Systematically Mapping Connection between Rapid Automatized Naming Task and Reading Performance: A Meta-analysis of Correlational Data
Bibliographic record
Abstract
Introduction. This meta-analytical study of primary research on early literacy explores and summarizes patterns of correlation between performance on Rapid Automatized Naming (RAN) task and measures of specific reading skills. This is the first large-scale meta-analysis intended to verify claims of the double-deficit hypothesis of relative independence of naming speed and phonological awareness factors in developmental dyslexia and to systematically map specific connection between RAN performance and various literacy competencies. Method. Two-hundred-forty-one primary studies identified through systematic searches of related empirical literature yielded 1551 effect sizes of two types – cross-sectional (correlations at the same time) and longitudinal (when measures of RAN and reading were considerably separated in time), reflecting RAN-to-reading correlations for seven independent outcome types. Results. The overall weighted average effect sizes were: r+ = 314, k = 1254 and r+ = 343, k = 297, respectively. Subsequent moderator variable analyses further explored RAN-to-reading associations dependent on RAN type, particular reading skills, age of learners and other factors. Among the strongest and most consistent in both sub-collections were correlation between symbolic RAN and reading speed and between non-symbolic RAN and reading comprehension, whereas both RAN types were strongly associated with decoding skills and reading composite measures. Discussion. Patterns of RAN-to-reading correlation provided insufficient support for the double-deficit hypothesis, but were suggestive of perceiving RAN as a measure of “pre-reading” skills, an “equal among equals” correlate of reading performance. The study also emphasizes the important role of both automatic and controlled cognitive processes for successful RAN task performance in its connection to reading competency.
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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 | 0.043 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.029 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".