Examination of Latent Test Anxiety Profiles in a Sample of U.S. Adolescents
Bibliographic record
Abstract
The present study examined latent test anxiety profiles in a sample of 592 U.S. adolescents in grades 6-12 using latent profile analysis (LPA). The adolescents were administered a multidimensional measure of test anxiety in their schools. The results of LPA indicated that a three-profile test anxiety model provided the best fitting model. The three latent test anxiety subgroups were named low, medium, and high test anxiety. In addition, grade-level and gender were added as covariates to the model and LPA was performed again. Grade-level and gender were found to differentially predict membership in the latent test anxiety subgroups, with females more likely to be in the high latent test anxiety subgroup than in the medium and low latent test anxiety subgroups and middle school students were more likely to be in the high latent test anxiety subgroup than in the low latent test anxiety subgroup. Middle school students were also more likely to be in the medium latent test anxiety subgroup than in the low latent test anxiety subgroup. Implications for the development of measures, treatment, and prevention of test anxiety in the U.S. adolescent population 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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".