Building a Self-Confidence Scale According to the Item Response Theory for High School Students in Jordan
Bibliographic record
Abstract
This study aimed to build self-confidence for high school students in Al-Mafraq Governorate in Jordan following the Item Response Theory (IRT). The scale included its initial version (50) items. To ensure the external validity of the scale, it was reviewed by several experts. According to the experts’ feedback, some items should be deleted or modified. The final version of the scale included (44) items. The scale was further applied to an experimental sample of (310) male and female students to verify psychometricians’ characteristics. Finally, the scale was administered to a sample of (1060) male and female high school students in Al-Mafraq Governorate. Data were collected, coded, and analyzed using statistical programs (SPSS and WINSTEPS). The most important results were the following: the self-confidence measure was one-dimensional, which means it measures only a single dimension. The results further revealed identical to the partial estimation model, and the index of average matching of individuals and the external and internal items approached zero, and the standard deviation approached the correct one. The estimated values of the distinct thresholds for the scale items showed a clear discriminatory ability and the emergence of particular threshold scores on the scale. After deleting the paragraphs that did not fit the study's model, the scale's final version included 39 items. The results also showed that the transfer values of logistical capacity units were within (-2.88 -2.77), within the IRT's accepted range.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".