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Record W2598578485 · doi:10.5539/jedp.v7n1p265

Re(Examining) Relations between CHC Broad and Narrow Cognitive Abilities and Reading Achievement

2017· article· en· W2598578485 on OpenAlexvenueno aff
Ryan J. McGill

Bibliographic record

VenueJournal of Educational and Developmental Psychology · 2017
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsnot available
FundersWoodcock-Muñoz Foundation
KeywordsPsychologyWoodcockReading (process)CognitionDevelopmental psychologyProxy (statistics)Multilevel modelStatisticsMathematicsLinguistics

Abstract

fetched live from OpenAlex

Previously, Evans and colleagues (2001) utilized simultaneous multiple regression to examine relations between Cattell-Horn-Carroll (CHC; Schneider & McGrew, 2012) broad and narrow cognitive abilities and reading achievement across the school age span. Although their findings suggest that many broad/narrow abilities had clinically significant effects on reading achievement they failed to account for the potential moderating effects of the general factor. To account for these effects, the current study employed hierarchical multiple regression analysis to reexamine the relationships between CHC dimensions and reading achievement after controlling for the effects of the general factor with 4,722 participants ages 6-18 from the Woodcock Johnson III Psychoeducational Battery (WJ III; Woodcock, McGrew, & Mather, 2001a). Results from the present study indicate that the full scale GIA composite (as a proxy for g) consistently accounted for large effects across the school age span for all of the reading achievement variables that were assessed. Among the broad and narrow abilities, only Gc consistently accounted for meaningful proportions of reading scores beyond g. As a consequence, researchers are encouraged to give greater consideration to the dimensionality of broad and narrow CHC measures when examining cognitive-achievement relationships or they may risk over-interpreting the predictive effects associated with these indices. Potential implications for clinical application of CHC theory are also discussed.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.099
GPT teacher head0.409
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2017
Admission routes1
Has abstractyes

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