A Comparative Analysis of Data Quality in Online Zoom Versus Phone Interviews: An Example of Youth With and Without Disabilities
Bibliographic record
Abstract
Qualitative researchers are increasingly using online data collection methods, especially during the COVID-19 pandemic. I compared the data quality (i.e., interview duration, average number of themes and sub-themes, and inaudible words) of 34 interviews (29 conducted by Zoom (16 with camera on, 13 camera off) and 5 conducted by phone) drawn from a study focusing on youth’s coping experiences during the pandemic. Findings showed that phone interviews had a longer duration compared to Zoom. However, phone interviews had a similar average word count to Zoom interviews (with the camera on). Zoom interviews conducted with the camera off were shorter in duration than interviews with the camera on. The number of themes was similar across the different interview formats but there were fewer sub-themes for Zoom interviews with the camera off. The findings suggest that Zoom interviews conducted with the camera off could affect the data quality. This research also emphasizes the importance of giving participants choice in the format of their interview to allow for optimal sharing of experiences while enhancing the equity, diversity and inclusion of the participants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.114 | 0.268 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".