Implications of Test Dimensionality for Unidimensional Irt Scoring: An Investigation of a High-Stakes Testing Program
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
Determining whether a test violates the assumption of unidimensionality is an important precursor to item response theory (IRT) analysis. However, a test’s unidimensionality or nonunidimensionality may be a matter of degree, and the implications of the degree of nonunidimensionality may depend on how the test is analyzed and how the results are to be used. This study examined the dimensionality of a high-stakes graduate training selection test and the implications of the test’s dimensionality for the IRT calibration and scoring of each section of the test. The dimensionality analyses suggested that, although the items within each of the sections were not completely homogeneous, neither were they clearly measuring distinct constructs corresponding to the content disciplines. The correlations between student scores based on item parameters that were estimated separately within discipline and then formed into weighted composites and scores based on item parameters that were estimated across discipline (within section) exceeded .99.
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it