Confirmatory factor analysis of a short form of the Coping Inventory for Stressful Situations
Bibliographic record
Abstract
The 48-item Coping Inventory for Stressful Situations (CISS) was designed to assess three dimensions (task-oriented, emotional, and avoidant) of self-reported responses to stressful circumstances, but results from factor analyses suggest four factors. The present research used confirmatory factor analysis to verify the four-factor structure for the 21-item CISS short form in samples of 1,628 undergraduate students and 390 community-dwelling adults. Factors corresponding to task-oriented and emotional scales were orthogonal and were well defined by their seven constituent items. The avoidant scale was split into two three-item parcels that describe specific avoidance behaviors (contact a friend and treat oneself) rather than broad response categories. In the undergraduate sample, depression and anxiety correlated negatively with the task-oriented scale and positively with the emotional scale. In the community sample, the emotional scale was positively correlated with neuroticism and negatively correlated with extroversion and agreeableness, whereas the task-oriented scale was negatively correlated with neuroticism and positively correlated with extroversion, openness, agreeableness, and conscientiousness. It was concluded that the task-oriented and emotional scales have potential as measures of two types of responses to routine stressors.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".