7 RS-485 Instalación y configuración
7.4 Estructura del formato de mensajes del protocolo FC
The purpose of this story is to discuss three chal-lenges I (Holt) experienced while building a grounded theory for my PhD thesis. The study examined talent development in elite adolescent soccer players. The issues chosen for analysis relate to theoretical samp-ling, concerns about forcing the data and falling into an analytic rut. The nature of these challenges and the judgements made to ‘solve’ them will be addressed.
Overview of analytic approach
Strauss and Corbin (1998) suggested that grounded theorists are not so much interested in individual actors but are more concerned with discovering patterns of action/interaction with changes in condi-tions, either internal or external to the process itself.
Since I wanted to investigate the experiences of elite adolescent soccer players as they attempted to be-come professionals, the provision within grounded theory to focus on processes of change and interac-tions between individuals within structural organiz-ations made it appealing. I chose Strauss and Corbin’s version of grounded theory because it fitted with my philosophical approach and seemed to offer a series of guidelines that could be adapted to the logistical demands of my particular study.
The system of data management I employed was based on a progression from description, through conceptual ordering, to theorizing (Strauss and Cor-bin, 1998). Microanalysis and open coding were used to break the data down into discrete categories based on their properties and dimensions. Conceptual or-dering, which is the finishing point for some qualitat-ive studies, was the precursor for theorizing.
Theorizing involved the formulation of ideas into a logical, systematic and explanatory scheme, at the heart of which was the interplay of making inductions (i.e. deriving concepts) and deductions (i.e. hy-pothesizing relationships between concepts). Axial and selective coding were used during this stage to put the data back together in a coherent manner.
Challenge 1: Theoretical sampling
One of the more predicable challenges I faced in conducting this study involved recruiting an appropri-ate sample. Although sampling traditionally tends to become more focused as research advances (Strauss and Corbin, 1998), I identified a specific group of participants at the start of the project. I wanted to interview adolescent soccer players who were compet-ing at professional and/or international levels. I also wanted to speak to the coaches of these players. It was important that all the participants were operating in the most elite environments available to provide maximum insight into the developmental processes experienced in elite soccer.
As I was based in Canada, I recruited members of the Canadian under 20 and under 17 international teams, formally interviewing 20 players (average
age:16.8 years), informally interviewing their coaches and observing behaviours during training camps. However, I was acutely aware that soccer was not a major sport in Canada, and that for my theory to be taken seriously I would need to go to a major soccer playing nation. Therefore, to increase the potential of developing a useful theory, I also sampled 14 young players (average age:16.2 years) employed by professional soccer clubs in England and six professional youth-level coaches. Overall then, data collection consisted of three fieldwork trips – Mon-treal, Toronto and England – all completed during the summer of 2000.
In grounded theory the researcher engages in data analysis as soon as the first data are collected.
However, each fieldwork trip involved intensive data collection with two or three interviews per day, so it was difficult to fully analyse data immediately. As such, I engaged in more extensive data analysis between fieldwork trips, but the interplay between data analysis and data collection (whereby analysis leads to new questions to ask in the field) was limited at best. Once all the fieldwork had been completed, I started to compare the Canadian and English data. I discovered that new questions arose as I became more involved in the complexities of data analysis. I realized that I needed to go back into the field but I could not afford another set of fieldwork trips. To solve this problem during the final stages of data analysis and theory development, six informal confirmatory inter-views were conducted with older players (average age:25.2 years) who possessed professional playing experience in both England and Canada. These
‘second-round’ interviews helped add to the depth and variability of the data collected and facilitated increased interaction between data collection and data analysis. These players were able to provide alterna-tive examples by reflecting on their own experiences as youth soccer players, and they commented on my evolving theoretical interpretations.
Challenge 2: Forcing the data
It was important to separate the two data sets in order to analyse them for the purpose of cultural compari-son. All Canadian data were analysed first using the techniques of microanalysis and open coding. Once the Canadian data had been accounted for, and a variety of descriptive concepts and categories devel-oped, the English data were similarly (descriptively) analysed. As this point every effort was made to allow
concepts unique to a particular data set to emerge inductively from the data.
I was aware of the potential danger of forcing the English data into the pre-established concepts and categories and emerging conceptual framework cre-ated from the Canadian data. I was also aware that Glaser (1992) criticized the Straussian approach for forcing the data to produce ‘full conceptual descrip-tion’ rather than theory grounded in the data. I felt this could be a potential flaw of my study. Constant comparison was a particularly important technique to avoid forcing the data because it facilitated the comparison of concepts within a particular data set as well as between the respective data sets. It was important to ensure that raw data extracts included in a concept within a particular data set invoked the same attributes and dimensions. It was also important to ensure that concepts were used consistently to describe the data across the data sets. Accordingly, if data did not fit with an existing concept, a new concept was created. As the study progressed 21 concepts were created, represented by nine sub-categories and four main sub-categories. Engaging in the constant comparative process enabled me to tease out subtleties in the data, ensure that concepts were located in the appropriate subcategory/category and identify differences between Canadian and English experiences.
Challenge 3: Falling into an analytic rut One problem I did not anticipate occurred when I found myself falling into an analytic rut. Following the conceptual ordering of the data, the final theorizing step was undertaken. Theorizing is based on develop-ing explanations between the data whereby concepts are connected (using statements of relationship) to form an explanatory theoretical framework. This moves the findings beyond conceptual ordering to theory. I found it very difficult to suddenly switch into a ‘theory’ mode from the ‘descriptive’ mode I had been working in. It seemed that I had focused too much on following the order of description – conceptualization – theory building, rather than ap-proaching every step of the process with the intention of developing theory. This extract from my memos revealed the problem:
Although I’ve got a range of concepts, sub-categories, and categories they don’t seem to be coming together. I might be concentrating too
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much on describing what I think is going on, but the more I ‘interpret’ the further away from the data I get. I thought that by going through all the steps in the process, the theory would come together.
Maybe I got some of the description wrong (maybe I missed a step?). Go back and check.
Following the recording of this memo I engaged in a circular process of going back to the raw data and juggling certain concepts and subcategories, hoping that the connections between the data would be revealed. And thus the analytic rut deepened because I was still thinking descriptively (that is, I was concerned with ensuring that the appropriate raw data had been coded into the appropriate concept). I somehow expected the theory to come together of its own volition as long as I got the description right. In fact, I realized that I was working and thinking too much at the descriptive level, rather than looking at the bigger picture and attempting to make theoretical connections. I reflected that perhaps because I was a neophyte grounded theorist a more descriptive ac-count would be ‘safer’ (and easier to get past my committee!). But of course, theory cannot be devel-oped by description alone. Fortunately, my supervisor (Juliet Corbin) encouraged me to use a range of analytic tools to move beyond this analytic rut.
Inherent within the grounded theory coding pro-cedures are certain techniques that enable the analyst to make theoretical interpretations and form state-ments of relationship between concepts. I fully embraced these techniques in an attempt to break out of my analytic rut. The data were ordered to form a storyline that explained what was apparently going on.
Diagrams were used to visually examine relationships between categories. I reviewed and assessed my memos and notes intermittently and compared them with the emerging theory. The emerging theory was also compared with previous talent development research to illuminate plausible connections. Finally, using the comparative techniques of ‘flip-flop’ and
‘systematic comparison of two or more phenomena’
(Strauss and Corbin, 1998: 94–5), I compared adoles-cent soccer players’ careers to the career of a lawyer I knew.
The techniques helped change my mode of think-ing which subsequently enabled me to move out of my analytic rut and helped me to develop a better understanding of factors that underpinned the pursuit of a soccer career. For example, by comparing soccer players to lawyers I considered that a lawyer learns
his/her trade during adulthood, whereas a soccer player learns during childhood and adolescence. I went on to consider: ‘Do lawyers dream about the law during childhood? What factors motivate them to study late nights? When will they be rewarded for their many years of training?’ I then compared these thoughts to the demands facing adolescent soccer players. In doing so I was able to attain creative analytic insights at a more theoretical level as I sought to link categories together as opposed to simply describing concepts. Such ‘far out’ comparisons mir-ror the classic work of sociologist E.C. Hughes, who made comparisons between ‘professionals’ like psy-chiatrists and prostitutes (cf. Strauss and Corbin, 1998).
Conclusions
In the example presented here I learned the import-ance of theoretical sampling and maintaining close contact with participants throughout the study. The
‘second round’ participants were useful in helping confirm or refute some of my interpretations as I attempted to build theory. My concerns about forcing the English data into the Canadian findings were allayed by relying on the appropriate use of the constant comparative method. Finally, I was able to break out of an analytic rut by embracing a range of analytic tools that helped change my mode of thinking from the descriptive to the more conceptual and theoretical.
I learned that even though theorizing was the final step in developing grounded theory, theorizing can and maybe should occur along every step of the research process. I had spent a lot of time working on identifying and categorizing a list of unique concepts and categories rather than seeking relationships be-tween the data. With hindsight, I reflected that I could have engaged in theorizing rather than descriptive analysis as soon as the first data were collected. I realized that good research is reflexive and good researchers adapt to the demands of the situation.
Talking through problems with colleagues and work-ing where there is an atmosphere of support certainly help, but it is clear that research constantly requires judgements. Although some problems can be antici-pated and planned for, others can only come to light in the process of doing research. It seems that the lesson is to anticipate as many problems as possible while remaining flexible, reflexive and responsive to difficult decisions as they arise.
Annotated bibliography
Charmaz, K. (1990) ‘Discovering chronic illness: using grounded theory’, Social Science Medicine, 30: 1161–72.
This article demonstrates the range of insights and understandings that a researcher can gain on a subject, here chronic illness, using the grounded theory approach. It is also an excellent example of how to write up one’s grounded theory findings using a blend of theoretical formulations and words of participants.
Charmaz, K. (2000) ‘Grounded theory: objectivist and constructivist methods’, in N. Denzin and Y. S. Lincoln (eds), Handbook of Qualitative Research. Thousand Oaks, CA: pp. 509–35.
This chapter presents a constructionist view of grounded theory while at the same time retaining the method’s essential features. It is an important chapter because it shows how other authors conceptualize and think about grounded theory.
Clarke, A. (2004, in press) Situational Analyses: Grounded Theory After the Postmodern Turn. Thousand Oaks, CA: Sage.
This book takes grounded theory methodology beyond constructionism. It addresses differences and complexities of social life articulated from a postmodern perspective. The book provides another option for thinking about grounded theory and brings it into the postmodern era while retaining the theory building foundation.
Creswell, J.W. and Brown, N.L. (1992) ‘How chairpersons enhance faculty research: a grounded theory study’, Review of Higher Education, 16(1): 41–62.
This is an example of a theory generated using grounded theory methodology. Again it is useful to study the article in terms of how findings are presented in an article format.
Glaser, B. (1992) Basics of Grounded Theory Analysis. Mill Valley, CA: Sociology Press.
An alternative approach to grounded theory written in response to Strauss and Corbin’s (1990) book.
Holt, N.L. and Dunn, J.G.H. (2004, in press) ‘Toward a grounded theory of the psychosocial competencies and environmental conditions associated with becoming a professional soccer player’, Journal of Applied Sport Psychology, 4.
This article summarizes the thesis referred to in the Stories from the Field section above. It gives a detailed description of the theory that emerged and an account of how certain techniques associated with grounded theory were used to produce it.
Patton, M.Q. (2002) Qualitative Research and Evaluation Methods, 3rd edn. Thousand Oaks, CA: Sage.
This is my favourite general book about qualitative research. It’s well written, complete and charming. It doesn’t detail how to do analysis but it does take the reader through the entire research process.
Strauss, A. and Corbin, J. (1998) Basics of Qualitative Research, 2nd edn. Thousand Oaks, CA: Sage (1st edn, 1990).
This book now in its second edition is a bestseller. It provides a set of procedures and techniques that can be used to analyse qualitative data whether the aim is description or theory development. The procedures and techniques presented in this book are meant as a guide and not as a set of directives and should be used flexibly.
Further references
Denzin, N.K and Lincoln, Y.S (1994) ‘Introduction’, in N.K. Denzin and Y.S. Lincoln (eds), Handbook of Qualitative Research. Thousand Oaks, CA: Sage, pp. 1–17.
Hage, J. (1972) Techniques and Problems of Theory Construction in Sociology. New York: John Wiley & Sons.
Glaser, B. and Strauss, A. (1967) The Discovery of Grounded Theory. Chicago: Aldine.
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