General and Specific Effects on Cattell–Horn–Carroll Broad Ability Composites: Analysis of the Woodcock–Johnson III Normative Update Cattell–Horn–Carroll Factor Clusters Across Development

Floyd, Randy G.; McGrew, Kevin S.; Barry, Amberly; Rafael, Fawziya; Rogers, Joshua

doi:10.1080/02796015.2009.12087836

Cited by 19 publications

(1 citation statement)

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“…In this paper this is referred to as the mixed-g model ( g has a direct impact on broad CHC abilities [but no direct effect on the individual CHC tests], as well as g having possible indirect effects on achievement). An extensive number of mixed- g studies completed by Keith, Reynolds, and colleagues (e.g., Caemmerer et al 2018 , 2020 ; Floyd et al 2003 , 2007 , 2008 , 2009 ; Hajovsky et al 2014 ; Keith 1999 ; Keith and Dunbar 1984 ; Keith and Reynolds 2010 , 2018 ; McGrew et al 1997 ; Meyer and Reynolds 2017 ; Niileksela et al 2016 ; Reynolds et al 2013 ; Reynolds and Keith 2017 ; Reynolds and Turek 2012 ; Taub et al 2008 ; Vanderwood et al 2002 ; hereafter called the Keith–Reynolds group) support a multi-factorial view of intelligence with a hierarchical psychometric g factor that does not focus on explaining as much common test variance as possible. While equivocal, Reynolds and Keith ( 2017 ) state that “the higher-order model is more in line with our current conception of human intelligence as it represents a system of interrelated latent constructs and not one in which g and [CHC] broad abilities operate entirely independently of one another” (p. 33).…”

Section: Literature Reviewmentioning

confidence: 99%

A Psychometric Network Analysis of CHC Intelligence Measures: Implications for Research, Theory, and Interpretation of Broad CHC Scores “Beyond g”

McGrew¹,

Schneider

Decker

et al. 2023

J. Intell.

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For over a century, the structure of intelligence has been dominated by factor analytic methods that presume tests are indicators of latent entities (e.g., general intelligence or g). Recently, psychometric network methods and theories (e.g., process overlap theory; dynamic mutualism) have provided alternatives to g-centric factor models. However, few studies have investigated contemporary cognitive measures using network methods. We apply a Gaussian graphical network model to the age 9–19 standardization sample of the Woodcock–Johnson Tests of Cognitive Ability—Fourth Edition. Results support the primary broad abilities from the Cattell–Horn–Carroll (CHC) theory and suggest that the working memory–attentional control complex may be central to understanding a CHC network model of intelligence. Supplementary multidimensional scaling analyses indicate the existence of possible higher-order dimensions (PPIK; triadic theory; System I-II cognitive processing) as well as separate learning and retrieval aspects of long-term memory. Overall, the network approach offers a viable alternative to factor models with a g-centric bias (i.e., bifactor models) that have led to erroneous conclusions regarding the utility of broad CHC scores in test interpretation beyond the full-scale IQ, g.

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Section: Literature Reviewmentioning

confidence: 99%

A Psychometric Network Analysis of CHC Intelligence Measures: Implications for Research, Theory, and Interpretation of Broad CHC Scores “Beyond g”

McGrew¹,

Schneider

Decker

et al. 2023

J. Intell.

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References

2015

Essentials of Gifted Assessment

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Joint Confirmatory Factor Analysis of the Woodcock‐johnson Tests of Cognitive Abilities, Third Edition, and the Stanford‐binet Intelligence Scales, Fifth Edition, With a Preschool Population

Chang

Paulson

Finch

et al. 2013

Psychology in the Schools

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This study examined the underlying constructs measured by the Woodcock‐Johnson Tests of Cognitive Abilities, Third Edition (WJ‐III COG) and the Stanford‐Binet Intelligence Scales, Fifth Edition (SB5), based on the Cattell‐Horn‐Carrol (CHC) theory of cognitive abilities. This study reports the results of the first joint confirmatory factor analysis of the WJ‐III COG and SB5 with an independently collected preschool‐aged sample. The WJ‐III COG and SB5 were administered to 200 preschool‐aged children of 4 to 5 with no known disorders or disabilities. Confirmatory factor analyses using maximum likelihood estimation were conducted to evaluate three models of increasing complexity and two alternative models to determine which model best describe the underlying constructs measured by the WJ‐III COG and the SB5. Though none of the models displayed a good fit to the data, results showed that the underlying construct of the two tests was best represented by a Three‐Stratum alternative CHC model in which the Gf factor and subtests were omitted. Exploratory factor analysis was conducted to provide further insights into the actual latent structure underlying the data. Implications of findings to guide school‐based practitioners in using cross‐battery assessment with preschool children were addressed.

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General and Specific Effects on Cattell–Horn–Carroll Broad Ability Composites: Analysis of the Woodcock–Johnson III Normative Update Cattell–Horn–Carroll Factor Clusters Across Development

Cited by 19 publications

References 0 publications

A Psychometric Network Analysis of CHC Intelligence Measures: Implications for Research, Theory, and Interpretation of Broad CHC Scores “Beyond g”

A Psychometric Network Analysis of CHC Intelligence Measures: Implications for Research, Theory, and Interpretation of Broad CHC Scores “Beyond g”

References

Joint Confirmatory Factor Analysis of the Woodcock‐johnson Tests of Cognitive Abilities, Third Edition, and the Stanford‐binet Intelligence Scales, Fifth Edition, With a Preschool Population

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