Validity = Credibility + Trustworthiness
Eisener’s quote reminds me of the importance of metaphor within my research. There will be times when words will fail or be insufficient; when alliteration, cadence, allusions, metaphor and images may stand proxy to the true meanings in what needs to be communicated. These meaning making devices will become part of the research variance expected in such P-IP frameworks. I need to emphasize that notions of validity and reliability are inconsistent with a social-constructivist epistemology and the interpretivist research design applied to this proposed research, since it comes from a positivistic perspective. The nature of P-IP research is “creative, inventive, emotionally charged, and uneasy. “Good enough” researchers find ways to sustain all these aspects” (Luttrell, 2000, p. 8). Trustworthiness, rather than validity, will emerge as one criteria for quality research, “rooted in the epistemological/ethics nexus” of standards such as positionality, discourse communities, voice, critical subjectivity, reciprocity, sacredness, and privilege (Guba & Lincoln, 2005, p. 209). I will explicitly consider the impact of the big-tent criteria for qualitative research (Tracy, 2010) – worthy topic, rich data, rigor, sincerity, credibility, resonance, significant contribution, ethics, and meaningful coherence. In the research results, the claims, warrants and justifications will be explored and explicitly revealed (Carter & Little, 2007; Hart, 1998).
In order for this research to be perceived as having value and merit, I will frame my research in terms of trustworthiness, credibility, and transparency. From an interpretivist stance, research should include clarifying positionality, ontological authenticity, fairness, and voice; from a critical theory approach this is seen in researcher reflexivity (Guba & Lincoln, 2005). To increase research authenticity and trustworthiness (Guba & Lincoln, 2005), once the transcripts, reflective artifacts, and stories are graphically rendered, visualized, thematically coded, and analyzed, results will be returned to participants for review. This emulates the member checking processes of positivistic research techniques.
By applying a crystallizing methodology, credibility and trustworthiness develops over time, through the creation of many diffuse reflections and refractions within the data engagements, data analysis, and data representations (Guba & Lincoln, 2005).
Crystals are prisms that reflect externalities and refract within themselves, creating different colors, patterns, arrays, casting off in different directions, what we see depends upon our angle of repose. Not triangulation, crystallization … crystallization provides us with a deepened, complex, thoroughly partial understanding of the topic” (Guba & Lincoln, 2005, p. 208).
Further to this, Ellingson (2009) described crystallization as a research process that “turns back upon itself, highlighting its own construction by showing that no one genre offers truth. By making and problematizing claims, crystallized texts gain a level of reflexive validity” (Ellingson, 2009, p. 15). In this way, the research validity will be revealed through new understandings as the crystallization methods are applied to the research artifacts.
Trustworthiness and credibility of the research findings will become evident in the depth, complexity, and rigour evidenced in the constructions created (Stewart et al., 2017). Authenticity and dependability are revealed, not as an absolute truth, but in the reported reflexivity and interactions between researcher, researched, and research data re-visualization techniques (Stewart et al., 2017). Providing a trusted and reliable representation of the research data will come from consistently comparing, reporting, sharing thick, rich descriptions of the data, and providing a chain of evidence for all field notes, memos, member reviews, debriefs, engagements, observations, frameworks, typologies and recreations (Stewart et al., 2017). By preserving links and threads through the research process, readers will recognize the logical paths and recursive steps I have taken, in ways that are methodical, transparent, and adhere to best practices for data management (Stewart et al., 2017). As an example, by providing a word cloud visualization from a participant’s video interview transcript as an alternative presentation for the coded data collection, the readability of resulting analysis will improve. In this way, researched and reader can recognize how I, as the researcher, has dependably managed the alchemic and crystallizing data analysis strategies.