Adapting Descriptive Psychological Phenomenology to Include Dyadic Interviews: Practical Considerations for Data Analysis
Bibliographic record
Abstract
Dyadic interviews are an approach to qualitative data collection designed to understand the meaning pairs of individuals make from experiences. The greatest benefit of dyadic interviews, and perhaps a reason for their gaining momentum in the literature, is that they encourage participants to interact, resulting in detailed and complex descriptions of phenomena. However, dyadic interviews pose challenges to qualitative researchers. Researchers must figure out how to account for the presence of two interviewees, any differences in perspective, and interactions. Unfortunately, no known study demonstrates how the interactions of dyadic interviews can be analyzed in accordance with a methodological approach. Rather, researchers tend to observe pre-existing methods without direct mention of modification for conducting and analyzing dyadic interviews. Thus, the degree to which participant interactions are being analyzed in current studies remains unknown. In the following paper, we use Giorgi’s (2009) descriptive psychological phenomenology as an exemplar for how dyadic interviews may be applied to qualitative investigations. The theoretical fit of dyadic interviews with Giorgi’s approach, proposed modifications, and their limitations, are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.401 | 0.460 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.012 | 0.027 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".