Multifactorial structure of cognitive assessment tests in the UK Biobank: A combined exploratory factor and structural equation modeling analyses

Ciobanu, Liliana G; Stankov, Lazar; Ahmed, Muktar Beshir; Heathcote, Andrew; Clark, Scott R.; Aidman, Eugene · 2023 · Frontiers in Psychology

DOI: 10.3389/fpsyg.2023.1054707

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Summary

This study addresses the limitations of current methods for analyzing cognitive assessment data in the UK Biobank, a large-scale biomedical resource. Researchers often rely on either single specific tests or a aggregated general intelligence score ($g$), approaches the authors argue are suboptimal because they are either too narrow or too broad to capture the complexity of cognitive functioning. To resolve this, the study aims to identify a more granular, multifactorial structure of cognitive abilities that can better serve as outcome measures for investigating health predictors and biological underpinnings. The researchers utilized data from 3,425 UK Biobank participants aged 37–73 years, excluding those with psychiatric or neurological disorders. They selected nine cognitive tests based on the Cattell-Horn-Carroll (CHC) theory of intelligence, hypothesizing they would map onto fluid intelligence ($Gf$), short-term working memory ($Gwm$), and processing speed ($Gs$). The analysis employed a combined approach of Exploratory Factor Analysis (EFA) and Exploratory Structural Equation Modeling (ESEM). Using maximum likelihood extraction and Promax rotation, the authors evaluated factor solutions ranging from one to three factors, guided by scree plots, parallel analysis, and chi-square statistics. The results indicated that a three-factor model provided the best fit for the data, significantly outperforming one- and two-factor solutions. Contrary to the initial hypothesis of three distinct CHC domains, the analysis revealed that two of the three factors loaded onto fluid reasoning ($Gf$), with a clear distinction between visuospatial reasoning and verbal-analytical reasoning. The third factor was identified as processing speed ($Gs$). Notably, the tests initially hypothesized to measure short-term working memory did not form a separate factor but instead loaded onto the fluid reasoning factors. The average correlation among tests was low (around 0.20), supporting the need for a multifactorial rather than a single general factor model. The significance of this work lies in providing a standardized, multifactorial framework for interpreting UK Biobank cognitive data. By demonstrating that the data is best represented by distinct fluid reasoning and processing speed factors rather than a single $g$ score or isolated test results, the study offers a more precise tool for researchers. This model allows for the examination of specific cognitive facets in relation to health outcomes, potentially improving the replicability of findings and the understanding of the biological mechanisms underlying cognitive decline and variation.

Key finding

A three-factor model comprising visuospatial reasoning, verbal-analytical reasoning, and processing speed best characterizes the structure of UK Biobank cognitive assessment data.

Methodology

dataset

Sample size: 3425

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• individual differences