Perfectionism, Anxiety Sensitivity, and Negative Reactions Following a Failed Statistics Test: A Vulnerability-Stress Model
Bibliographic record
Abstract
Self-critical perfectionism and anxiety sensitivity are potential vulnerability factors for increased distress following performance failure. We hypothesized that participants who fail a statistics quiz will have lower state self-esteem, lower positive affect, and greater negative affect at post-test than those who get a good grade, after controlling for pre-test scores and the effect of experimental condition would become larger as self-critical perfectionism and anxiety sensitivity increase. Exploratory analyses examined rigid perfectionism and a newly introduced construct (statistics anxiety sensitivity) as moderators. We tested this vulnerability-stress model in 329 post-secondary students using a two-group, pre-post, between-subjects design. Students completed an easy or hard statistics test and were assessed on pre- and post-test state self-esteem (social & performance) and state affect (anxiety, dysphoria, hostility, & positive affect). Across outcomes, main effects of experimental condition predicted between 7-33% of the variance, with the largest effects for performance self-esteem. Personality by condition interactions predicted 0.1-2% of the variance; 16 of 24 interactions were statistically significant in the expected direction (i.e., the effect of experimental condition was larger for participants high in measured personality traits). Findings suggest personality traits are vulnerability factors for decreased self-esteem and increased negative affect following failure in a statistics assessment.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".