Multi-Informant Test Anxiety Assessment of Adolescents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A total of 263 junior and senior high school students (grades 7, 8, 9, 10, 11, 12; ages 12 to 19) with relatively more informants identifying as females (57.4%) than males (42.6%) and more junior high school students (68.3%) than high school students (31.7%), along with 267 parents and 167 teachers responded to a student, parent, and teacher version of the German Test Anxiety Inventory (TAI-G) (Hoddapp & Benson, 1997). All reliabilities for all TAI-G scales for all three samples were above .70. The resulting data were fitted to two, three, and four factor models of test anxiety based on theoretical and empirical evidence. The four factor model (worry, emotion, distraction, lack of confidence) of the reduced (17 item) version of the TAI-G (Hoddapp & Benson, 1997) yielded the best fitting model for students (comparative fit index = .97; residual mean square = .042), parents (comparative fit index = .95; residual mean square = .073), and teachers (comparative fit index = .96; residual mean square = .080), thus providing very strong support for the proposed model. Sex, age, grade, and informant differences are presented and discussed. In conclusion, this study supports further research and use of a multi-informant assessment system of test anxiety.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it