Relative Cognitive Complexity of the Kaufman Assessment Battery for Children (K-ABC) and the WISC-R

Kline, Rex B.; Guilmette, Sylvie; Snyder, Joseph F.; Castellanos, María Clara

doi:10.1177/073428299201000207

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…sequential vs. simultaneous [21], [33], [38]; low, intermediate, high [23]; variance shared with a g factor of intelligence [22], [23]). This order was confirmed statistically with a principal component analysis (PCA) on the test scores of the healthy full term control children within the sample to reveal each task's loading on g (high workload tasks require integration of various cognitive processes thus intercorrelations among these tasks should be higher than among low workload tasks [23], please see Table 2 for details).…”

Section: Methodsmentioning

confidence: 99%

“…Working memory models suggest that cognitive resource utilization increases with task complexity to allow for adequate behavioral performance even in the most demanding situations [22], [23]. Specifically, it has been proposed that cortical areas are specialized for certain tasks but each cortical area has limited computational capacities restraining its activity [24].…”

Section: Introductionmentioning

confidence: 99%

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Effects of Gestational Age at Birth on Cognitive Performance: A Function of Cognitive Workload Demands

2013

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ObjectiveCognitive deficits have been inconsistently described for late or moderately preterm children but are consistently found in very preterm children. This study investigates the association between cognitive workload demands of tasks and cognitive performance in relation to gestational age at birth.MethodsData were collected as part of a prospective geographically defined whole-population study of neonatal at-risk children in Southern Bavaria. At 8;5 years, n = 1326 children (gestation range: 23–41 weeks) were assessed with the K-ABC and a Mathematics Test.ResultsCognitive scores of preterm children decreased as cognitive workload demands of tasks increased. The relationship between gestation and task workload was curvilinear and more pronounced the higher the cognitive workload: GA2 (quadratic term) on low cognitive workload: R 2 = .02, p<0.001; moderate cognitive workload: R 2 = .09, p<0.001; and high cognitive workload tasks: R 2 = .14, p<0.001. Specifically, disproportionally lower scores were found for very (<32 weeks gestation) and moderately (32–33 weeks gestation) preterm children the higher the cognitive workload of the tasks. Early biological factors such as gestation and neonatal complications explained more of the variance in high (12.5%) compared with moderate (8.1%) and low cognitive workload tasks (1.7%).ConclusionsThe cognitive workload model may help to explain variations of findings on the relationship of gestational age with cognitive performance in the literature. The findings have implications for routine cognitive follow-up, educational intervention, and basic research into neuro-plasticity and brain reorganization after preterm birth.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effects of Gestational Age at Birth on Cognitive Performance: A Function of Cognitive Workload Demands

2013

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“…Test variables would be spatially aligned in the two-dimensional space, according to their degree of similarity. Highly similar test variables would be closer together spatially, and highly dissimilar test variables would be farther apart spatially (for an example of an MDS analysis applied to test variables, see the visual scatter plot in Kline, Guilmette, Snyder, & Mastellanos, 1992). For a two-dimensional space, therefore, each test variable can be represented by two coordinate values: one coordinate value that locates its position on one dimension and a second coordinate that locates its position on the second dimension.…”

Section: Description Of the Pams Proceduresmentioning

confidence: 99%

Using Profile Analysis via Multidimensional Scaling (PAMS) to identify core profiles from the WMS-III.

Frisby

Kim

2008

Psychological Assessment

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Profile Analysis via Multidimensional Scaling (PAMS) is a procedure for extracting latent core profiles in a multitest data set. The PAMS procedure offers several advantages compared with other profile analysis procedures. Most notably, PAMS estimates individual profile weights that reflect the degree to which an individual's observed profile approximates the shape and scatter of latent core profiles. The PAMS procedure was applied to index scores of nonreplicated participants from the standardization sample (N = 1,033) for the Wechsler Memory Scale--Third Edition (D. Tulsky, J. Zhu, & M. F. Ledbetter, 2002). PAMS extracted discrepant visual memory and auditory memory versus working memory core profiles for the complete 16- to 89-year-old sample and discrepant working memory and auditory memory versus working memory core profiles for the 75- to 89-year-old cohort. Implications for use of PAMS in future research are discussed.

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“…While most factor analyses of multitest batteries typically identify latent factors among the column variables (items or subtests), a PAMS analysis identifies latent profiles among the row variables (profiles of individuals). In solutions from a simple MDS (as illustrated in the Kline, Guilmette, Snyder, & Mastellanos, 1992, example), a dimension (axes on a visual map) represents a continuous bipolar characteristic of individual subtests. In solutions from a PAMS analysis, a dimension represents a continuous bi-directional latent profile.…”

Section: Application Of the Pams Model To Profile Analysismentioning

confidence: 99%

Estimating Cognitive Profiles Using Profile Analysis via Multidimensional Scaling (PAMS)

Kim¹,

Frisby²,

Davison³

2004

Multivariate Behavioral Research

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Two of the most popular methods of profile analysis, cluster analysis and modal profile analysis, have limitations. First, neither technique is adequate when the sample size is large. Second, neither method will necessarily provide profile information in terms of both level and pattern. A new method of profile analysis, called Profile Analysis via Multidimensional Scaling (PAMS; Davison, 1996), is introduced to meet the challenge. PAMS extends the use of simple multidimensional scaling methods to identify latent profiles in a multi-test battery. Application of PAMS to profile analysis is described. The PAMS model is then used to identify latent profiles from a subgroup (N = 357) within the sample of the Woodcock-Johnson Psychoeducational Battery-Revised (WJ-R; McGrew, Werder, & Woodcock, 1991; Woodcock & Johnson, 1989), followed by a discussion of procedures for interpreting participants' observed score profiles from the latent PAMS profiles. Finally, advantages and limitations of the PAMS technique are discussed.

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Relative Cognitive Complexity of the Kaufman Assessment Battery for Children (K-ABC) and the WISC-R

Cited by 6 publications

References 23 publications

Effects of Gestational Age at Birth on Cognitive Performance: A Function of Cognitive Workload Demands

Effects of Gestational Age at Birth on Cognitive Performance: A Function of Cognitive Workload Demands

Using Profile Analysis via Multidimensional Scaling (PAMS) to identify core profiles from the WMS-III.

Estimating Cognitive Profiles Using Profile Analysis via Multidimensional Scaling (PAMS)

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