Qualitative Data Analysis Software and Family Science: 2011–2020 Usage Trends
Bibliographic record
Abstract
ABSTRACT Objective This brief report examines how often a technology called QDAS (qualitative data analysis software) was mentioned in family science (FS) research between 2011 and 2020 and how it was described. It also provides data about qualitative FS research trends and examines whether there is a relationship between QDAS use and grounded theory. Background QDAS is a technology used by qualitative FS scholars, but little is known about its prevalence or how it is used. Research in this area is needed because technology can influence qualitative research practices in unknown or unacknowledged ways such as methodological homogenization. Method Empirical articles from five FS journals were examined for whether they presented qualitative findings during 2011–2015 and 2016–2020. Identified articles were then examined for their QDAS use. A chi‐square analysis compared articles mentioning QDAS with those not mentioning it for whether they were more likely to mention grounded theory. Results The percentage of qualitative research findings increased from 15% to 17% across the two time periods; in those articles, QDAS use increased from 25% to 41%. Few details were provided about how the programs were used, and a moderate relationship was found between QDAS and grounded theory. Conclusion QDAS use is increasing in FS, and more detail needs to be provided about how it is used. This information is increasingly important due to the incorporation of automatic features into QDAS programs such as Generative Artificial Intelligence tools.
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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 | 0.084 | 0.185 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".