An Evaluation of the Validity of the <i>Test of Visual Perceptual Skills - Revised</i> (TVPS-R) Using the Rasch Measurement Model
Bibliographic record
Abstract
The visual perceptual skills of children are often evaluated by health care and education practitioners. Even though the Test of Visual Perceptual Skills - Revised (TVPS-R) is one of the most frequently used instruments with school-age children, its construct validity has not been evaluated thoroughly. The purpose of the study was to evaluate the scalability/interval level measurement, unidimensionality, lack of differential item functioning (DIF), and hierarchical ordering of items of the TVPS-R and its seven subscales using the Rasch Measurement Model (RMM). The TVPS-R scores from a sample of 356 normally developing children (171 boys and 185 girls), ranging in age from 5 to 11 years, were used to complete the RMM analysis. When the seven individual TVPS-R scales were analysed, they all exhibited adequate measurement properties (scalability/interval level measurement, unidimensionality, lack of DIF, and hierarchical ordering). However, when they were collapsed together to form an overall composite scale of motor-free visual perceptual skills, the TVPS-R items failed to group together to measure a unidimensional construct. In addition, many scale items exhibited RMM misfit or DIF. The results suggest that the seven TVPS-R subscales can be used on an individual basis with clients to generate a profile of their motor-free visual perceptual skills, but that they cannot be summed together to calculate an overall summary motor-free visual perceptual score or perceptual quotient. The TVPS-R composite scale does not exhibit adequate construct validity.
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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 | 0.025 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".