Psychometric Stability of Nationally Normed and Experimental Decoding and Related Measures in Children with Reading Disability
Bibliographic record
Abstract
Achievement and cognitive tests are used extensively in the diagnosis and educational placement of children with reading disabilities (RD). Moreover, research on scholastic interventions often requires repeat testing and information on practice effects. Little is known, however, about the test-retest and other psychometric properties of many commonly used measures within the beginning reader population, nor are these nationally normed or experimental measures comparatively evaluated. This study examined the test-retest reliability, practice effects, and relations among a number of nationally normed measures of word identification and spelling and experimental measures of achievement and reading-related cognitive processing tests in young children with significant RD. Reliability was adequate for most tests, although lower than might be ideal on a few measures when there was a lengthy test-retest interval or with the reduced behavioral variability that can be seen in groups of beginning readers. Practice effects were minimal. There were strong relations between nationally normed measures of decoding and spelling and their experimental counterparts and with most measures of reading-related cognitive processes. The implications for the use of such tests in treatment studies that focus on beginning readers are discussed.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".