Conceptual Categories or Operational Constructs? Evaluating Higher Order Theory of Planned Behavior Structures in the Exercise Domain
Bibliographic record
Abstract
The theory of planned behavior (TPB) is a popular framework for understanding the informational and motivational influences of exercise behavior One tenet of this model that has not been examined is the belief that direct measures of TPB component constructs are organized through higher order constructs. The authors'purpose of this article was to test this higher order conceptualization in comparison with a multidimensional TPB model using structural equation modeling. Participants (N=268) completed direct measures of the TPB and a 2-week follow-up of exercise behavior The results generally supported multidimensional TPB constructs over higher order structures. Direct measures of attitude (i.e., affective and instrumental) and subjective norm (i.e., injunctive and descriptive) had better psychometric properties when considered multidimensionally. Perceived behavioral control (i.e., self-efficacy, controllability), however, had estimation problems for both the multidimensional and the higher order model. Aggregation of TPB components is not warranted, and the perceived behavioral control components may possess a structure more complex than simple multidimensionality or a superordinate higher order construct.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".