Final version holistic research for holistic practice: making sense of qualitative research data


Traditional methods of data analysis



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Traditional methods of data analysis


Whether qualitative or quantitative, most social and educational research is dominated by an approach to data analysis that has been termed ‘paradigmatic’ (Polkinghorne, 1995). The basic technique is to identify key categories relating to the original research questions, and then to code portions of the data according to these categories. The use of the ‘cut-and-paste’ wordprocessing facility is often recommended as way to extract and group data (Mason, 1996, Ritchie and Spencer, 1994). Software programmes such as QSR NUD*IST allow similar but more technically sophisticated facilities. Fundamentally, however, the process of data analysis is described as one of identifying similarities and differences between different extracts from the data (Dey, 1993, May, 1997). The process then moves on to the elaboration of more abstract concepts, and the interconnections that can be drawn between categories, with recommendations for the drawing up of matrices, typologies and spectra. Huberman and Miles (1998) advocate that this should be pursued with an ‘audit trail’ approach that would allow other researchers to trace each step in the process, and arrive at the same resultant findings.

A refinement of this approach is ‘grounded theory’ (e.g. Glaser and Strauss, 1967, Strauss and Corbin, 1998a), which Kim Diment discusses in relation to her own research below. Although this approach insists on the strict formation and distillation of categories to which the data will be assigned, it avoids prejudgements about what those categories might be. Grounded theory seeks to allow the categories to emerge from the data itself through a repeated process of analysis to ‘saturate’ the categories, followed by further testing in ensuing fieldwork, the collection of new data, and subsequent adaptation of the categories if necessary.



The etymological roots of the word ‘analysis’ mean ‘taking apart’, and we can see how apt a description this is for traditional methods of data analysis. It is literally a process of taking apart different portions of our data, and regrouping them according to categories in what is essentially a reductive process (Wolcott, 1994). It is ‘atomistic’, which itself derives from the Greek verb ‘to cut up’. The reader may already have discerned the potentially mechanistic tendencies of the techniques recommended in the research method textbooks cited above. The key characteristics of such research methods show striking similarities with the scientific methods which blocked scientific understanding over a century ago, and which could only be resolved through the adoption of radically different, holistic methods, as we have seen. As research students, we both experienced (albeit in different ways) pressures, some internal, some external, to conform to paradigmatic methods of data analysis. Both of us found such methods frustrating to our efforts to make sense of our qualitative data, and we will briefly recount our own individual experiences as illustrations of our argument. Although one of these projects was based in secondary education and one in post-16 vocational training, both dealt with issues of concern to the learning and skills sector as a whole, as well as highlighting issues of data analysis. Kim Diment begins with an account of her need to adapt grounded theory in order to represent her findings.

Pupil responses to Shakespeare in the National Curriculum (Kim Diment)


The topic of my research was concerned with Pupils’ responses to Shakespeare at Key Stage 3. The study focussed on Year 9 pupils aged 13-14 years, because at this stage in many pupils’ school careers they will encounter Shakespeare formally for the first time by sitting a public examination on a Shakespeare play (an obligatory part of the National Curriculum in England). In the light of the continuing debates and controversies about testing, at all levels, within the education system, I wanted to look more carefully at how such a ‘testing’ regime might affect the responses of pupils from a range of academic and social backgrounds in different schools from both the public and private sectors. Results from my earlier exploratory studies (in a suburban state secondary school and a City Technology College) had shown that pupil-responses were more wide-ranging than what I had expected from most teenagers having to study Shakespeare, i.e. “It’s boring!” and “We don’t understand the language”. They did say things like that, of course, but in doing so hinted at other feelings, concerns, exasperations and hopes. These suggested ways in which they viewed themselves as learners and participants - or not - in an educational system predicated on passing examinations, and the consequences of success or failure in that system for them later on in their lives (Indeed, such issues of diversity and disaffection may well be important aspects of student identities in FE.) The complexity and subtlety of the range of responses was becoming clear to me, and I knew that in analysing the subsequent data from the main research study, I would need an equally responsive method of data analysis to support that range.

The data itself comprised of two sets of interviews with focus groups of about six or seven mixed-gender and mixed ability pupils from each of the three different schools, (an inner-city school, a suburban school and in independent school) supplemented by interviews with each class teacher and departmental head, classrooms observations and school documentation.


Having ‘captured’ the data, I was now presented with problem of how to analyse it. Since my research study was concerned with Shakespeare, a Shakespearean metaphor seems apt to describe my feelings when faced with a Polonius-like taxonomy of the different research methodologies on offer:
tragedy, comedy, history, pastoral, pastoral-comical, historical-pastoral, tragical-comical-historical …

(Hamlet, Act II, ii)


To begin with, a choice had to be made between using qualitative or quantitative data analysis, or a synthesis of both. For my part, I had made the decision, with supervisory advice, to follow the qualitative route at an early stage in my research design. But once this decision was reached, the sheer range of qualitative approaches that were on offer, ranging from language-based to experiential approaches, seemed overwhelming. However, again with supervisory advice, I began a reading of grounded theory methods (Glaser and Strauss, 1967, Glaser, 1978, 1992, Strauss, 1987, Strauss and Corbin, 1997, 1998b).

Since its ‘discovery’ in 1967 by Glaser and Strauss, grounded theory has become increasingly widespread as a qualitative research methodology. Even critics of grounded theory have described it as ‘currently the most comprehensive qualitatative research methodology available’ (Haig, 1995: 281) and ‘as one of the most sophisticated and developed approaches to rigorous qualitative research’ (Reason and Rowan, 1981). Despite its popularity it has been subject to an increasing number of critiques in the intervening years (e.g. Bryman and Burgess, 1994a,b, Denzin, 1988, Haig, 1995, Layder, 1993, Reason and Rowan, 1981, Silverman, 1993, Thomas and James, 2001). Whilst it arose out of a reaction to the dominant positivist mode of the 1950s and 60s (Kinach, 1995) it should nevertheless ‘be understood within the predominantly scientific context in which it was created’ (Seale, 1999:100). Even the founders followed divergent routes, with Glaser (1992) himself criticising over-technical and rule-following behaviours that he felt Strauss and Corbin were espousing.

For me, the emphasis on allowing concepts and theories to emerge from the data, rather than subjecting the data to a priori analysis, was what I found most useful about grounded theory. However, it was only in the course of actually doing it that I realised how difficult a process it was. Nor was I alone:
Grounded theory research requires certain qualities of the researcher. In particular, confidence, creativity and experience (both of doing research and of the context (s) being researcher) are of great benefit. Accordingly, the research does not favour the novice researcher who may just be beginning to develop these qualities (Pandit, 1996: 12).
Caveat emptor indeed…

Before committing myself to grounded theory, I had also considered other qualitative research accounts using methods ranging from language-based approaches to narrative and experiential approaches. At this stage, knowing that I would probably be using grounded theory, but still not sure if this would prove to be the right decision, another literary metaphor surfaced. I now felt as if I had embarked upon an Alice in Wonderland journey, chasing an increasingly elusive White Rabbit through endless corridors where all the doors are locked: ‘and when Alice has been all the way down one side and up the other, trying every door, she walked sadly down the middle, wondering how she was ever to get out again’ (Lewis Carroll).

Finding and then managing to keep hold of the ‘golden key’ that leads out of locked rooms is a challenge, and one which does not necessarily lessen during the process of the research enquiry. For having eventually seized upon one methodological key - in my case, grounded theory - to make sense of all the unwieldy data that I was amassing, it soon became apparent that here in ‘Data Analysis Wonderland’ my solid, golden key had metamorphosed into something else. It had become a kaleidoscope. Familiar as we all are with the way that a kaleidoscopic image dissolves and changes in shape and form when the instrument is fractionally revolved, it can nevertheless be a disconcerting experience to discover that both the data and the methods you are using to analyse it are also dissolving and changing. The difficulty is to hold the shape steady enough for a composite picture to emerge and my specific difficulty with applying the tenets of grounded theory to the data analysis meant that I could no longer see the data as fixedly as I believed I needed to in order to arrive at a ‘definitive’ analysis.

This somewhat startling mixture of literary and visual metaphors that I have used does not reflect an ill-assorted rag-bag of methodological assumptions that I have grabbed hold of in my descent down the rabbit hole, but rather it tries to reflect the complex and multi-faceted nature of the methodological journey that I had embarked upon at the beginning of the research process. In using the words ‘journey’ and ‘process’, I also want to make explicit the link with the fictional and literal tropes of Polonius, Alice, the White Rabbit and the kaleidoscope. Stitched together in this way, they illustrate what I have come to think of as the transformative and propulsive nature of methodological enquiry. By this I mean that the methodological process is one that is not in fact fixed, as I had thought it to be, but is often beset by stops and starts. The painful lesson to be learnt from this is that, nevertheless, a momentum does develop which propels the enquiry towards resolution.

In my case, to get the momentum going again, I needed to understand two things much more fully about how I was engaging with the data analysis. The first was that I had to accept that it was indeed my data and that it would be my analysis. This sounds simple enough but in claiming such ownership I also had to accept the responsibility that went alongside such a claim. In particular this meant the responsibility that I had as an outsider to tell the truth about those people I had been fortunate enough to be able to study. ‘To thine own self be true’ exhorts that arch- dissembler Polonius, but what did it really entail to be true not just to oneself but also to others? The underlying question to be asked was ‘How can truthfulness exist in educational research which takes place in a post-modern context where certainties of any sort no longer exist, and one person’s truth can so easily be another person’s untruth?’ In the worst case this can lead to a paralysis of the research investigation, where it becomes almost impossible to present one’s own research-truth because in a relativistic context, that truth is inherently unstable, liable to be pushed aside and toppled by other truths. It becomes, in a sense, frightening to tell the truth and there is even a term ‘veriphobia’, coined by the American epistemologist Alan Goodman, to describe the phenomenon (Bailey, 2001). In his article, Overcoming Veriphobia Bailey (2001) tries to reclaim truth in educational research whilst acknowledging that uncertainties regarding one’s own position, beliefs, and theories can still exist:
Educational research, in this light, depends upon a conception of objectivity that is defined in terms of honest inquiry, openness to criticism and an unapologetic pursuit of the truth… Without a strong and ever-present sense of truth-seeking, along with a recognition that truth is very hard to find, inquiry becomes impossible, and academia becomes little more than a forum for political whim and fancy (Bailey, 2001: 169-170).
In answer to the question, ‘Am I going to be telling the truth?’ I needed not only to examine my own position closely, as an actor situated in the social world looking at other actors situated in their social worlds, I also had to acknowledge the range of biases that all the performers are likely to bring to their roles. As a researcher I needed to be aware of whose truth was being told as well as how it was being told.

Secondly, in looking beyond the horizon of my own research study at the range and heterogeneity of other educational research studies and the methodologies used to support them, I realised that if I was successfully to consider methodology as a hermeneutic aid, it was not until I learned to trust my own interpretative voice that I began to see how I could use methodology, instead of methodology using me. Indeed, I would say in looking back at my attempts, my perspective has been a continually evolving one, and somewhat different to the one that I started out with. I do not consider this to be a bad thing, for if the research process is to be properly reflexive, then the researcher is necessarily going to be evaluating methodology and its application not just to the current study but also to future research studies.


Methodological decisions entail coming to terms not only with one’s personal situation, values and beliefs, but also with the whole intellectual ethos which pervades research … one aspect of this involves standing up inwardly to conventional research assumptions and ceasing to be intimidated by ‘big name’ researchers (Salmon, 1992: 84).
Coming to terms with methodology involves the growth of confidence, something that is not always easy for the novice researcher. For me, this was realised when I could become confident enough to accept that I could use the methodological approach I had chosen in the way that best fitted the research questions that I was asking, whilst being aware that other researchers would have also drawn different conclusions.

I realised that I had to settle upon a methodology whose ‘precepts’ (to quote Polonius again) I could follow and develop in relation to my own research study in order to produce an interpretative truthfulness derived from a systematic and rigorous analysis of the data. In using a model derived from grounded theory I hoped that these apparently dichotomous demands would be met. After much effort and perhaps ‘thinking too precisely upon the event’ (Hamlet this time!), I was finally able to work within my own hermeneutic paradigm to analyse the data, using what might be called a ‘ventriloqual’ voice that would allow the educational stories that I was attempting to re-tell to unfold as freely and truthfully as possible.

Finally, I would just like to conclude this brief account of some aspects of my own methodological struggle with an observation about just what it is that I have done with the data analysis. It would be easier to say what it is that I have not done, which is to use grounded theory in a pure or classical sense, but I do not think I am alone in this respect. In using an adapted model of grounded theory which avoided what I felt to be the supra-refining of emergent categories, I wanted to allow the eventual core category arising from the data analysis as much dynamism and fluidity as possible so as to reflect and engage with the many and varied pupil responses to Shakespeare at Key Stage 3 in an holistic a way as possible.




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