Creating parsimony at the expense of precision? Conceptual and applied issues of aggregating belief-based constructs in physical activity research
Bibliographic record
Abstract
The aggregation of measured social cognitive beliefs to form scales is a common procedure in physical activity research. In this paper, we propose that specific beliefs may actually have unique associations with physical activity, which are obscured by the practice of aggregation. Further, we point out that beliefs may be related in a more complex manner than the theory behind scale aggregation. Both of these factors are interpreted in terms of limiting physical activity intervention efforts. Therefore, the purpose of this study was to examine alternatives to summative scales of physical activity beliefs using structural equation modeling. Demonstrations were performed using belief-based constructs of self-efficacy, pros and cons with a large Canadian random sample (N = 683) over three, 6-month time points. Results demonstrated that items of belief-based scales are multidimensional and that a correlated belief structure fit the observed data better (P < 0.05) and explained more variance in vigorous physical activity (an additional 6-7%) than aggregated scales. Finally, a causally ordered structure among beliefs was supported, suggesting that items within a scale may be linked causally rather than as indicators of a higher-order latent variable. Implications for future research and physical activity interventions are discussed.
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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 | 0.349 | 0.629 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".