Qualitative research with NVIVO : between opportunities to explore and risks to be in distress
Bibliographic record
Abstract
In this paper, we focus on the software NVivo. Its capacity to follow and to support the user in his analytic and reflection process is the main quality of this software. This communication will present the learning outcomes from two different doctoral projects conducted with NVivo. The first research has been conducted in Montreal with 88 extensive semi structured individual interviews. The focus of the second research is the commitment determinants of workers in the context of e-learning at work. The data have been collected through several qualitative methods. In this paper, we share our learning outcomes concerning the use of this software and explain how our research process has been enriched by the use of this tool. We also highlight the difficulties that we met and to share our doubts. We outline the problem of coding and the temptation to go further in depth. Indeed the possibility of micro coding could represent a danger for the researcher who can lose time and his initial object seduced by the microcoding. Moreover, we stress the fantastic possibilities of NVivo concerning the analysis and the right to test ideas. But there are side effects to this flexibility. Through this paper, we highlight how these CAQDAS are powerful and helpful tools. But in the same time, researchers could be seduced by the possibilities of the tool and forget the most important, the results of their 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 | 0.119 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".