Qualitative Data Analysis of a Study on Meditation
Could you tell us something about your professional background and research interests?
I began meditating as a university basketball player on an athletic scholarship and found it not only increased my focus during the games but generally helped my activities as a student. I continued to explore spiritual pursuits which involved components of meditation, self-awareness, and mindfulness. I had a successful career as a financial consultant while still pursuing intensive practice in vipassana meditation in Burma under the guidance of a particularly gifted monk and teacher, Chanmyay Sayadaw. Eventually I was invited to ordain as a permanent monk and assist him in teaching this practice worldwide. I became well-versed in the Eastern models of mental health. I was particularly interested in how some of these models were being adapted and applied as interventions in the treatment of mental health issues within the community of psychologists. Accordingly, I enrolled in a PhD program in clinical psychology.
The research for my dissertation, for which I received the Francisco J. Varela Research Award, comprised a qualitative study of 11 advanced meditators in the Mahasi tradition of vipassana meditation practice. This research direction was particularly interesting and important as the Mahasi method of meditation had served as the wellspring from which a variety of mindfulness-based psychological interventions were derived. These mindfulness-based interventions had been well researched and, even as rudimentary mindfulness practices, had been proven to be extremely efficacious in the treatment of a variety of psychological disorders. However, there has been remarkably little research done into the experiences of advanced meditators in this wellspring practice. Therefore, I was particularly interested in investigating these experiences using qualitative research methods as a means to understand the first person narratives of a sample of practitioners in this tradition. As is common in the qualitative research paradigm, there is a vast amount of data to be managed, organized, and categorized. ATLAS.ti. was extremely important in the effective and efficient organization of the research data.
In what study are you using ATLAS.ti? Tell us something about it.
The title of my thesis is Mindfulness and Beyond: A Qualitative Study of Advanced Mahasi Meditators’ Experience. The purpose of this study was to qualitatively investigate the experiences and enduring changes of a number of advanced vipassana practitioners in the Burmese Mahasi tradition using qualitative narrative techniques of enquiry and analysis.
The following research questions explored enduring changes in everyday life as well as lived-experiences arising during practice:
- How has vipassana practice influenced/affected awareness, self-management, and relationships?
- How has vipassana practice influenced moral/ethical actions or behavior?
- How has vipassana meditation influenced general functioning and perception of environment?
What methods are you using?
I used qualitative methods of research, specifically, collected narrative data and appropriate methods of analysis were used in this study.
Participants and Recruitment
An advanced meditator is defined as one who has attained a certain criterion level of of meditative experience in the Mahasi tradition of vipassana meditation practice. Attainment of this criterion was ascertained by qualified teachers within the Mahasi tradition. Preference was given to those participants who met this criterion and had completed multiple month retreats under the guidance of a qualified teacher. The selection of participants was purposeful to optimize the potential for rich data collection based on their advanced experience. Eleven participants were selected and were asked to sign an informed consent form and complete a demographic questionnaire.
Procedures and Data Collection
Prior to participation each participant was asked to sign a consent and confidentiality form agreeing to all conditions of the study. A semi-structured 2 hour interview, meant to target experience during and after the practice, was the main source of data collection. The use of an interview guide was meant to increase thoroughness and consistency. All interviews were conducted and completed in a face-to-face manner at participants’ residences or in a hotel environment, and as such, the project involved a a fair degree of travel throughout North America. The interviews were recorded and transcribed for later coding and analysis.
I used the constant comparison method wherein data were compared (a) from one individual to another, (b) among different points in each individual narrative, (c) from incident to incident and, (d) from category to category. ATLAS.ti was used to facilitate data storage, management, and categorization.
Three levels of coding were used: (a) open coding, (b) axial coding, and (c) selective coding.
The first level involved breaking down the transcribed data into units of meaning or concepts, which were categorized and labelled. As additional data were gathered, coded concepts were compared to existing data and re-categorized.
Axial coding, the second level of coding, involved organizing and further explicating the relationships among categories by grouping them into more encompassing or key categories that clearly subsume several sub-categories. Another constant comparison method was utilized with four kinds of comparison; (a) comparing and relating sub-categories to categories, (b) comparing categories to new data, (c) expanding the complexity of the categories by describing the properties and dimension of each category, and (d) exploring variations or apparent anomalies.
The final stage of analysis was to create an integration of categories that is substantive. At this stage of the analysis the process of selective coding involved selecting a central or core category that integrates all other categories into a central story. Finally, the refinement of the theoretical construction is accomplished by linking or integrating categories around a core category.
After a final analysis of the data, a model of meditative processes and experiences emerged comprising insights and experiences which contributed to enduring and transformative changes in participants’ perspectives and life paradigms. The model includes seven primary themes; (a) meditative practice, (b) transformation, (c) mental/cognitive processes, (d) disturbing emotions, (e) relationships, (f) morality, and, (g) living life. The seven themes of this model, while transformative, are not necessarily universally developmental, sequential, or linear. A number of important and interesting sub-themes emerged and informed these overarching primary themes providing nuance and insight to the transformative processes of the practice.
The qualitative research paradigm presents itself as a challenging process involving the management and organization of vast numbers of pages of transcribed raw data. ATLAS.ti was used continuously throughout the entire analysis section of the project after data was collected and transcribed. The transcriptions were easily downloaded into ATLAS.ti and then the power of the software really presented itself. Not only did it store data effectively and efficiently but provided pinpoint accessibility to emerging themes and sub-themes. This made data management and categorization surprisingly simple and allowed the context of emerging themes to remain clear and available even over long periods of time.
Specifically, as illustrated in screenshot # 1 below, it was possible to go through the three coding processes and capture relevant quotes by color coding sidebars to correspond with the emerging individual and overarching themes.
While setting up the basic open coding of the individual participant’s data it was possible to categorize and make summaries of useful quotations using the code manager function (see screenshot 2). This function also served the processes of creating and categorizing the more general and overarching themes that arose as the analysis progressed.
Finally, as the central or core categories and themes emerged it was possible to choose a final selection of relevant and supportive quotes (see screenshot 3) which, with the help of Dr. Nick Woolf, were converted to a Word file (see screenshot 4). From this file, the final overarching themes, relevant sub themes, and supportive quotations were copied and pasted into the narrative of the draft presentation.
For those of us who are slow on the keyboard, as I am, it’s helpful to know that the ATLAS.ti software is compatible and works very well with speech to text software such as Dragon Naturally Speaking. It is important to activate the ATLAS.ti software first and then the Dragon program.
While I am by no means a master of ATLAS.ti, and no doubt there are much more sophisticated applications and uses of it to be learned , I am absolutely certain my dissertation research analysis and completion was greatly facilitated by this software package. As mentioned earlier, not only was I able to effectively and efficiently organize, categorize, access and summarize the project’s data, but the program facilitated the essential component of continuity over a long stretch of time. Therefore it is without reservation that I would highly recommend this program to novice researchers as well as more experienced investigators.
Any final words?
As I mentioned earlier, I am certainly not a “whiz kid” on this program yet. However, I will most certainly use ATLAS.ti for future qualitative research studies in which I may involve myself.
I think it is also important to highlight the support, both virtual and human, that is available in the “ATLAS.ti community”. The online tutorials that are available are very clear and helpful. Dr. Ricardo Contreras and Eve Weiss were important and supportive human components throughout the process as was Dr. Nick Woolf. May we never forget the value and importance of the interactive human dialogue as as new learning curves are engaged. I’d like to register my deepest thanks and appreciation for this team.
Contact information for Sean M. Pritchard
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