The association of self-efficacy, anxiety sensitivity, and perfectionism with statistics and math anxiety
Bibliographic record
Abstract
Statistics and math anxiety are pervasive problems for post-secondary students. We hypothesized that self-efficacy would be negatively related to math/statistics anxiety, and that anxiety sensitivity and perfectionism would be positively related to math/statistics anxiety, even when controlling for gender, university program, and education level. Method: Graduate and undergraduate students (N = 447, after exclusions) completed an online self-report questionnaire, including an abbreviated version of the Statistics Anxiety Rating Scale (STARS), math anxiety, self-efficacy, anxiety sensitivity, perfectionism, and demographics. Results: Exploratory factor analysis supported a six-factor structure for statistics anxiety. Self-efficacy was negatively associated with math/statistics anxiety, whereas anxiety sensitivity and perfectionism were positively associated with math/statistics anxiety. Relationships ranged from small-to-moderate, and most relationships persisted after adding covariates. Discussion: Our study suggests the feasibility of a short-form version of the STARS. Moreover, it provides important information on how personality is associated with domain-specific anxiety that can impede statistics education.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".