Computer assisted data analysis

An important decision I had to make regarding analysis was whether or not to invest time and effort in learning how to use a computer assisted qualitative data analysis software (CAQDAS) and I came across a number of authors with helpful views on this including Bryant and Charmaz (2007a); Cohen, Manion and Morrison, (2007), Gibbs (2002) and Webb (1999). Webb states that when CAQDAS such as NUD.IST and The Ethnograph were first developed, much of their original appeal lay in their potential to add objectivity and reliability to the analytical process (Webb, 1999: 324). However, Webb points out that these advantages were not always seen as compatible with qualitative frameworks so this kind of justification became less commonly used later on. For this reason, rather than justifying the use of CAQDAS on the basis of systematic rigour,

Webb recommends that researchers base their decision on the size and complexity of the data sets they are working with. Where data sets are modest, Webb concludes that the researcher is better off using manual coding as the software can take over and leave the researcher alienated from the data, and the data itself fragmented. Nevertheless, the ability of software to facilitate the handling of large and complex data sets seems to give it significant advantages over manual methods.

Although my own data set was of a relatively modest size (about 75,000 words), the complexity of the task of cutting and pasting sections of eighteen interviews with twenty-four participants justified the effort required to learn to use the software. The design of NVivo (version 9) also made it fairly accessible, and greatly eased the processes of storage and retrieval of transcripts and memos. The memos recorded how certain categories (nodes in NVivo) emerged from my analysis of the pre-pilot and pilot interviews, and were later dropped or merged with other categories, finally producing the four core categories of language, relationships, skills and group work processes, which informed the main findings of my research. The software also enabled me to easily retrieve data at any point during the analysis in order to carry out a process of constant comparison by checking that my conclusions were supported by my participants‟ accounts.

The following table (Table 7) depicts the top-level categories I used and their origins. The nodes in bold are the core categories (Glaser‟s term, see Holton, 2007: 279) which emerged from this process and which I use as organising categories for the presentation of my findings in the next chapter of my thesis. The concept of a core category is explained further in the following section of this chapter. It can be seen in Table 7 that whilst most of the categories emerged in the early interviews, not all were pursued as major themes during the main interviews. After coding the transcripts in sections using these broad categories, the comments were then iteratively recoded using subcategories which either emerged from my interpretation, or which were the result of reading which I undertook during the analysis in order to explore the comments using concepts which are well known in the appropriate literature.

Table 7: Origins of thematic categories for coding

Top-level nodes (thematic categories) Origin of theme

Cultural differences (living in the UK) Interviewing issues

Language difficulties

Living in the UK Motivation

Pedagogic differences (AL)

Work or business experience Quotable comments Recommendations Relationships

Skills

Mentioned by interviewees in first pre-pilot with comments going beyond classroom experiences.

Responses sought by author in pre-pilot group interviews in order to develop an appropriate approach for main interviews.

Mentioned by interviewees in first pre-pilot and throughout all of the interviews.

Mentioned extensively by students in first pre-pilot with some overlap between this and the node “Cultural differences”.

Based on literature on Chinese learners (see literature review)

Based on literature on Active Learning and practice at author‟s HEI, and also on the literature on Chinese learners. Within the theme of pedagogy, most students commented extensively on their perceptions of group work.

Mentioned by students in second pre-pilot, but not pursued in main interviews.

Occurred to researcher during the first interviews

Mentioned by students in first pre-pilot and incorporated into all interviews.

One of Van Manen‟s (1990) essential themes, taken up by students in first pre-pilot and developed as a specific interview theme throughout the pilot and main interviews.

Mentioned by students in first pre-pilot and developed during the main interviews to focus on academic (especially metacognitive) skills with more generic life skills being re-coded under the node “Living in the UK”.

An example of iterative recoding using subcategories based on analysis of comments and further reading can be seen in the screenshot (Figure 4 below) of the nodes I developed on NVivo under the categories of relationships and skills. Taking the example of skills in Figure 4, it can be seen that this category groups together a large number of comments, but more importantly for my interpretation, most of the comments either explicitly referred to or allowed me to infer a range of types of metacognitive theories held by the participants. As I began to develop this thematic framework, the software allowed me to go back to the interview sections and to check that my interpretation was supported by the accounts of my participants. For further clarification of how NVivo was used to assist the coding process, Appendix 4 presents a complete transcript, followed by a selection of coded transcript and an example of nodal analysis: a cropped screenshot of the summary and sections from interviews and memos coded under the subcategory: “Difficulty understanding English (other students)”.

Figure 4: Screenshot of the node frameworks “Relationships” and “Skills”

Webb (1999) refers to the danger that researchers can become ensnared by the ease with which CAQDAS allows vast numbers of codes to be generated, particularly where they work on the codes in isolation from the text. In order to avoid this, before attempting any coding, I listened to the interviews several times and produced a summary of each interview, which I then invited participants to approve or correct (see appendix 2 for examples of summaries and correspondence). In this way I attempted to ensure that I would not become “alienated” (Webb, 1999: 325) from the data, but would remain aware of the context within which each comment had been made.