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Record W2768964053 · doi:10.1177/0081246317741822

Confirmatory factor analysis of the Kaufman assessment battery in a sample of primary school-aged children in rural South Africa

2017· article· en· W2768964053 on OpenAlexfundno aff
Joanie Mitchell, Mark Tomlinson, Ruth Bland, Brian Houle, Alan Stein, Tamsen Rochat

Bibliographic record

VenueSouth African Journal of Psychology · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsnot available
FundersGrand Challenges CanadaWellcome
KeywordsPsychologyCronbach's alphaConfirmatory factor analysisContext (archaeology)Test (biology)PsychometricsReliability (semiconductor)Developmental psychologyStructural equation modelingStatistics

Abstract

fetched live from OpenAlex

The Kaufman Assessment Battery for Children, Second Edition, measures cognitive processing, includes non-verbal sub-tests, and is increasingly used in low- and middle-income countries. While the Kaufman Assessment Battery for Children, Second Edition, has been validated in the United States, a psychometric evaluation has not been conducted in Southern Africa. This study aims to establish the reliability and validity of the Kaufman Assessment Battery for Children, Second Edition, among a sample of 376 primary school-aged children in rural South Africa (7–11 years). We examined Cronbach’s alpha and conducted a confirmatory factor analysis. The battery showed good reliability (mental processing index [α = .78]), and the originally validated structure of the Kaufman Assessment Battery for Children, Second Edition, was maintained (χ 2 = 16.30, p = .432). Mean scores were low on the Planning sub-scale. On the Simultaneous sub-scale, the mean score was higher for the supplementary sub-test Block Counting versus the core sub-test Triangles. With translation and the inclusion of supplementary sub-tests, the Kaufman Assessment Battery for Children, Second Edition, is an appropriate assessment to use in this context (150/150).

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)0.001
metaresearch head score (Gemma)0.001
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 score0.076
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.033
GPT teacher head0.343
Teacher spread0.309 · 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

Citations31
Published2017
Admission routes1
Has abstractyes

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