Can Bayesian Confirmatory Factor Analyses Help Recover the Hierarchical Structure of Coping?
Bibliographic record
Abstract
A pervasive problem in the coping literature has been the tendency for traditional confirmatory factor analyses (CFA) to reject hierarchical models of coping. In this study, we examined the first-order and hierarchical factor structure of the Coping Inventory for Academic Strivings with two independent samples of students. Results of traditional CFA again revealed the inferiority of the hierarchical model. Using Bayesian estimation, we present a more flexible statistical approach from which the cumulation of small cross-loadings appeared to be responsible for poor fit, yet inconsequential for the first-order structure of coping strategies and their organization in distinct higher-order dimensions. Results of multiple regression analyses further indicated that the two coping dimensions have their own nomological network, thus providing support for their criterion validity. Overall, our findings suggest that many more coping questionnaires may be salvaged and reintroduced in the coping literature after unleashing the inherent complexities of the coping construct.
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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 | 0.153 | 0.407 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".