Differential Ability Scales–Second Edition

Dumont, Ron; Willis, John O.; Viezel, Kathleen; Zibulsky, Jamie

doi:10.1002/9781118660584.ese0737

Encyclopedia of Special Education 2014

DOI: 10.1002/9781118660584.ese0737

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Differential Ability Scales–Second Edition

Ron Dumont

John O. Willis

Kathleen Viezel

et al.

Abstract: The DAS-II is a useful instrument for assessing the cognitive ability of young children and adolescents. It is particularly valuable for the assessment of preschool-aged children, because few other cognitive ability tests are available for the assessment of this age group.• Accepted as an instrument that enjoyably engages children and helps to identify their processing strengths and weaknesses with efficiency and precision.

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Cited by 7 publications

References 5 publications

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How well is psychometric g indexed by global composites? Evidence from three popular intelligence tests.

Reynolds¹,

Floyd²,

Niileksela³

2013

Psychological Assessment

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Global composites (e.g., IQs) calculated in intelligence tests are interpreted as indexes of the general factor of intelligence, or psychometric g. It is therefore important to understand the proportion of variance in those global composites that is explained by g. In this study, we calculated this value, referred to as hierarchical omega, using large-scale, nationally representative norming sample data from 3 popular individually administered tests of intelligence for children and adolescents. We also calculated the proportion of variance explained in the global composites by g and the group factors, referred to as omega total, or composite reliability, for comparison purposes. Within each battery, g was measured equally well. Using total sample data, we found that 82%-83% of the total test score variance was explained by g. The group factors were also measured in the global composites, with both g and group factors explaining 89%-91% of the total test score variance for the total samples. Global composites are primarily indexes of g, but the group factors, as a whole, also explain a meaningful amount of variance.

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How well is psychometric g indexed by global composites? Evidence from three popular intelligence tests.

Reynolds¹,

Floyd²,

Niileksela³

2013

Psychological Assessment

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The Flynn effect: A meta-analysis.

Trahan¹,

Stuebing²,

Fletcher³

et al. 2014

Psychological Bulletin

270

159

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The “Flynn effect” refers to the observed rise in IQ scores over time, resulting in norms obsolescence. Although the Flynn effect is widely accepted, most approaches to estimating it have relied upon “scorecard” approaches that make estimates of its magnitude and error of measurement controversial and prevent determination of factors that moderate the Flynn effect across different IQ tests. We conducted a meta-analysis to determine the magnitude of the Flynn effect with a higher degree of precision, to determine the error of measurement, and to assess the impact of several moderator variables on the mean effect size. Across 285 studies (N = 14,031) since 1951 with administrations of two intelligence tests with different normative bases, the meta-analytic mean was 2.31, 95% CI [1.99, 2.64], standard score points per decade. The mean effect size for 53 comparisons (N = 3,951) (excluding three atypical studies that inflate the estimates) involving modern (since 1972) Stanford-Binet and Wechsler IQ tests (2.93, 95% CI [2.3, 3.5], IQ points per decade) was comparable to previous estimates of about 3 points per decade, but not consistent with the hypothesis that the Flynn effect is diminishing. For modern tests, study sample (larger increases for validation research samples vs. test standardization samples) and order of administration explained unique variance in the Flynn effect, but age and ability level were not significant moderators. These results supported previous estimates of the Flynn effect and its robustness across different age groups, measures, samples, and levels of performance.

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Factor structure of the Differential Ability Scales–Second Edition: Exploratory and hierarchical factor analyses with the core subtests.

Canivez

McGill

2016

Psychological Assessment

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The present study examined the factor structure of the Differential Ability Scales-Second Edition (DAS-II; Elliott, 2007a) standardization sample using exploratory factor analyses, multiple factor extraction criteria, and hierarchical exploratory factor analyses (Schmid & Leiman, 1957) not included in the DAS-II Introductory and Technical Handbook (Elliott, 2007b). Exploratory factor analyses with multiple factor extraction criteria and hierarchical analyses with the Schmid and Leiman (1957) procedure were conducted with the 3 DAS-II standardization samples (Lower Early Years [Ages 2:6-3:5], Upper Early Years [Ages 3:6-6:11], School-Age [Ages 7:0-17:11]). All factor extraction criteria suggested 1 factor despite the author and publisher recommended and promoted 2 (Ages 2:6-3:5) or 3 (Ages 3:6-6:11, Ages 7:0-17:11) factors. Results indicated that most DAS-II subtests were properly associated with the theoretically proposed first-order factors. Hierarchical exploratory analyses with the Schmid and Leiman procedure, however, found that the hierarchical g factor accounted for large portions of total and common variance, while the 2 or 3 first-order factors accounted for small portions of total and common variance. It was concluded that the DAS-II provides strong measurement of general intelligence but clinical interpretation should be primarily at that level. (PsycINFO Database Record

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Differential Ability Scales–Second Edition

Cited by 7 publications

References 5 publications

How well is psychometric g indexed by global composites? Evidence from three popular intelligence tests.

How well is psychometric g indexed by global composites? Evidence from three popular intelligence tests.

The Flynn effect: A meta-analysis.

Factor structure of the Differential Ability Scales–Second Edition: Exploratory and hierarchical factor analyses with the core subtests.

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