Rethinking coding and cross-case analysis: summary vs analysis

It could be argued that focused coding for the life history section was somewhat fluent because it had a predetermined organising framework; on the other hand, cross-theme

analysis was comparatively more difficult and took more time because the framework was a bottom-up development perhaps more closely linked to traditional forms of

grounded theory analysis. In trying to comply with the fundamental principle of grounded theory; i.e. generating bottom-up and grounded theoretical understanding, I

encountered two other challenges after I had conducted the cross-case analysis; and both led me to rethink the meaning of coding and cross-case analysis.

The first challenge was the difficulty of fitting every produced open-code into

developed categories (there were always some open-codes I could not use). The problem, I suspected, was that because not every line of data appeared to be

meaningful, not every open-code was useful. Perhaps this was unavoidable when adopting line-by-line coding. However, I then wondered how to claim that the product

was grounded in all of the collected data? Moreover, the second challenge was a growing awareness that making comparisons between so many open-codes/data was

not a sensible approach. It seemed to me that it should be meaningful to compare similarities and differences between different participants’ opinions upon the same

event, yet many of the open-codes I produced were about personal actions, incidents and turning points. I could not, therefore, figure out the point of comparing one

teacher’s background with another, or one action against the other in this manner. For example, one participant was an elite footballer but the other was not, or one was

involved in more administration but the other less. I kept asking myself the question: if the comparisons are simply about yes/no or how many, how is it possible to achieve

deep understanding? The above two challenges forced me to review my analytic process by asking: have I done something wrong and then fail to generate themes from

the produced open-codes? Should I compare something else? Or, again, what should open-codes look like in Nvivo 8? These questions troubled me, and guided me to

search answers by returning to a number of methodology books.

Charmaz (2006) indicated that “using codes to summarize but not to analyze” (p. 69) was a crucial problem of coding. However, in the stage of open coding, examples

provided in Charmaz’s book were very similar to summarizing because she used short open-codes. I kept wondering what the difference was between summary and analysis.

After I reached a section in Kvale and Brinkmann's (2009) book about “coding meanings”, I started to realize that “interpretation” should be the key distinction

between summary and analysis. In addition, I also understood there were different ways of interpreting. My position in interpreting data was not to produce “multiple

interpretations” that “extend the original text” (p. 201), but to understand each segment based on the “participant’s concern” (Charmaz, 2006, p. 69).

In an attempt to code/analyze data from a constructive or interpretive perspective, I

returned to a comparison of the coding process between the traditional way (coding on paper) and Nvivo 8, by asking an epistemological question: how should I code

meanings? or where is the meaning or interpretation coded/saved when doing analysis? On the one hand, when I conducted open coding on paper (Figure 3.04), short

open-codes were often suggested. It was assumed that the researcher’s interpretation about the meaning of the piece of data was generated along with the production of

open-codes. However, the produced open-code did not represent the researcher’s interpretation. Rather, the open-code could be seen as a mark-point of the researcher’s

interpretation, which was generated from the interaction between the researcher’s knowledge, research questions and the data. In this sense, interpretation was only

revealed when the researcher saw not only the open-codes but also the text. Afterwards, when moving on to focused coding, categories and sub-categories were

selected and linked by reviewing and comparing the open-codes in conjunction with the researcher’s interpretations. Thus, the use of short open-codes was suggested

because it could (1) promote the density of initial data analysis by allowing the researcher to do word-by-word, line-by-line or incident-by-incident coding; and (2) be

no distraction for later analysis because interpretation was revealed whenever the open-codes along with the text were viewed.

On the other hand, as was mentioned previously, it was not easy to view the

open-codes and its texts simultaneously. Thus, if I wished to build an interpretative understanding when using Nvivo 8, the open-codes used for later analysis had to be

not only of reasonable length, but also had to carry meanings. In summary, the open-code in Nvivo 8 was the record of the researcher’s interpretation of a specific

word, sentence or incident. Accordingly, the development of a theoretical framework could be built based on producing open-codes that carried the researcher’s

interpretations. Nevertheless, when a theoretical framework is only built upon produced open-codes, the researcher can be criticised for using computer-assisted

software to just ‘play around’ with the produced codes without deep understanding of the data (Charmaz, 2000). Indeed, the researcher might develop different

interpretations of the same text over time. To merely analyze produced open-codes can result in an understanding based on immature interpretations. However, from a

practical viewpoint, all grounded theorists eventually need to draw concepts away from the original texts when moving to more focused and theoretical coding.

Therefore, the biggest issue encountered when using Nvivo 8 to develop conceptual understanding was not that the original text seemed to be isolated once the open-codes

were produced, but how it was possible to bring the researcher’s interpretations – that were more mature – into the conceptual development throughout the analytic process.

In order to address these issues, initial coding of this study was divided into two

phases. Firstly, open coding was conducted on all interview data by paper, in order to gain an overall understanding of the data. Secondly, when I moved into Nvivo 8,

longer open-coding was used to carry the researcher’s more thoughtful interpretations and key information from the text. Moreover, in aiming to code meaning, the basic

techniques – word-by-word, line-by-line and incident-by-incident coding – of grounded theory could be explained in a slightly different way. When coding

interpretations, it was sometimes found that one line of text produced more than one open-code. However, very often, many single lines barely referred to anything when

they stood alone, but were well supported by the key meaning of the coded sentence/paragraph they belonged to. In this sense, while the key meaning of a

segment was coded, each word or line of the segment was also coded and attributed to the key meaning. Therefore, word-by-word or line-by-line coding were regarded as

techniques to inspect every piece of data carefully rather than readily produce a massive amount of open-codes whether they were meaningful or not.

Furthermore, in attempting to code meanings from segments that seemed to be

complex or fractured, I often tried to interpret the data by asking the analytic question: what was this participant trying to tell me by illustrating such actions, examples and

incidents? This analytic question reminded me to interpret the data from the participant’s perspective and prevented me from crossing the fine line between

“interpreting data and imposing a pre-existing frame on it” (Charmaz, 2006, p 68). In addition to interpreting data, the ‘Memos’, another function in Nvivo 8, was used to

record analytic thoughts developed during both the initial and focused coding process. Produced memos were used for (1) selecting codes to be categorised, (2) building

relationships between categories, and (3) developing a conceptual or theoretical framework associated with the studied phenomena.

3.10.5 Developing an analytic procedure: using Nvivo 8 to serve the analysis and