Quantifying subjective data using online Q-methodology software
Bibliographic record
Abstract
Abstract The Q-Sort methodology has been used to study participants’ subjective views on various topics ( Brown, 1996 ). The task has historically been completed by manually sorting cards into categories that force responses into a normal distribution ( Brown, 1996 ). Data collection using this method is time consuming and manual data entry is prone to human error. We describe here QMethod Software – a computerized web-based application that allows participants to sort and record their responses online. This online application eliminates the need for researchers to attend the study sessions and to manually enter data. QMethod Software described here is currently being used in both applied and cognitive psychology studies, including a clinical study that evaluates participants’ perception of behaviours seen as most characteristic or most uncharacteristic of psychological aggression or coercive control in situations of intimate partner violence. In a health psychology study, it is being used to examine people’s perceptions of food allergy, and in a psycholinguistics lab it was used to evaluate the affective valence, abstractness, and semantic richness ratings of words. We will show here that the data obtained from one of these psycholinguistic studies (abstractness/concreteness) correlates highly with existing measures ( Brysbaert, Warriner & Kuperman, 2014 ) thus demonstrating that the Q-sort methodology and this particular implementation, the QMethod Software app, reproduces more typical evaluations/assessments in the psycholinguistics literature.
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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 | 0.059 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".