EVALUACIÓN DEL ESTADO ACTUAL DE LA ESTRUCTURA

The use of computer analysis in qualitative data analysis has become more popular in recent years. As information technology develops at a rapid rate, computer assisted qualitative data analysis systems (CAQDAS) have become more sophisticated, from basic content analysis in the 1960s to today’s more critical functions such as assisting in theory building, modelling, linking codes and attaching analytic memoranda to specific points in the text (Saunders et al., 2009).

CAQDAS provide several benefits to qualitative researchers. They offer a systematic tool to organise hundreds of pages of text. CAQDAS also offer flexibility for searching, accessing and reviewing contexts from the data. Despite these benefits, it is also important to be aware of the potential disadvantages and limitations. One criticism concerns the role of researchers. The danger here is that these tools can distance researchers from their fieldwork and their data. Indeed, some believe that these methods presume ‘an objectivist, realist, foundational epistemology’ (Denzin & Lincoln, 2005, p. 638), and that their use too often takes for granted the interpretive procedures and assumptions that transform field notes into text-based materials. Another criticism is that coding may become a standardised procedure rather than allowing for a more emergent process of analysis (Coffey & Atkinson, 1996). The risk may be the physical separation of coded material from the original text. An emphasis on codes and categories can ‘produce endless variable analyses that fail to take account of important situational and contextual factors’ (Denzin & Lincoln, 2005, p. 638).

An important message here is to be circumspect about the role that computers can play in the process of analysis and data integration. It is important that the researcher avoids letting the computer (and the software) determine the form and content of interpretive activity (Denzin & Lincoln, 2005). They cannot perform the creative and intellectual tasks of devising categories and deciding which categories are relevant or of generating appropriate propositions with which to interrogate the data (Richards, 2009). Hence, ‘informal’ analyses of transcripts (and accompanying field notes and references) was an important part of the process. This research experience suggests that although the computer software was a useful tool for this study, it was by no means a substitute for close reading and immersion in the data and both methods were complementary in developing understanding.

The selection of appropriate software should be highly driven by the research objectives and methods (Denzin & Lincoln, 2005). In selecting the appropriate programme, several factors were considered. First, this study involved a significant amount of textual data from 37 interviews. This meant it would require a system that can manage several hundred pages of text. Another important factor was that the selected programme needed to be capable of handling Mandarin so that original transcripts can be coded and searched directly. Furthermore, the research was exploratory so features such as fast search and retrieval were necessary. This study used ‘template analysis’ (King, 1998) which means that a set of themes was developed from the literature review prior to the data analysis. This required a system which can store relevant articles and link them with the empirical data. It also required the system to have high levels of flexibility in

terms of changing, moving and merging codes. With these factors in mind, QSR NUD*IST (Non-numerical Unstructured Data Indexing Searching and Theorising) or NVivo 9.1 was chosen for this study.

NVivo is a powerful program that allows easy access to data and extensive automation of clerical tasks. The software allows users to classify, sort and arrange information, examine relationships in the data, and combine analysis with linking, shaping, searching and modelling (Patton, 2005). The ninth version has added several new features which were very useful for this project. PDF files can be now stored and coded, meaning that the literature can be imported into NVivo and analysed along with the empirical data. The software now recognises various languages including Mandarin, which means phrase searching can be done directly in Mandarin from the transcripts.

4.7.3 Coding

Coding is the process of making judgements about the meanings of contiguous blocks of text (Miles & Huberman, 1994) and is an essential part of content analysis. Unlike quantitative coding which is about data reduction by a system of symbols or numbers, qualitative coding focuses more on ‘data retention’ (Richards, 2009, p. 56). Coding is not merely the labelling of parts of documents about a topic but rather bringing them together to develop the identifiable and understanding relevant topics. The goal is to learn from the data, to keep revisiting the data extracts until patterns and explanations are seen and understood. Coding does not eliminate data but provides a systematic tool

to revisit it (Daniels & Cannice, 2004). Coding leads researchers from the data to ideas and from the idea to all the data pertaining to that idea.

Coding should be purposeful and the purposes strongly influence the standards of coding and analytical results (Richards, 2009). The first purpose was to gain information about participants (such as gender, age, marital status, and their IAs such as location, length of IAs, job position) so these attributes could be used to compare, link and explain patterns. The initial process ‘entails little interpretation. Rather, researchers are attributing a class of phenomena to a segment of text’ (Miles & Huberman, 1994, p. 57). This type of coding is referred to as descriptive coding (Richards, 2009). NVivo was very effective in facilitating this type of coding. Statistical information developed onto a spreadsheet was easily imported into NVivo. This information then was used to generate queries based on the sample’s attributes.

The second purpose was to develop a category of topics that emerged from the data. This was achieved by a type of coding referred to as topic coding (Richards, 2009) which is intended to provide accurate descriptions of the varieties of retrieved material and to develop understanding based on frequent patterns. NVivo can search for a particular word or phrase throughout the document and will code this into a particular node. The last purpose of coding was to see patterns from the data and to make, illustrate and develop categories theoretically. This was the process where ideas are ‘taking off from the data’ (Richards, 2009, p. 141), to progress into themes. It is often referred to as analytic coding as it is derived from interpretation and reflection on

meaning (Babbie, 2007). This was the core part of this study in which meanings were considered in context, categories were developed to express new ideas and connections were merged to form concepts. NVivo stored these ‘reflections’ in memos and annotations where they could be revisited. NVivo also allows for the data to be sorted based on their topics and categories so patterns and connections can be obtained.