MétaCan
Menu
Back to cohort
Record W2921505365 · doi:10.1177/0013164419834607

Comparing Age- and Grade-Based Norms on the Woodcock–Johnson III Normative Update

2019· article· en· W2921505365 on OpenAlexaff
Allyson G. Harrison, Kaitlyn Butt, Irene T. Armstrong

Bibliographic record

VenueEducational and Psychological Measurement · 2019
Typearticle
Languageen
FieldMedicine
TopicMedical Education and Admissions
Canadian institutionsQueen's University
Fundersnot available
KeywordsWoodcockNormativePsychologyDevelopmental psychologyStatisticsMathematicsEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

There has been a marked increase in accommodation requests from students with disabilities at both the postsecondary education level and on high-stakes examinations. As such, accurate identification and quantification of normative impairment is essential for equitable provision of accommodations. Considerable diversity currently exists in methods used to diagnose learning disabilities, including whether an impairment is normative or relative. This study investigated the impact on impairment classification if grade-based norms were used to interpret identical raw scores compared with age-based norms. Fourteen raw scores distributed equally across the adult range of the Woodcock-Johnson III Normative Update subtests were scored using norms for either age (18-29 years) or grade (13-17). The results indicate that raw scores receive a significantly lower standardized score (and thus percentile ranking) when grade-based norms are used. Furthermore, the difference between age- and grade-based scores increases dramatically as raw scores decrease, and there is a significant interaction between age and grade in the standard scores obtained. This study provides evidence to suggest that using different norms may result in different decisions about diagnoses and appropriate accommodations.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score0.997

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)0.0040.000

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 head0.209
GPT teacher head0.371
Teacher spread0.161 · 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.

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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueEducational and Psychological MeasurementSame topicMedical Education and AdmissionsFrench-language works237,207