“Why was I not taught to use this software earlier?” A gendered exploration of university students’ beliefs towards their future use of CAQDAS
Bibliographic record
Abstract
The use of computer-assisted qualitative data analysis software (CAQDAS) for the organization and analysis of qualitative data has been a hotly debated topic among qualitative researchers since the inception of the technology in the 1980s (Smith & Hesse-Biber, 1996; and Bong, 2002). Proponents of the software claim that QDAS can help strengthen the validity, reliability, and accuracy of data analysis, whereas critics have cautioned that the use of such software distances the researchers from the data and attempts to make the data objective (Saldaña, 2013). The debates surrounding the use of qualitative software have contributed to a lack of student training in academic settings. Consequently, few studies have examined the student experience and decision-making process regarding the use of QDAS in the university setting (Paulus, Woods, Atkins, & Mackin, 2015). Using reflective response data collected from participants of a beginner qualitative coding workshop, this paper adds to the limited literature on student experiences using QDAS by examining gender differences in how participants critically reflected on their first experience using QDAS and examines the likelihood that participants would use QDAS in their future work. Findings from this study indicate that participants who used QDAS for the first time perceived that there are more potential benefits to using QDAS versus manual coding. Gender differences were present with female participants strongly believing that the software would allow them to be more effective and efficient researchers, and male participants believing that they would be better able to gain deeper insights into their data.
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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 | 0.041 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".