Data Analysis Method

In this thesis, two methods to analyse data were employed: thematic analysis and content analysis. The description and application of these techniques will be discussed as follows.

Thematic analysis

Seeing a pattern or theme begins the process of thematic analysis. Boyatzis (1998) describes thematic analysis as ‘a way of seeing’ (p. 1). He argues that ‘observation precedes

159 understanding in that recognising an important moment is seeing, which precedes coding it, or seeing it as something, which in turn precedes interpretation’ (Boyatzis 1998, p. 1). As such, thematic analysis moves the researcher through these three phases of inquiry. In this thesis, the researcher utilised thematic analysis to provide a structured way of understanding how to develop thematic codes and sense themes.

The employment of thematic analysis in this thesis was conducted based on five phases of thematic analysis as recommended by (Braun & Clarke 2006): 1. data familiarisation; 2. initial codes generation; 3. themes searching; 4. themes reviewing; and 5. defining and naming themes.

All interviews were analysed by the researcher for the theme category analysis (McMurray, Pace & Scott 2004). This initial analysis was done by reading each transcript and highlighting narrative that offered a theme for example, introduction to work, receiving money, early saving and spending behaviours, and the importance of parental role modelling. Selected quotes that were representative of the theme were then transferred to a table.

In implementing this analysis, the researcher also adopted three stages of coding processes as proposed by Corbin & Strauss (2008): open coding, axial coding and selective coding to enhance the thematic analysis in this thesis. In the first phase, the researcher sought to familiarise the interview transcripts and memos through an open coding process. In this

160 process, the researcher tried to understand any themes that emerged from the data by conceptualising line by line. This process eventually led to the second stage of the analysis that was the generation of initial nodes. Nodes is a term used in NVivo that refers to concepts, processes, thought or ideas that are derived from sources such as research data or participants (Miles & Huberman 2002). NVivo analysis confirmed the six topics from the theme-category analysis.

Figure 4.4: Sample of open coding

Figure 4.4 shows a sample of forty-eight nodes or topics identified from a first pass of analysis using NVivo software. Sources refers to the number of interviews where the topic was referred to and References refers to the number of times the word or topic was identified in transcripts. The majority of nodes were in direct response to open-ended questions in the interview schedule as well as views expressed by participants.

161 Women were recruited from within financial services and other sectors to identify if women’s financial capability was enhanced or not, by working in the financial industry compared to those who worked in other sectors. To preserve the women’s identity, those working in financial services were identified by a pseudonym followed by (FS). Women employed in other sectors were identified by a pseudonym followed by (OS). Furthermore, ethnic and national differences and employment in these areas were not intentionally sought as the research focus was financial capability, with employment considered an important factor contributing to well-being. However, it would seem that regardless of women’s ethnic background and employment, the two categories of working in financial services or in other sectors were chosen to explore if influencing factors were similar or different for women in the two groups. Women in both sectors invested in property as their place of residence and for some, as an additional investment. However, women in financial services understood share investing and were willing to make additional contributions to superannuation because they understood these topics compared to those from other areas. Women working in other sectors appeared to have little trust in those who managed superannuation fund investments, and preferred to invest in additional property for future financial security, not direct shares or superannuation (Hogarth, Beverly & Hilgert 2003).

These distinctions shed light on the research question because even though these women had different work backgrounds and different views on how to invest for the future, these factors

162 did not adversely affect their financial capability, but their choice of investments to be financially secure was different (Faff, Hallahan & McKenzie 2011).

The next stage of the coding process is axial coding where this reflects the third and further thematic analysis phases theme searching and theme reviewing. In these two phases, the researcher coded the data by grouping the themes that were similar or connected to each other (Charmaz 2006). The classification of initial themes that emerged was refined and filtered based on the researcher’s judgement. This ensured that the generated themes accurately reflected the meaning in the data and provided a correct representation of participants’ views and experiences (Braun & Clarke 2006) on women’s personal financial management. The researcher also sought her supervisors’ opinions on the relevancy and importance of themes related to the research topic as well as its link to themes with other studies. Furthermore, memos were also used to assist the researcher to refine the themes and to ensure the data were analysed consistently in this process.

The last coding process is selective coding. The researcher identified the core themes that explain the factors influencing middle class women’s financial capability. This process reflects the fifth and last process of thematic analysis in the thesis where the researcher defined and classified the themes. In this stage, themes were finally categorised into the six main themes that were mentioned earlier.

163 The thematic analysis using NVivo enabled the researcher to develop the tree-structured indexing system as proposed by Richards and Richards (1991). Themes in this thesis were developed based on a hierarchy that creates the ‘tree’. The concept of Values, or Attitude to Money was an initial node or category. This was later shortened to Values as the parent node or root, while other categories that are linked immediately below the parent node are called child nodes. This concept of family structure is represented by child nodes such as Achievement, Ambition, and Care belonging to the parent node, Values.

Figure 4.5: NVivo Node Values, Attitude to money

Sources References

To enhance the data analysis in this thesis, the content analysis technique was also employed and will be discussed in the next section.

Content Analysis

The researcher identified themes that assisted in understanding factors that influence women’s financial capability through the thematic analysis. Utilising content analysis helped focus on the relevant themes that relate to the thesis framework and guarded the researcher from

164 making assumptions based on perception and not on data (McMurray et al. 2004). The content analysis is referred to as the constant comparative method in qualitative analysis (Glaser 1965). This method requires the analyst to convert qualitative data such as frequencies of events, words, action and other variables related to research data (Crowther & Lancaster 2009) into a numerical form to develop evidence for a given proposition.

This method contributed to the refinement of identified themes by noting frequencies in the speech patterns. NVivo assisted in this process where coding queries from the research data assisted the researcher to identify which factors were most frequently cited by participants by looking at the source of each theme and coding references.

By constantly comparing data, this thesis identified themes and patterns in the data. Referring to Figure 9 above, the process assisted the researcher to identify which factors were most cited by the participants by examining the sources of each theme and coding references. This supported understanding the importance of each theme or factor. For example, participants described the importance of being ambitious, enjoying a challenge or having a dream in motivating them to achieve financial independence. These values were balanced with the role of caring for others. This inclusiveness is indicative of women’s ability to be financially responsible for themselves and others.

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