A Comparison of Covariates, Equating Designs, and Methods in Equating TIMSS 2019 Science Tests
Bibliographic record
Abstract
This research aimed to compare the equated scores by the methods based on classical test theory (CTT) and kernel equating, using covariates design (NEC) and anchor test design (NEAT). TIMSS 2019 science test scores equated by both Tucker, Levine true score, Levine observed score, equipercentile equating (pre-smoothing and post-smoothing) methods in CTT, and linear and equipercentile methods in kernel equating. Additionally, the covariates in NEC design were “home resources for learning,” “student confidence in science and mathematics,” “like learning science,” “instructional clarity in science lessons,” “math achievement,” “sex,” and “speaking the language of the test at home”. The equating results in NEC were compared with those in NEAT and EG. The participants comprised 1699 4th-grade students who attended the e-TIMSS 2019 in Canada, Singapore, and Chile. Results were analyzed according to equating errors and differences between equated scores. The research concluded that math achievement and home resources for learning could be used as covariates in NEC to equate the science test in case equating could not be done in the NEAT. However, when the other variables were used as covariates in NEC, the equated scores were very similar to the EG. Also, Tucker (CTT) and post-stratification (kernel) yielded similar equated scores in linear equating, and these methods were similarly different from kernel linear equating in EG. In equipercentile equating, the equated scores obtained from the post-smoothing (CTT) and EG were close to each other but slightly differed from post-stratification.
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 | 0.118 | 0.287 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".