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The fourth edition of the Wechsler Adult Intelligence Scale (WAIS-IV) is a revised and substantially updated version of its predecessor. The purposes of this research were to determine the constructs measured by the test and the consistency of measurement across large normative and clinical samples. Competing higher order WAIS-IV four- and five-factor models were analyzed using the WAIS-IV’s sample of 1,800 normative adults and 411 clinical adults. When all 15 WAIS-IV subtests were considered, both four- and five-factor models were suitable, but the five-factor model provided a better fit. The WAIS-IV PRI differentiated into two composites as follows: POI(Gv) consisting of Block Design, Visual Puzzles and Picture Completion; and FRI(Gf) consisting Matrix Reasoning, Arithmetic and Figure Weights. The five-factor solution included Quantitative Reasoning (RQ), consisting of Arithmetic and Figure Weights, as a narrow ability subsumed under FRI(Gf). Arithmetic, Vocabulary, and Figure Weights subtests had the highest g loadings. Cancellation had the lowest g loading. The WAIS-IV generally demonstrated full factor invariance between clinical and nonclinical samples.

The purpose of this study was to determine the constructs measured by the WISC-IV and the consistency of measurement across large normative and clinical samples. Competing higher order four-and five-factor models were analyzed using the WISC-IV normative sample and clinical subjects. The four-factor solution is the model published with the test manual. In the five-factor model, the POI differentiated into a visual-spatial factor (consisting of Block Design and Picture Completion) and a fluid reasoning factor (consisting of Matrix Reasoning and Picture Concepts, with and Arithmetic). The five-factor solution included Inductive Reasoning (IR), consisting of Matrix Reasoning and Picture Concepts, as a narrow ability subsumed under the FRI (Gf). When all 15 WISC-IV subtests were considered, both four-and five-factor models were suitable and showed close model-data fit. Further, both models generally demonstrated full factorial invariance between clinical and nonclinical samples. Interpretation of the fifth factor is discussed.

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This study investigated the direct and indirect effects of general intelligence and 7 broad cognitive abilities on mathematics achievement. Structural equation modeling was used to investigate the simultaneous effects of both general and broad cognitive abilities on students' mathematics achievement. A hierarchical model of intelligence derived from the Cattell-Horn-Carroll (CHC) taxonomy of intelligence was used for all analyses. The participants consisted of 4 age-differentiated subsamples (ranging from ages 5 to 19) from the standardization sample of the Woodcock-Johnson III (WJ III; Woodcock, McGrew, & Mather, 2001). Data from each of the 4 age-differentiated subsamples were divided into 2 data sets. At each age level, one data set was used for model testing and modification, and a second data set was used for model validation. The following CHC broad cognitive ability factors demonstrated statistically significant direct effects on the mathematics achievement variables: Fluid Reasoning, Crystallized Intelligence, and Processing Speed. In contrast, across all age levels, the general intelligence factor demonstrated indirect effects on the mathematics achievement variable.