Self-Discovery: Data Collection From Self and Others for Individual and Societal Implications

Self-Discovery: Data Collection From Self and Others for Individual and Societal Implications

DOI: 10.4018/978-1-5225-9365-2.ch005
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Heuristic, autoethnographic, or other biographical approaches to doctoral research allow for a deeper understanding of self in context of a phenomenon experienced by the self-as-subject and the greater understanding of others, society, and culture. This chapter presents current research insights into data collection processes used for self-as-subject research at the doctoral level. Illustrations of the variety of data sources used for both heuristic research and autoethnography are presented as well as insights and recommendations from method experts are included.
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By plucking her petals, you do not gather the beauty of the flower. ― Rabindranath Tagore (2017)

Data collection methods and procedures used for autoethnography vary widely, which can be initially confusing for new doctoral researchers as they range from the generative writing to connect the personal with the cultural as advocated by Ellis and Bochner (2000), and allows for expansion beyond the inner and outer life to expose the lived experience that Badenhorst and FitzPatrick (2017) noted as constructed and reproduced socially. Further, the authors described it as a “scholarly gaze on their intimate memories, reminiscences, and nostalgias” (Badenhorst & FitzPatrick, 2017, p. 3), which can occur in data collection whether generative writing, self-questioning, or reflection on artifacts are used as data sources or other sources of data are accessed. Hughes, Pennington, and Makris (2012) noted the widening respect for autoethnography and itemized autoethnographic “defensible data sources, which may include observations, interviews, documents and other artifacts, audio or video recordings, standardized instruments like surveys and tests, structured interview protocols, and categorical demographic information that permits aggregation and disaggregation of data across cases or units of analysis” and remains rigorous in the expectations for “material evidence” as required for other research designs (p. 213). Many authors purport autoethnography as a form of narrative inquiry due to the centrality of the narrative or life story generation for data collection (Toyosaki, 2018), or narrative data for autoethnography may be gathered from past narratives written by the researcher-participant as generated for another purpose or function (Stahlke Wall, 2016) or even as graphic illustrations by creative researchers (Parsons, Tregunno, Joneja, Dalgarno, & Flynn, 2018). Or authors such as Reed-Danahay (2017) who referenced autoethnography as a writing genre.

Winkler (2018) noted that while some autoethnographers prefer soft impressions versus hard data, a strong justification should be made when memory is considered as data, which is a good point of consideration for new doctoral researchers already mystified by these types of nebulous data sources and how to articulate and justify them to an institutional review board or within a doctoral research proposal. Records of memory such as diaries can be argued as valid and reliable data sources for autoethnography when reflexivity and contextual aspects are considered and the diary as record may also ensure reliability of the data (Winkler, 2018). Or as Hills, Lees, Freshwater, and Cahill (2018) argued, field notes and purposeful dairying are used to record memory in ethnography for later analysis and thus, pertinent for autoethnography as data collection methods; although, the researcher-participant’s lived experience does remain the primary data. Further, the authors noted the “embodied awareness” can be enabled through this systematic recording of the experience (Hills et al., 2018, p. 137). While others continue to challenge the notion that researcher-participant lived experiences are technically worthy as data or whether this method differs from ethnographic participant observation (Moors, 2017).

Le Roux (2017) described the inborne nature of data collection in autoethnography for the researcher-participant who can embody the data and allow for an organic production of knowledge as a result, which fortuitously may have a therapeutic effect for both the researcher and the reader. For doctoral researchers new to autoethnography, this may also have a symbiosis whereby learning more about autoethnography occurs simultaneous to insights gained for the study phenomenon (Stahlke Wall, 2018). Livesey and Runsen (2018) noted how these experiences can serve as a refreshingly new and innovative data source for researchers in disciplines that have heavily relied on quantitative survey research or other tired and taxed systematic methods for data collection that also face challenges. The authors noted autoethnography may explore emotional intelligence or other human factors within the discipline may see the lived experience as an untapped data source (Livesey & Runsen, 2018).

Key Terms in this Chapter

Intuitive Research: Intuitive research or intuitive inquiry typically refers to a hermeneutic interpretive mode of inquiry that balances objective and subjective perspectives to ensure consideration of multiple ways of knowing. Some authors also reference the necessity for compassionate writing of results in intuitive inquiry or what others reference as mindfulness.

Confirmability: Confirmability of qualitative data may be assured as data are checked and rechecked throughout the data collection and analysis process to assess repeatability and as one measure for overall data trustworthiness. Traditional qualitative methods of confirmability may not apply to self-as-subject research as these intrinsic and highly personal results may not be corroborated, and critics have noted this is a challenge for methods such as autoethnography and heuristic inquiry as lacking the rigor of conventional research. However, evaluation of confirmability within self-as-subject research can be adapted by alternative means for authenticity and transparency or post-data analysis reflections on bias or distortion.

Collaborative Autoethnography: Collaborative autoethnography is characterized by two or more authors focused on a phenomenon of inquiry from the respective of self through a concurrent or sequential systemic research approach that typically combines the perspectives, findings, and conclusions.

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