An Alternative Method of Interviewing: Critical Reflections on Videoconference Interviews for Qualitative Data Collection
Bibliographic record
Abstract
Qualitative research is an increasingly popular research approach for tackling the evolving complexity of social issues. With this rise in use, methods of qualitative data collection are becoming highly diverse, moving away from conventional approaches and welcoming more innovative and creative methods of data collection in a quest to produce critically and theoretically engaged new knowledge. Although traditional face-to-face interviews remain a compelling and popular means, modern innovative technology-based interviewing, such as videoconference interviews, can play a pivotal role in qualitative research. This article argues that this approach is pragmatic because video conferencing interviews are relatively affordable for research teams and, for many research participants, they are more accessible than face-to-face interviews. On the other hand, it provides a unique opportunity for researchers and participants by compressing the time-space divide, facilitating safety, reducing travel-related expenses, accessing transnational participants, maintaining social distance, and protecting personal space and privacy. Yet, this article also argues that videoconferencing can be dogged by practical challenges that might conflict with the holistic quality of qualitative research, such as dropped calls and loss of intimacy compared to traditional in-person interviews. This article presents the experiences of a young researcher, who reflects on how and why he conducted Skype interviews in his research. The article concludes that, despite the relative merits and demerits, videoconference interviews can be a useful supplement or replacement for traditional face-to-face interviews. However, more research is needed to gain a robust understanding of how this type of interview meets basic assumptions about the quality of interviews and affects the overall rigor of qualitative research.
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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 | 0.080 | 0.112 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".