying reasons for particular phenomena (Burton et al., 2008). Throughout the data analysis I continued to connect with the literature surrounding the topic to enhance the validity of the resulting codes and to ensure that all relevant codes were included.

I have presented my data with my analysis, rather than separately, as well as using my research questions as a structural device to support the analytic process, as advocated by Burton et al. (2008). Creswell (2007) suggests that with multiple cases it is common to give a detailed description and themes of each, called within-case analysis and then follow with a cross-case analysis of themes across the cases including assertions (interpretation of meanings). Chapter Four is largely a within-case analysis of each school, with a cross-case analysis offered in Chapter Five.