What does the Mental Rotation Test Measure? An Analysis of Item Difficulty and Item Characteristics
Bibliographic record
Abstract
The present study examined the contributions of various item characteristics to the difficulty of the individual items on the Mental Rotation Test (MRT). Analyses of item difficulties from a large data set of university students were conducted to assess the role of time limitation, distractor type, occlusion, configuration type, and the degree of angular disparity. Results replicated in large part previous findings that indicated that occluded items were significantly more difficult than non-occluded and that mirror items were more difficult than structural items. An item characteristic not previously examined in the literature, configuration type (homogeneous versus heterogeneous), also was found to be associated with item difficulty. Interestingly, no significant association was found between angular disparity and difficulty. Multiple regression analysis revealed that a model consisting of occlusion and configuration type alone was sufficient for explaining 53 percent of the variance in item difficulty. No interaction between these two factors was found. It is suggested, based on overall results, that basic figure perception, identification and comparison, but not necessarily mental rotation, account for much of the variance in item difficulty on the MRT.
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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 | 0.010 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".