Development and Validation of a Two-component Perceived Control Measure
Bibliographic record
Abstract
BACKGROUND: Research indicates that perceived behavioral control (PBC) is an important determinant of behavior and that it is composed of perceived capability and opportunity. However, typical measurement of these constructs may be confounded with motivation and outcome expectations. Vignettes presented before questionnaire items may clarify construct meaning leading to precise measurement. PURPOSE: The purpose of this study was to develop and validate measures of perceived capability and opportunity that parse these constructs from the influence of motivation and outcome expectations using vignettes. METHODS: Study 1 collected feedback from experts (N = 9) about the initial measure. Study 2a explored internal consistency reliability and construct and discriminant validity of the revised measure using two independent samples (N = 683 and N = 727). Finally, using a prospective design, Study 2b (N = 1,410) investigated test-retest reliability, construct and discriminant validity at Time 2, and nomological validity. RESULTS: After Study 1, the revised measure was tested in Studies 2a and 2b. Overall, the evidence suggests that the measure is optimal with four items for perceived capability and three for the perceived opportunity. The measure demonstrated strong internal consistency ( > 0.90) and test-retest reliability (intraclass correlation coefficients [ICCs] > .78). The measure also showed construct and discriminant validity by differentiating itself from behavioral intentions (i.e., motivation) and affective attitude (based on expected outcomes) (SRMR = 0.03; RMSEA = 0.06). It also demonstrated evidence of nomological validity as behavior 2 weeks later was predicted. CONCLUSIONS: We recommend researchers use this tool in future correlational and intervention studies to parse motivation and outcome expectations from perceived capability and opportunity measurement.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".