Multi-group and hierarchical confirmatory factor analysis of the Wechsler Intelligence Scale for Children—Fifth Edition: What does it measure?

Reynolds, Matthew R.; Keith, Timothy Z.

doi:10.1016/j.intell.2017.02.005

Cited by 76 publications

(95 citation statements)

References 61 publications

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“…However, one study (H. Chen, Zhang, Raiford, Zhu, & Weiss, ) reported factorial invariance of the final publisher preferred WISC‐V higher‐order model with five‐group factors across gender, although it did not examine invariance for rival higher‐order or bifactor models. Likewise, Reynolds and Keith () reported WISC‐V invariance across age groups, but the model they examined for invariance was an oblique five‐factor model, which ignores general intelligence altogether.…”

Section: Introductionmentioning

confidence: 99%

Construct validity of the Wechsler Intelligence Scale For Children – FifthUKEdition: Exploratory and confirmatory factor analyses of the 16 primary and secondary subtests

Canivez

Watkins

McGill

2018

Brit J of Edu Psychol

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Section: Introductionmentioning

confidence: 99%

Construct validity of the Wechsler Intelligence Scale For Children – FifthUKEdition: Exploratory and confirmatory factor analyses of the 16 primary and secondary subtests

Canivez

Watkins

McGill

2018

Brit J of Edu Psychol

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“…Psychometrically unidimensional tasks with high factor loadings on fluid intelligence also share similar patterns of associations with superior frontal, inferior and posterior parietal and temporal-occipital regions (Ebisch et al, 2012). Overall, it is not surprising that these pattern of findings are consistent with the parieto-frontal integration theory (P-FIT) of general intelligence neural correlates, given that fluid intelligence ability is related to a higher-order general intelligence factor (Carroll, 1993;Colom et al, 2009;Jung & Haier, 2007;Reynolds & Keith, 2017). The recent voxel-based meta-analysis of Basten, Hilger, and Fiebach (2015) on brain structural and functional correlates of intelligence lends further support to the P-FIT hypothesis.…”

Section: Introductionmentioning

confidence: 80%

“…For our study, we relied on full and abbreviated forms of the Wechsler intelligence scales with established construct validity and reliability in both typically developing and ASD populations (Minshew, Turner, & Goldstein, 2005;Scott, Austin, & Reid, 2007;Weiss, Keith, Zhu, & Chen, 2013). Others have suggested that the abbreviated form may overestimate nonverbal intelligence ability, and the PRI has also been recently separated into two independent factors representing fluid intelligence and visual processing in the latest iteration of the WISC (Axelrod, 2002;Reynolds & Keith, 2017). Varying definitions and measurement of cognitive constructs might account for inter-site differences in findings, such as the prominent occipital mediation in ASD that we observed in replication analyses.…”

Section: Discussionmentioning

confidence: 99%

“…Fluid intelligence, or fluid reasoning, refers to the broad cognitive ability to solve novel problems and is typically estimated from composite scores of non-verbal or abstract tests (Barbey, Colom, Paul, & Grafman, 2014;Reynolds & Keith, 2017;Schneider & McGrew, 2012). As a cognitive construct, fluid intelligence is distinct from but strongly correlated with general intelligence (Blair, 2006).…”

Section: Introductionmentioning

confidence: 99%

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Different brain networks underlying intelligence in Autism Spectrum Disorders

Pua

Malpas

Bowden

et al. 2017

Preprint

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There has been sustained clinical and cognitive neuroscience research interest in the neural basis of intelligence. The characterisation of brain structure and function underlying cognitive performance is necessary to understand the neurodevelopment of intelligence across the lifespan, and how associated neural correlates could be perturbed in atypical populations. As most work in this area has focused on neurotypical adults, the nature of functional brain connectivity underlying intelligence in paediatric cohorts with or without abnormal neurodevelopment requires further investigation. We use network-based statistics (NBS) to examine the association between resting-state functional Magnetic Resonance Imaging (fMRI) connectivity and fluid intelligence ability in male children with Autism Spectrum Disorders (ASD; M=10.45, SD=1.58 years, n=26) and in matched controls (M=10.38, SD=0.96 years). Compared to typically developing controls strictly matched on age, sex and fluid intelligence scores, boys with ASD displayed a subnetwork (network size=24, p=.0373, FWE-corrected) of significantly increased associations between functional connectivity and fluid intelligence performance. Between-group differences remained significant at a higher edge threshold of t=4 (size=6, p=.0425, FWE-corrected). Results were validated in independent-site replication analyses representing a similar male cohort with ASD (network size=14, p=.0396, FWE-corrected). Regions implicated in atypical ASD fluid intelligence connectivity were the angular gyrus, posterior middle temporal gyrus, occipital and temporo-occipital regions. Across all sites, within-group analyses failed to identify functional connectivity subnetworks associated with fluid or general intelligence performance in matched typically developing males. Findings suggest a prematurely accelerated but aberrant development of fluid intelligence neural correlates in young ASD males, possibly as a compensation mechanism that supports equivalent task performance to controls. The absence of whole-brain network correlates of general and fluid intelligence in young neurotypical males may represent the shift from local to global integration in the development of cognitive ability.

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“…Fluid intelligence refers to the ability to solve novel problems, and is typically estimated from composite scores of nonverbal or abstract tests. Under the Catell‐Horn‐Carroll (CHC) model, fluid intelligence (Gf) is a broad ability subsumed under an overall general intelligence factor (Reynolds & Keith, ; Schneider & McGrew, ). The autism spectrum disorders (ASD) are a group of heterogeneous neurodevelopmental conditions associated with deficits in social communication, social interaction, and restricted and repetitive behaviours.…”

Section: Introductionmentioning

confidence: 99%

Different brain networks underlying intelligence in autism spectrum disorders

Pua

Malpas

Bowden

et al. 2018

Human Brain Mapping

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There has been sustained clinical and cognitive neuroscience research interest in how network correlates of brain-behavior relationships might be altered in Autism Spectrum Disorders (ASD) and other neurodevelopmental disorders. As previous work has mostly focused on adults, the nature of whole-brain connectivity networks underlying intelligence in pediatric cohorts with abnormal neurodevelopment requires further investigation. We used network-based statistics (NBS) to examine the association between resting-state functional Magnetic Resonance Imaging (fMRI) connectivity and fluid intelligence ability in male children (n = 50) with Autism Spectrum Disorders (ASD; M = 10.45, SD = 1.58 years and in controls (M = 10.38, SD = 0.96 years) matched on fluid intelligence performance, age and sex. Repeat analyses were performed in independent sites for validation and replication. Despite being equivalent on fluid intelligence ability to strictly matched neurotypical controls, boys with ASD displayed a subnetwork of significantly increased associations between functional connectivity and fluid intelligence. Between-group differences remained significant at higher edge thresholding, and results were validated in independent-site replication analyses in an equivalent age and sex-matched cohort with ASD. Regions consistently implicated in atypical connectivity correlates of fluid intelligence in ASD were the angular gyrus, posterior middle temporal gyrus, occipital and temporo-occipital regions. Development of fluid intelligence neural correlates in young ASD males is aberrant, with an increased strength in intrinsic connectivity association during childhood. Alterations in whole-brain network correlates of fluid intelligence in ASD may be a compensatory mechanism that allows equal task performance to neurotypical peers.

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Multi-group and hierarchical confirmatory factor analysis of the Wechsler Intelligence Scale for Children—Fifth Edition: What does it measure?

Cited by 76 publications

References 61 publications

Construct validity of the Wechsler Intelligence Scale For Children – FifthUKEdition: Exploratory and confirmatory factor analyses of the 16 primary and secondary subtests

Construct validity of the Wechsler Intelligence Scale For Children – FifthUKEdition: Exploratory and confirmatory factor analyses of the 16 primary and secondary subtests

Different brain networks underlying intelligence in Autism Spectrum Disorders

Different brain networks underlying intelligence in autism spectrum disorders

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