Does the easy–difficult item measure attitude or perceived behavioural control?
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
OBJECTIVE: In order to determine if easy-difficult item measures attitude or perceived behavioural control (PBC), we used structural equation modelling of 10 cross-sectional data sets. DESIGN: Cross-sectional design was used. METHOD: Ten studies that examined health-related behaviours and used the theory of planned behaviour as a theoretical framework were analysed. Samples totalling N=4,552 participants were employed. All studies involved multi-item measures of attitude (Aact) and PBC items derived from pilot testing. RESULTS: Confirmatory factor analysis confirmed the discriminant validity of Aact and PBC. Structural equation modelling of relevant path indicated that in three studies, easy-difficult item is an indicator of both Aact and PBC. In the other seven studies, easy-difficult item belongs to PBC. The indexes of meta-analysis suggest that overall, easy-difficult item is an indicator of PBC. CONCLUSION: Findings from 10 studies converged toward the conclusion that the easy-difficult item is an indicator of perceived PBC. However, since the easy-difficult item is sometimes classified as both Aact and PBC, and only the perceived difficulty dimension of PBC captures a significant increment in the variance of intention, it appears important to develop and validate a set of items devoted to measure the perceived difficulty dimension adequately.
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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 | 0.044 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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