Relation between Manual Rotation and Abductive Reasoning in Q-Methodology
Bibliographic record
Abstract
Subjectivity is usually evaluated using qualitative research methods. However, Q-methodology offers a different set of techniques for measuring and evaluating subjective viewpoints. Q-methodology is a combination of qualitative and quantitative research techniques that is used to identify unique as well as common viewpoints. The quantitative component of Q-methodology is based on factor analysis and factor rotation. A common approach of analysis in Q-methodology is the use of a centroid factor extraction followed by a manual rotation. Some advocates of manual rotation technique claim that manual rotation is based on the abductive reasoning principle. This article shows that manual rotation and abductive reasoning are two different approaches serving different purposes. Abductive reasoning is a method of hypothesis generation while manual rotation is a method of hypothesis testing. Manual rotation does not conform to abductive reasoning principle if there is no pre-specified theory or hypothesis and consecutive manual rotation of factors toward a satisfactory solution is not the same as rotating factors based on adductive reasoning principle.
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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 | 0.167 | 0.359 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".