How to analyze change in perception from paired Q-sorts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it