Does the easy–difficult item measure attitude or perceived behavioural control?
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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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 | 0.013 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".