Do Dimensions of Perfectionism Predict Dimensions of Test Anxiety While Controlling for Depression?
Bibliographic record
Abstract
The present study examined whether cultural differences in different dimensions of perfectionism exist and whether different dimensions of perfectionism (i.e., rigid and self-critical perfectionism) predicted different dimensions of test anxiety while controlling for depression in a sample of Canadian and Singapore higher education students. In addition, culture was examined to determine whether it served as a moderator variable in the relationship between different dimensions of perfectionism and different dimensions of test anxiety. The present study was grounded in DiBartolo and Rendón’s cross-cultural framework for conducting intra- and intercultural research in the area of perfectionism. The sample for the study included 1,095 undergraduate students. Perfectionism, test anxiety, and depression measures were administered to the students online. The results of mean and covariance analyses found the perfectionism measure was invariant across Canadian and Singapore students. In addition, the results of latent mean analyses found no significant differences on the different dimensions of perfectionism between Canadian and Singapore students. The results of analyses of variance also found no significant differences in different ethnic groups on the different dimensions of perfectionism in Canada and Singapore. Furthermore, the results of five hierarchical regression analyses found self-critical perfectionism explained unique variance in the five different test anxiety dimensions while controlling for depression, and culture did not serve as a moderator variable in the relationship between the different dimensions of perfectionism and test anxiety. Implications of the findings 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".