LOGROS DE RESULTADO.

Concurrent Data Collection and Analysis

In grounded theory, data analysis is both inductive and deductive (Glaser, 1 978; Glaser & Strauss, 1 967) . Initially, inductive reasoning shapes data collection. Emergence of data according to the way the participants see their world is critical. Glaser ( 1 978) emphasised that, as the problem becomes obvious, questions regarding emerging incidents and concepts guide theoretical sampling. As the study becomes focused systematic deduction of theoretical possibilities helps identify hypotheses that "guide the researcher back to the locations and comparative groups in the field to discover more ideas and connections from the data" (p. 40) .

Once data collection commenced reading was kept to the minimum to prevent going off at a tangent, consciously or unconsciously, and interpreting data according to vague speculations of what ought to be happening, rather than what was occurring (Glaser, 1978, 1 998) . Occasionally, the researcher could not resist reading about an emerging idea. However, interviewing was influenced by this concurrent reading so, in order to avoid forcing the story (Glaser, 1 992), literature was put aside until selected literature was reviewed formally following the clarification of the theoretical codes and the basic core variable. However, a later literature review was significant for promoting critical reflection about the ongoing theoretical development.

Time was taken to constantly compare incoming data and to plan subsequent interviews, in order to assure rigorous theoretical development. Data collection occurred over eight months in three waves. The frrst wave o

comprised the first twenty interviews when the initial participant group were interviewed, and the two teams were being observed and interviewed. A short break for intensive analysis followed until the second wave of interviewing began with the third team. Thirty _{interviews were completed.} At that point the researcher had to decide whether to continue collecting data. Theoretical development was progressing but the work was ordinary. Emerging ideas seemed commonplace. Where was the originality? What was different about this research? Where was the grounded theory? There were no satisfactory answers.

At the time, the researcher did not appreciate that, the synthesis of emergent concepts into a coherent theoretical explanation of behaviour, takes time. Another wave of interviewing with a new team, the fourth team in the day ward, was organised. That final wave of interviewing was well worth while as systematic theoretical sampling was developed, and a denser conceptualisation resulted. Tentative hypotheses were checked for similarities and differences. Thinking became more creative and interpretations more adventurous and sophisticated, as the researcher examined options, synthesising ideas into a tight, integrated set of hypotheses typical of a grounded theory. In due time, one hundred and sixty hours were spent collecting data from interviewing and participant observation. Data collection was probably more extensive than necessary .. but was driven by a researcher engaged in a learning experience. Developing autonomy as a researcher was a gradual process. Charmaz ( 1983) suggested grounded theory is a practice better learned through apprenticeship. Glaser's ( 1 99 1 ) advice was followed, albeit unwittingly:

If the researcher achieves autonomy by taking her work out of the hands of teachers and colleagues and by developing her own plan of research with its own pacing, this is an immeasurable contribution to the honesty and theoretical richness and results of the work. [S]he should provide [her] own training, because [she] is going in a different direction. Then, later, when the analysis is finished, she should bring the work back to the sociological fold as a contribution. (p. 1 3- 14)

The Interviews

In grounded theory research the beginning research question is very general. The earliest interviews were unstructured. The problem emerged as the study progressed and the researcher systematically clarified and developed directions of inquiry. In the beginning, participants were asked:

I am looking at how teams operate in the health service and would like to talk to you about your experiences. Perhaps we could begin with you telling me a little about the work you do . . .

Questioning varied according to the person, the time of joining the research, and the context. Originally, questions were intended to encourage focused discussion. The frrst eight interviews, with the initial participant group of experienced health professionals, were completely freely flowing. Data was run open (Glaser, 1 978) . No attempt was made to ask everyone the same questions although participants were encouraged to remain with the general topic. Free expression was welcomed. The researcher invited people to share nuances and detailed understandings about team actions and interactions. Being open to everything is important in an emergent grounded theory study, as patterns do recur in what is said and what is left out (Glaser, 1 978; Strauss, 1 987) . If something was left unsaid it was probably not important. In time, questioning moved to exploration of emergent analytical insights. Interview data informed participant observation so that the researcher's senses were sharpened and alerted to possibilities.

Participant Observation

During the project, teams were observed as people carried out their usual roles and responsibilities. Because human behaviour develops through interaction and action with others it should be checked out by observation so that participant information can be validated. Lofland and Lofland ( 1995) argue that participant observation occurs when a researcher spends

a reasonable period of time in the natural setting with the purpose of understanding scientifically the actions and interactions of the people. ObseiVation in the four teams combined well with intensive interviewing, helping the researcher check perceptions and recollections, developing deeper understandings of the dimensions and properties of categories, and uncovering new events not evident in interviews. ObseiVation of unexpected incidents produced fresh insights into everyday experiences.

Atkinson and Hammersley ( 1 994) have clarified the differences between participant and non-participant obseiVation. In the flrst instance the researcher is fully immersed, working with participants in the fleld. In the second situation the researcher is an obseiVer. Ashworth ( 1 995) criticised the latter role because separation perpetuates objectivity. The difficulty with that position is that positivistic objectivity is at odds with interpretive subjectivity and the development of a humanistic researcher-participant relationship. In this study an obseiVer role was chosen deliberately by this researcher, who is no longer a clinician. Although the researcher was a complete obseiVer (Morse & Field, 1 995) in the sense that she did not take on a clinical role, she was by no means passive. The researcher was certainly not concealed in any way; she was visible to everyone; and she engaged in social interaction with participants and staff members. However, the researcher was certainly a stranger who was establishing rapport through intermittent social intera.ction over time.

Another criticism of participant obseiVation is that people behave better when they are watched. ObseiVed behaviour is expected to differ from a person's normal ways of acting and interacting. A research study that uses interview only as data may attract comment because participants may say one thing and do another. While that is possible, unnatural behaviour is harder to sustain when a researcher designs prolonged contact with participants into a study, and is obseiVing people over time. Participant obseiVation becomes yet another opportunity to validate data, regardless of the source.

It is argued here that the participants who volunteered for this research study were hardly likely to be those with something to hide. Health professionals are used to public observation. They are scrutinised constantly by peers and patients in the course of their work life. Observing participants in their workplace helped the researcher to be alert for anomalies, distortions, and biases that needed to be clarified by further questioning. As a field researcher, she was surrounded by a continuous flow of data. What was important was how that data was focused systematically to develop theoretical relevance and purpose.

Originally, observation of all team meetings (See Appendix C, D, & E) was intended although this did not eventuate according to the plan. At the team meetings people drifted in so the researcher was not always known to everyone. Berg (1998) believes that, morally, passive processes do not allow for full information giving, or provide potential participants with sufficient opportunities to refuse participation. While it was reasonable to assume that health professionals had non-coercive collegial relationships, some team members may not have supported the researcher's presence if they were given an opportunity to voice their preference. If anyone appeared uncomfortable, at the meeting end the researcher approached individuals directly, explaining her attendance. As researcher confidence improved, more time was spent being seen in a clinical area, so that introductions were secured and information sheets distributed.

The benefits of participant observation increased over time. Participants became more used to the researcher wandering around, taking notes, and asking questions. Seldom were clinicians interrupted in their clinical work. The pace of life was rapid. Time was precious. Questions were usually saved for interview. The interplay between data collection processes was brought home to the researcher early in the study when she set out on a morning ward round. The researcher noticed team members teaching colleagues. The researcher presumed "informal teaching" was an important clinical role. Reading about learning organisations had skewed the researcher's thinking towards notions of teaching and learning.

Preconceptions were revised during an interview with Marilyn (See Appendix F). In that interview Marilyn described the situated context of knowledge.

Participant observation alerted the researcher to the contradictions and paradox inherent in practice. Looking back on the field notes (see example in Appendix G) many other incidents were documented as well, suggesting that the "teaching" emphasis was somewhat simplistic as a later theoretical memo revealed (See Appendix H). In the early stages of the study the researcher was sorting out how incidents compared to each other and whether teaching was to be identified as a significant category. Hazy ideas about "informal teaching" were renamed several times as each incident was compared with others in the coding process. This kind of rigorous coding, a coding-recoding strategy, is essential if a study is to be true to the grounded theory method, and fmdings are to be trustworthy.

NUDIST - A Computer Tool for Analysis

Computers are a useful tool to explore impressions, to create new ways of looking at the data, and to assist the competent computer person to discover unrecognised ideas and concepts (Richards & Richards, 1 994).

NUDIST (Non-numerical Unstructured Data Indexing Searching and Theorising) , is a code and retrieve system that is a useful tool to help the researcher edit and explore documents. NUDIST does not analyse data, the researcher does. In a grounded theory study "because the categories and meanings are found in the text or data records, this process demands data management methods that support insight and discovery, encourage recognition and development of categories, and store them and their links with the data" (Richards & Richards, 1 994, p. 446). The computer is a technical tool for storing a large amount of data, and retrieving data quickly and efficiently (Berg, 1 998).

Ready access to the data helped the researcher be alert to new or unexpected connections that were checked out quickly. To illustrate, if the

analyst noticed repetitive pattems, a word search was run to see how often.

and across how many interviews that notion occurred. If an idea occurred in three documents out of one hundred, the researcher concluded that it was probably unimportant. Ideas were scrutinised carefully, to see if they were upheld and supported by empirical evidence consistently. But, like all computer programs, NUDIST worked better when the operator understood the program.

NUDIST has two main systems, a document system that copes with document storage, and an index system. The latter is designed for "the user to create and manipulate concepts and store and explore emerging ideas" (Richards & Richards, 1 994, p. 457) . In the index system there is a series of nodes that are used to organise data. These are arranged hierarchically to allow the researcher to code a category and organise incoming information accordingly. Under a category, the analyst formulates various codes that are labelled according to the properties of that category. As the researcher used the causal model to examine categories, nodes representing the causes, context, conditions, strategies, consequences, and covariances of the category should have been developed. However, despite some elementary training in using NUDIST, this researcher's computer organisation skills were less sophisticated. Hindsight is a valuable teacher.

Using NUDIST had both advantages and disadvantages. The program was useful to manage and retrieve data. Because data could be copied and pasted, and moved anywhere, new ideas and creative links could be readily tested. Having a tool to move data about freely assisted the researcher's flexibility of thought. The main difficulty was in not taking enough time to learn how to use the program fully. The program had enormous potential that was not realised. Computer comfort level (Berg, 1 998) is achieved with effort and time. However, despite that, computer technology was invaluable to manage the large amounts of data generated in this project.

helped the researcher sort out, summarise and synthesise incoming information, so that it could be used effectively.

As material poured in, data were examined line by line for similarities, differences, and consistencies in behaviours or phenomena (Glaser, 1 978; Glaser & Strauss, 1 967) . Incidents were compared with incidents, incidents with categories, and category with category. In substantive coding, data were arranged according to common content organised around conceptual ideas that were combined into a category. Codes were labelled using phrases that summarised broad descriptions (See Appendix I). The natural language of the participants was used if possible. Those in vivo codes were expressions that conjured up a rich picture, a vivid image of what was happening in a particular context. The term pioneering new structures was an example of an in vivo code that was retained as a category.

Categorical analysis was complicated. For example, many incidents described roles. Role position, clarifying role, pioneering roles, redefining roles, ambiguous roles, hidden roles, and role confusion all emerged. Where did they flt? Which were the indicators? Were participants saying the same thing? Were there distinctions between the words? Was role a category? The researcher searched for answers asking how and why questions as options were explored (Charmaz, 1 990) . As data were sorted the indicators pointed towards a category of role redefinition. Role incidents were similar as all shared the role component, but differences existed in relation to position, definition, clarification, confusion, and ambiguity.

As analysis proceeded understanding deepened and some characteristics were merged into one idea. Role clarification and role position were

combined into role understanding that seemed to be connected to a·

category of blurred boundaries. Roles and boundaries were closely

connected in the data. How close were those connections? Should they be

separated? Perhaps a more accurate category was pioneering new

structures? Why did the concept of role understanding stay as an indicator instead of a category? Data were essentially descriptive and at the concrete

level of development. In contrast, pioneering new structures was more abstract. It subsumed a range of situations and had a broader meaning than a descriptive concept.

The analytical process is not linear. Overlap in categorical indicators is inevitable. Indeed, Strauss ( 1 987) suggested that the interchangeability of indicators is a sign of category saturation. Even though fme detail might be slightly different, the general ideas "add up to the same thing" and "nothing new happens" in the data (p. 26). Whether an incident was lifted to the categorical level or not also depended on how well questions were asked, and the density, specificity, of the discussion. Did people tell the researcher anything new? Was it significant? Ideas that occurred early on in the study did not always retain their importance when data were sorted again, and recoded as conceptual density and explanatory power developed. Sometimes, a promising description simply disappeared from the data analysis, as its importance lessened and the analyst took the study in another direction once selective coding was under way (See Appendix J) .

Concepts and incidents were compared and verified as the theory was refined and categories saturated (Glaser, 1 978).

Glaser ( 1 978) has commented on the interchangeability of indicators and suggested the analyst decide on what is reasonable. Indicator interchangeability means other researchers will make different interpretations of the data. Right answers do not exist. What was important was that codes were developed systematically using the concept-indicator model. "This model provides the essential link between data and concept, which results in theory generated from the data" ( Glaser, 1978, p. 62). It is quite different from concept analysis techniques where concepts are constructed by adding up indicators (Rodgers & Knafl, 1993; Walker &

Avant, 1 988) . In the concept-indicator model indicators are compared to each other as the researcher is "forced to confront similarities, differences and degrees of consistency of meaning between indicators" {Glaser, 1 978,

p. 62).

The search for scientific uniformity was involved. The researcher monitored the process by constantly returning to the data, to confrrm conceptual accuracy and recurrence. The computer program NUDIST facilitated specific word searches. Sometimes the researcher thought that an idea was common only to discover that a NUDIST search did not confrrm such a perception. Stem's ( 1 989) view that qualitative researchers work with words as well as numbers was well founded. "We use words, clustering them, ordering them, and building them into a picture of reality. We map data and draw pictures about it, and we try to see how it moves and changes" (p. 137). So, even though interpretive researchers work with words rather than numbers, numbers assist in analysis and interpretation of data as long as they "do not blur the vision of the researcher" (p. 136).

Visions were clarified once data were examined minutely, and compared and contrasted with the overall picture. As analysis progressed and categories were saturated, it was harder to treat categories as single entities. Many were interconnected. It was up to the analyst "to weave (the ideas] together into a processual analysis through which she can abstract and explicate the experience" (Charmaz, 1983, p . 1 1 7). For example, when the researcher questioned the different types of roles, analytical inquiry was developed from the data. The researcher used knowledge of a situation to help decide which leads to cover next. Did role connect with competency? Perhaps roles were a strategy in breaking stereotypical images? Could it fit anywhere else? As leads were pursued the depth of description was sufficiently intense to enable conceptual reduction, simplification, and synthesis into a coherent explanation revolving around a basic core variable.

Memo Writing

Memo writing was another procedure essential in the process of theory development. Analytical memos were the researcher's independent notes