How to analyze change in perception from paired Q-sorts
Bibliographic record
Abstract
Although there have been some previous attempts on analyzing changes in perceptions in Q-methodology, a systematic approach is lacking. In this article we introduce two new methods for analyzing change in perceptions in Q-methodology using paired Q-sorts. We also demonstrate these methods using an actual dataset.Method I: This approach is appropriate for assessing the changes in perceptions between two different conditions of instruction applied to the same subjects. The changes are assessed using a factor analysis on the differences between the Q-sorts from the two conditions of instruction.Method II: This method examines the changes in perception from a baseline Q-analysis. This is usually appropriate when data are collected at two time-points, e.g., before-after situations, where the first assessment is considered as the baseline. In this approach, a by-person factor analysis is conducted on the baseline Q-sorts (condition 1) and factors are identified. Then, the changes in perceptions are assessed for the subjects loaded on any factor from baseline using the Q-sorts from condition 2.In conclusion, these two methods are easy to apply, the results are more objective, and are less prone to investigator bias.
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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 | 0.044 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".