Abstract
This chapter presents current research insights into various forms and representations of the results of self-as-subject data analyses from both expert and doctoral research experiences. Distinctions between heuristic inquiry and autoethnography are highlighted as well as differences between self-as-subject data analysis approaches when compared with convention methods used within other forms of qualitative research. In self-as-subject research, data representation and data interpretation are often also simultaneous or overlap in presentation to illustrate key findings and insights into the phenomenon of inquiry.
TopIntroduction
Our mind is the canvas on which the artists lay their colour; their pigments are our emotions; their chiaroscuro the light of joy, the shadow of sadness. The masterpiece is of ourselves, as we are of the masterpiece. ― Kakuzō Okakura (1906)
Data representation and interpretation across self-as-subject research can be quite integrated within protocols for self-as-subject research when compared to distinct procedures between data analysis and interpretation that may occur within other forms of qualitative research. Forms and formats for the representation of findings can vary creatively across autoethnographic or heuristic study (see Table 1). What is important here for the doctoral researcher is to comply with expectations of the respective discipline, doctoral research supervisor, and any institutional requirements imposed upon the self-as-subject research if it is to be a culminating demonstration for doctoral degree completion.
Table 1.
Representation of data and findings from doctoral self-as-subject research
| Author(s) | Method | Representation |
|
Fox (2018)
| Heuristic | Visual images juxtaposed with song lyrics |
|
Goings (2015)
| Autoethnography | Journal excerpts, personal narratives, vignettes |
|
Hokkanen (2017)
| Autoethnography | Character portraits |
|
Kumar (2017)
| Heuristic with coresearchers | Self and other narratives |
|
Lake (2015)
| Autoethnography | Personal narratives |
|
Ozertugrul (2017b)
| Heuristic | Poetry, allegory, narrative, self-interview dialogue |
| Yeh (2018) | Heuristic with phenomenology | Brief narratives |
Key Terms in this Chapter
Personal Narrative: Personal narrative is a narrative literary genre that utilizes literary devices and techniques of fiction as nonfiction; yet, is often considered a separate genre from personal essay or expository writing that may describe the same experience as it relies on voice, tone, character, and other fictive strategies. Personal narrative is often used across forms of narrative inquiry.
Data Interpretation: Data interpretation in self-as-subject research typically commences early on in the data collection process whereby layered meaning is uncovered and discoverable through the cyclical, iterative processes used for analyses. Data interpretation and data representation also often overlap through these phases as meaning is complex, layered, and presented as new knowledge or insight into the phenomenon of inquiry.
Literary Genre: Categories of literary compositions are known as genre. Genre and subgenre are typically organized by literary technique, voice and tone, content, literary devices, and sometimes format and length (i.e. fiction, short fiction, flash fiction).
Vignettes: Vignettes are characterized by brief narrative or visual accounts of an experience and often used as an evocative description for data representation in self-as-subject research.
Data Representation: Data representation in self-as-subject research refers to the manner in which data are presented as research findings or to illustrate research findings. Data may be represented as illustrative excerpts to highlight meaning of the new knowledge or insights or may be wholly represented as synthesized portraits of the experience such as by personal narratives, vignettes, character portraits, fictional or nonfictional story, poetry, visual art or images, and other art forms.