Advancing Qualitative Research Using Qualitative Data Analysis Software (QDAS)? Reviewing Potential Versus Practice in Published Studies using ATLAS.ti and NVivo, 1994–2013
Bibliographic record
Abstract
Qualitative data analysis software (QDAS) programs are well-established research tools, but little is known about how researchers use them. This article reports the results of a content analysis of 763 empirical articles, published in the Scopus database between 1994 and 2013, which explored how researchers use the ATLAS.ti™ and NVivo™ QDAS programs.* The analysis specifically investigated who is using these tools (in terms of subject discipline and author country of origin), and how they are being used to support research (in terms of type of data, type of study, and phase of the research process that QDAS were used to support). The study found that the number of articles reporting QDAS is increasing each year, and that the majority of studies using ATLAS.ti™ and NVivo™ were published in health sciences journals by authors from the United Kingdom, United States, Netherlands, Canada, and Australia. Researchers used QDAS to support a variety of research designs and most commonly used the programs to support analyses of data gathered through interviews, focus groups, documents, field notes, and open-ended survey questions. Although QDAS can support multiple phases of the research process, the study found the vast majority of researchers are using it for data management and analysis, with fewer using it for data collection/creation or to visually display their methods and findings. This article concludes with some discussion of the extent to which QDAS users appear to have leveraged the potential of these programs to support new approaches to 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 | 0.552 | 0.691 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.038 | 0.057 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.035 | 0.031 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".