WISC-IV Cognitive Assessment Scores for 1,130 U.S. Children

A U.S. sample of 1,130 children aged 6 to 16 years who were assessed for learning difficulties provides data for this Bayesian structural equation modeling study. Author Philippe Golay from the University of Geneva analyzed the Wechsler Intelligence Scale for Children (WISC-IV) core subtest scores to compare factor models. The analysis found support for a bi-factor model over a higher-order model, suggesting a simple interpretation of the subtest scores.

Use Cases

• Compare higher-order and bi-factor structural models based on cognitive assessment data.
• Apply Bayesian structural equation modeling to estimate small cross-loadings based on the described methodology.
• Study the factor structure of the WISC-IV intelligence test within a referred U.S. pediatric sample.

Strengths

• Dataset includes scores from 1,130 U.S. children, providing a substantial sample size for analysis.
• Focuses on a specific, clinically relevant population of children assessed for learning difficulties.
• Analysis employs Bayesian SEM, a method described as overcoming limitations of traditional confirmatory factor analysis.

Limitations

• Column-level documentation is absent; field semantics must be inferred from the research paper.
• Row count for the underlying raw data is unknown, which may limit suitability assessment.
• Data may reflect geographic and clinical bias inherent to the referred sample from a single study.

Provenance

Source

University of Geneva

Collection Method

WISC-IV data were obtained from children assessed for learning difficulties and subjected to Bayesian SEM analysis.

Geography

United States