The Researcher as Autobiographer: Analysing Data Written About Oneself
by
Colleen Tenni, Anne Smyth & Carlene Boucher
+

The Qualitative Report, Volume 8, Number 1 March 2003
(http://www.nova.edu/ssss/QR/QR8-1/tenni.html)


Abstract

This paper explores some of the issues that arise when one is dealing with data that has been produced by the researcher about their own experience. In particular, we are interested in exploring the ways that researchers can go about analysing autobiographical data. Many researchers produce data that is autobiographical. Ethnographers produce fieldnotes. Action Researchers often write about their own practice. Phenomenologists, sociologists and historians may write narratives that are autobiographical. There is a growing trend for researchers working in a range of settings to view themselves simultaneously being both a subject (or the subject) and a researcher. Data analysis techniques that work well when dealing with data about other people may not be as useful when one is working with one's own data.
We will suggest a number of strategies that a researcher can employ to analyse such data including collaborative analysis, forms of grounded theory and alternative forms of representation such as poetry, art and drama. We will also discuss the use of frameworks such as particular psychodynamic theories, feminist theories and critical theories as ways of gaining additional insight from the analysis of the researcher's biographical writings.

Key Words: Autobiography, Data analysis, Action research

Introduction

This paper explores some of the issues that arise when one is dealing with data that has been produced by the researcher about their own experience. In particular, we are interested in exploring the ways that researchers can go about analysing autobiographical data and we describe techniques that they can use to increase the rigour and usefulness of that analysis. First, we will discuss some ways of ensuring that the data is of the highest quality, as data analysis is only as good as the raw material that the researcher has to work with. Second, we will describe some ways of using professional supervisors and other individuals and groups to help the researcher move beyond their own assumptions and comfort zones. We will also briefly discuss how data analysis tools (such as grounded theory) and theoretical frameworks (such as psychodynamic theories) can also be used to help the researcher gain new perspectives. Finally, we will draw upon or own experiences to describe in a more personal way, the emotional and psychological anxiety that working with one's own data creates and we will mention some of the ways we attempt to deal with this anxiety.

For the purposes of this paper autobiographical data is data that '…contains information about the self' (Brewer, 1986). Stone (1981, p. 80) describes autobiography as being '…simultaneously historical record and literary artefact, psychological case history and spiritual confession, didactic essay and ideological testament'. Similarly, Berryman (1999) describes autobiography as being a series of paradoxes: fact and fiction, private and communal, lessons and lies.

Some writing about using autobiographical data is about using other people's autobiographical data. We are concerned here with data generated by the researcher about the researcher. The debate about the extent to which autobiographical data is 'true' (Brill de Ramirez, 1999; Neisser, 1982) is beyond the scope of this paper. The choice to use autobiographical data is often driven by the questions the researcher is asking. Research questions pertaining to one's own professional practice (e.g., Cherry, 2000; Boucher, 1995) or personal experience (Ellis, 1995; Ronai, 1992) clearly require the researcher to study themselves. Also, some research methods such as ethnography and action research are characterised by their use of autobiographical data. Ethnographers produce fieldnotes or keep diaries that often contain very personal data (Van Maanan, 1988; Ben-Ari, 1995; Coffey, 1999). Action Researchers may analyse and report on their own practice (Cherry, 2000). Phenomenologists, sociologists and historians may also use data that are wholly or partly autobiographical (Banks & Banks, 1998; Bartunek & Reis, 1996; Borsay, 1997; Richardson, 1992).

Data that researchers collect about themselves can be used in two ways. Some studies are intensely personal and most of the data collected is about the researcher. Other studies involve the generation of a range of data that includes some data about the researcher. For example, the researcher may be a participant in the study, the researcher may keep a personal journal as part of the study, or the researcher may keep a record of their thoughts and feelings as part of the process of ensuring that the data generation and analysis is rigorous (Coffey, 1999).

Using this type of data is well accepted in disciplines such as education (Bish, 1992; Gardner, 2001; Li, 1997; McKernan, 1991; Stoddart, 2001; Zuber-Skerrit, 1991) and nursing (Dooley & Hauben, 1979; Eubanks, 1991; Scherting, 1988), where an important and expected aspect of much research is reflection on one's own practice. It is also an accepted part of some research traditions that require examination of one's personal practice, such as action research (Dick, 1992; Greenwood, 1994; Greenwood & Levin, 1998) and of other research traditions, such as ethnography, where the personal experience of the researcher is viewed as being inexorably bound up with data generation activities (Ellis, 1995; Hannabuss, 2000).

There is a growing trend for researchers working in a diverse range of settings to view themselves as simultaneously being a subject (or the subject) as well as the researcher. Researchers working in disciplines as diverse in their politics and traditions as marketing (Reid & Brown, 1996), performance studies (Varner, 2000) and disability studies (Mairs, 1996) are working with autobiographical data. Research techniques that are based around the use of the researcher's autobiographical data, such as auto-ethnography (Ellis, 1999) and memory work (Boucher, 1995; Crawford, Kippax, Onyx, Gault, & Benton, 1992) are also becoming increasingly popular.

Some data analysis techniques that work well when dealing with data about other people may not be as useful when one is working with one's own data. We are not so much concerned with using data analysis techniques that remove or minimise researcher bias as '…authors never can choose to vanish completely from their texts; they can only pick the disguise in which they will appear' (Golden-Biddle & Locke, 1997, pp. 72-73). Rather, we are interested in ensuring that the researcher gets as much as possible from the data in terms of findings and also in terms of personal and professional learning for the researcher. In the next section, we will discuss some techniques that we think contribute to more effective autobiographical data analysis.

Creating Good Data

Data generation becomes problematic when working with one's own life. The focus on self and our practice, as researchers and practitioners, requires that we reveal, in all its complexity and as authentically as we can, what we do, how and why we do it and what this means about us and the field or context in which we operate (Argyris & Schön, 1977; Atkinson, 1999; Bish, 1992). The dynamic interaction between these variables is at the centre of our view of autobiographical enquiry and why we think it is inherently messy. We must write about what we really prefer not to write about. It is not about presenting ourselves in a good light - in charge, competent, controlled, organised and so on, or how we might like to be seen. Rather, it is about writing rich, full accounts that include the messy stuff - the self-doubts, the mistakes, the embarrassments, the inconsistencies, the projections and that which may be distasteful. It is about writing all of it (Cherry, 2000; Ellis, 1999; Prideaux, 1991).

This type of data is often not acknowledged and reported in conventional research literature, let alone worked with (Van Maanan, 1988). This type of autobiographical data is also quite different to what is usually presented in a conventional memoir or autobiography. Memoirs are often more about presenting a coherent narrative, with a beginning, middle and an end (Czarniawska, 1998). The data we gather for autobiographical research, particularly where there is a focus on reflecting upon our own practice is often disconnected, irrational and illogical. It is in this that it creates a greater wealth of issues for analysis.

So how can we produce any findings from this messy autobiographical data that makes sense to others and informs the scholarly community and the community of practice in one's field? If we include everything, how do we set reasonable boundaries around our research, how do we make it manageable and contain the beast? How do we know where to start and when to stop? It is here that we see the importance of engaging in cycles of data analysis, early and regularly. This enables the generation of theoretical memos (initial concepts, propositions, themes), progressively enabling 'saturation' of the data (Coffey, 1999). Data analysis must start early in the research process to avoid the tendency for researchers to gather too much data and to avoid the risk of drowning in or being overwhelmed by, the data.

The creation of good data in autobiographical research and the generation of rich material replete with issues for analysis cannot happen, unless the researcher is prepared to engage strongly and deeply with what is going on for them as they are immersed in the data gathering and analysis process. This means we need to develop a process for internal dialogue with ourselves. Practices in the fields of psychodynamic organisation studies (Conger, 1990; Hirschhorn, 1988, 1997; Kets de Vries, 1984a, 1984b, 1989, 1993, 1995a, 1995b; Long, 1989; Menzies Lyth, 1988; Schwartz, 1990; Smith, 1982; Whicker, 1996), human resource development (Atkinson, 1999; Carlopio, Andrewartha & Armstrong, 1997; Gardner & Boucher, 2000; Gardner, 2001; Hnederson, 1993) and organisational psychology (Fitzgerald & Kirby, 1997; Kohut, 1976) provide approaches to support this. This may mean keeping a journal to capture continuously what is going on in and around the research and especially where and how you are in it. The engagement with what is going on for us must be physical, emotional and intellectual. It is with the physical and emotional in particular, that we often get the first clue that something is happening and may be worthy of exploration. So our annoyance, discomfort, restlessness, sadness, excitement, triumph, tense neck, scratchy eyes or feeling of serenity is also data that alert us to something. This process may cause the researcher some degree of distress so having supports in place (such as a supervisor, colleague or support group) is important).

The Role of a Supervisor or Others in Data Analysis

The process of data analysis is often presented as being a solitary task with the researcher working away alone and then presenting their findings to others fro verification. When working with one's own autobiographical data, we believe there is also a need to engage in external dialogue with others, collaborators, subjects, supervisors (professional and/or research) and anyone else who can be pinned down and who will listen! The criticality of research supervision cannot be over-emphasised, but traditional supervision from an allegedly objective and distanced position is not appropriate.

The supervision must model the robust engagement the researcher has with their data, what we call 'a professionally intimate supervisory relationship' (Smyth & Holian, 1999). At the same time, the supervisor or other people helping with analysis must be able to step away but remain connected so that the data and the researcher are challenged to work at deeper levels. Openness to challenge and learning is essential for the researcher, the supervisor and others involved in data analysis. Quality and rigour in the data generation and analysis process depends on this.

Using Theory to Challenge One's Assumptions About Oneself

The place of theory in analysing qualitative data is a debate that is beyond the scope of this paper except to say this it is a matter that all researchers must consider (Blaikie, 1993; Bowden & Walsh, 1994; Crabtree & Miller, 1992; Fetterman, 1998; Glaser & Strauss, 1979). What we will discuss here is the ways we have found using theory at the data analysis phase useful in encouraging us to delve deeper into the data and to open up new possible meanings, alternative interpretations and learnings about our professional practice.

Writing of our biographical data in inexorably informed by the theoretical constructs that we use in our daily practice. Data is never theory-free. What we choose to write and how we choose to write it is constructed based on the ways we understand the world, our practice and ourselves. One of the challenges therefore, of autobiographical data analysis, is to attempt to identify and step outside the theoretical constructs upon which the writing of the data was predicated. As an example, if we are recording data about our experience of being in a group, the data will be laden with a whole lot of assumptions that we have about what is important/significant in groups, informed both by our prior experience, our 'theories in use' about group dynamics (Argyris & Schön, 1977) and our theoretical understandings of group behaviour.

As mentioned above, one of the ways of exposing these theoretical constructs is to involve a research supervisor or other colleagues in the data analysis phase. Another way is to apply a range of different theoretical constructs to the data. This can both help surface the deeper assumptions that the researcher has made about the data. It can also lead to vary different ways of thinking about an experience. For instance, Morgan (1997) describes a range of ways in which experiences of organizations can be theorised and describes how various metaphors can be employed to describe organisational experience in very different ways. These different metaphors also have the effect of problematising different aspects of organisational life, offering different ways of changing organizations and professional practice.

In our work we have found a few techniques and fields of study particularly useful in helping us look at our own data in different ways. One simple but very effective technique is to identify the level of analysis at which one is working and to use a theory that operates at a different level. If we are analysing data at an individual level, then we know that our data is imbued with a whole range of knowledge and assumptions that we have drawn from our understanding of various aspects of psychodynamic theory. It may then be useful to move to a systemic level and to use theoretical frameworks drawn from feminism and critical theory to help us understand what is going on in a different light. While psychodynamic theory problematises the person, more systemic theoretical approaches may help us understand the ways in which our behaviours are prescribed (and described) by others. Conversely, a description of an event informed by gender analysis may also be understood from interpersonal and intrapersonal perspectives and it may be these perspectives that are more useful identifying possible changes to our practice.

We also find it useful to draw on theory developed in disciplines other than our own and by people different to ourselves. The areas we work in like management, organisational behaviour, organization change, human resource management and leadership draw their theoretical traditions largely from social psychology (Bass, 1990; Burrell & Morgan, 1987; Conger, 1990). We have found a wide range of knowledges useful in uncovering the implicit theory in our autobiographical writings and analysis ranging from psychodynamic theories (e.g., Schwartz, 1990; Smith, 1982) to critical theory (Smircich & Calas, 1995).

We have also intentionally drawn on theory developed by people whose organisational and social positionality is different from our own. This informs our analysis in two ways. When we let our analysis be informed by writing from a marginal perspective such as disability theory (Barton, 1996), writing on race (Banerjee, 2000; hooks, 1982) queer theory or writing on masculinities (Cheng, 1996; Connell, 1995) we learn more about the ways we experience being marginal and mainstream. When we let our analysis be informed by writing from a majority perspective of white, male managerial life (e.g., Braham, 1987; Cawsey, Deszca & Mazerolle, 1995) we get a better understanding of the ways in which we experience organisational life as uncomfortable and how we may feel forced into acting in ways we do not like.

Personal Risk

Being able to really engage with one's own data also depends on understanding one's defenses and sources of resistance to difficult, unexpected and sometimes extremely confronting information or our reactions to such material. Otherwise the temptation is to discard, ignore, rationalise or prematurely intellectualise the information and thereby diminish the insights it may generate if it is worked with. This is not easy, the anxiety the data gathered in autobiographical research often unleashes can be great and the temptation to flee almost irresistible. Underpinning all of this is both the need for awareness of self and paradoxically, the search for greater self-awareness - perhaps these go hand-in-hand. The willingness to see, confront and discover oneself in one's practice and to learn from this is at the core of this work and central to the creation of good data. Below are three short stories drawn from our experiences of doing research that explores aspects of the personal risk of deeply engaging with one's own data.

Oh my god! - Did I really do that? (Carlene)

One of the hardest times for me was working with data generated by a memory work group of people with disabilities of which I was a member, the results of which are reported in part in Boucher (1999) and Boucher and Holian (2001). The memory work group was exploring the ways disability is constructed in the workplace and during the weeks, as we were talking about our stories and collaboratively analysing the data, I became acutely aware of my discomfort with the discussion and the fact that I was very quiet (a rare state). I initially put it down to group dynamics and the effort I had to put into facilitating discussion among a group of pretty strong-willed people. After much personal thinking and discussion with a number of people (including talking about how I was feeling at one of the meetings), I realised that the data was raising fundamental issues for me around my identity as a disabled person. Because my autobiographical data was also being scrutinised by others, my anxiety was heightened. Would the other group members also work out what a fraud I was? The two things that helped most in this situation were talking with my professional supervisor and learning to deal with my anxiety, embarrassment and my new learnings about myself. The other was to 'come out' to my co-researchers and to discover that they were also anxious about being exposed. This discussion lead to some really useful discussion about our collective need to manage our identities in public and the findings from the research were richer because we were able to share our fears. We went back to the data and found many examples where (just as we had been doing in the group) we engaged in behaviours to eliminate or minimise discussion abut our disabilities in the workplace.

How meaning changes over time (Colleen)

Autobiographical writing also allows for the interpretation of the collected data to be analysed over time and additional memories included. This can assist when the feelings at the time are extreme and 'scattered'. The following extract from journal writing is an example of one such response that required further explanation, but from a distance!

At last I pull out a new pen and begin to write. The struggle is much less now as I find myself easing into the position. The events of the past few weeks in the organisation haven't made me feel confident. There are real issues of leadership and followership and I have been put to the test! The organisation feels 'wobbly', the ability to make sustainable change appears out of reach for most managers. I have attempted to do what my supervisor advised me and the person involved has responded in a very positive way. When she left the organisation it was suggested that she liked to surround herself with 'yes' people. Where is the balance between offering positive feedback to senior managers and then being considered a 'yes' person?

To be able to take the time to add, expand and explain the response some time later so as not to detract from the data but further enrich the analysis and allow the author to write more coherently about the experience. The person involved in this short paragraph was a senior woman manager and I struggled with my relationship with her. I wrote about it, discussed her with my supervisor used grounded theory techniques to understand some of the language that I used in the data. I went back to the data some months later and wrote a portrait of the senior woman manager described in my journal. I painted a picture of her in my journal and then analysed the painting that I had created along with the words that I had used to describe her.

The paragraph jumps from organisational life, to gender in organisations along with feelings of incompetence, concern and fear! Some years later I can still visualise the senior woman manager and her management style. I could even now add data from my memory about some of the interactions I had with her or that I witnessed in group settings. I can quite easily see the personal me drowning in fear about being incompetent myself in a senior management position hence the need to jump from me to her. Being able to add further information and insights is a credible use of the data collection method in the same way as non-fiction writers have pulled together the pieces of their life and documented them into autobiographical writings. The rigour in this work is about the analysis and the analytical methods used to describe 'what is happening here'! The rigour involves moving back and forth with the data, being personally entrenched and then moving back with a subjective and an objective view of the themes developing in layers.

What is really going on here? (Anne)

I found the early stages of my research into Managerial Transitions really confusing, all over the place with widely varying views from my then supervisor, colleagues (academics) and myself depending on who I spoke to last! Not an uncommon scenario I know but I was struggling with how to sort this out and put some workable but stretching boundaries around the whole thing. I wanted to make it (the Masters) doable and actually get it done but I also wanted to do it with authenticity and learn. I wanted to learn not just about the issue, or the research process but myself and what I was capable of. I wasn't too sure I was capable of much at that point.

As this all unfolded I wrote about it in my journal. I had gathered some early data that flew in the face of the prevailing views in the literature. Being a good Catholic girl, I immediately thought, Oh my God, I've got it wrong! In speaking about this with a respected and experienced academic colleague and friend, I felt panicked because she reacted by saying, well then, you'll need to triangulate, increase your sample, check this, verify that, talk to a wider range of people and on and on it went. My anxiety about the manageability of this was immediate and grew as I wrote about it. And my sense of disturbance grew, something was not quite right. This feeling was strong, ill defined and uncomfortable. As I reflected on it, I realised I needed to check out these feelings rather than just put them away - my usual approach. This was hard for me, I didn't feel good about exposing myself, not looking capable and in control. I knew they were telling me something but I hated not knowing what and I feared what it might be.

I finally summoned up the courage to tell another colleague and he reacted quite differently. He was intrigued by the difference, thought it was great and commented on how interesting it would be to explore what that was about. It was such a relief to have another possibility before me and to have my instincts encouraged - my data might be useful and the direction it suggested could be worth pursuing. Interestingly enough, the first colleague rang me a bit later and said she realised she had 'run off' in a particular direction based on a set of interests and questions that were different to mine and was not listening to where I was coming from.

Somehow, the act of writing all this down, in all its messy glory, helped me explore the confusion I was feeling, gave me, eventually, a better sense of what was going on for me and others and a clearer sense of what I and they were getting hooked into. This exploration enabled me to identify and sit with my feelings of things not being quite right. This helped me confirm what were the essential concerns of my research, and it helped me trust and maintain my personal authority in the research process. It also showed me that it really was better if I didn't try to do it all on my own, that engaging others in my not knowing helped us all learn.

Conclusion

As the use of autobiography as a research approach continues to grow, we will learn more about ways to make it a more effective research tool. We will also need to continue to explore interesting ways of representing this data, ways that demonstrate its depth and power (Boucher& Holian, 2001; Ellis, 1999).

In this paper we have suggested four strategies. We first point to the importance of creating good data. We then discuss the importance of having an involved supervisor or fellow researchers who can work collaboratively with the researcher during the data analysis phase and we also discussed using different theoretical constructs to inform the interrogation of the data. Finally we discuss the need to take personal risks and indicate that when working with autobiographical data, the most personal, professional and theoretical learning comes when we take personal risk.

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Author Note

+Colleen Tenni has worked in senior management in healthcare organisations for over twenty years and is currently enrolled in a research degree at RMIT University. One of the management issues that holds a fascination for her is organisational dynamics and the maintenance of 'self'. Her contact information is Mrs Colleen Tenni, Trial Director, Coordinated Healthcare, Northern Health Service, Melbourne, Australia;
colleen.tenni@nh.org.au.

Anne Smyth works with practitioners about their engagement with personal and organizational change and transition. She is particularly interested in how one brings oneself strongly to management and leadership roles in this context and in the face of difficult organizational dynamics. Her contact information is Ms Anne Smyth, School of Management, RMIT University, Melbourne, Australia; anne.smyth@rmit.edu.au.

Carlene Boucher is interested in the development of innovative ways to study and report on management practice, especially the management of emotions in the workplace. Her contact information is Carlene Boucher, Associate Professor, School of Management, RMIT University, Melbourne, Australia; carlene.boucher@rmit.edu.au.

Article Citation

      Tenni, C., Smyth, A., & Boucher, C. (2003, March). The researcher as autobiographer: Analysing data written about oneself. The Qualitative Report, 8(1). Retrieved [Insert date here], from http://www.nova.edu/ssss/QR/QR8-1/.html

Colleen Tenni, Anne Smyth & Carlene Boucher
2003 copyright


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