This study investigated the link between the cognitive clusters from the Woodcock–Johnson III Tests of Cognitive Ability (WJ III COG) and Broad Math, Math Calculation Skills, and Math Reasoning clusters of the Woodcock–Johnson III Tests of Achievement (WJ III ACH) using data collected over seven years by a large elementary school district in the Southwest. The students in this study were all diagnosed with math learning disabilities. Multiple regression analyses were used to predict performance on the Broad Math, Math Calculation Skills, and Math Reasoning clusters from the WJ III ACH. Fluid Reasoning (Gf), Comprehension–Knowledge (Gc), Short–Term Memory (Gsm), and Long–term Retrieval (Glr) demonstrated strong relations with Broad Math and moderate relations with Math Calculation Skills. Auditory Processing (Ga) and Processing Speed (Gs) demonstrated moderate relations with Broad Math and Math Calculation Skills. Visual–Spatial Thinking (Gv) and Processing Speed (Gs) demonstrated moderate to strong relations with the mathematics clusters. The results indicate that the specific cognitive abilities of students with math learning disabilities may differ from their peers.

The constructs of compliance and temperament play an important role in children's school liking and engagement, and these constructs may differ between typically-developing children and children with autism because of the deficits associated with autism. The present study examined group differences among temperament, parent and child behaviors in a compliance context, and school liking and how these processes related to each other. This was the first study to examine school liking in children with high functioning autism and to explore the associations among school liking, temperament, and compliance in this population. Participants included children with high functioning autism (n = 20) and typically-developing children (n = 20) matched on language and mental age, and their parents. Compliance to a parent was observed in a laboratory setting, and temperament and school liking data were collected using parent-report measures. The findings revealed that children with autism had significantly lower Effortful Control (EC) and school liking scores than typically-developing children. However, there were no group differences in compliance, and no significant relation was found between temperament and compliance. Additionally, school liking scores were related to compliance and EC. These findings are discussed with respect to implications for potential future research and use of interventions for children with high functioning autism.

The Wechsler Intelligence Scale for Children – ; Fourth Edition (WISC – IV) is one of the most popular intelligence tests used for special education eligibility purposes in the United States. Despite the large prevalence of children and adolescents with Attention Deficit Hyperactivity Disorder (ADHD), the factor structure of the WISC – IV among this population has not been explored. Thus, the factor structure of WISC – IV scores among students with ADHD was investigated via replicatory factor analysis followed by a comparison with the factorial structure of the normative sample using the coefficient of congruence. The four factor model proposed by Wechsler was consistent with the factor structure found in the sample of students with ADHD for all subtests except Picture Concepts and Matrix Reasoning. The Verbal Comprehension and Processing Speed factors appeared to measure the same construct in the study sample as in the normative sample, while the Perceptual Reasoning and Working Memory factors were only fairly similar to the normative sample. It is recommended that clinicians interpret Perceptual Reasoning and Working Memory index scores of students with ADHD cautiously. Limitations of the study, future directions for research, and implications for practitioners are discussed.

The Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) General Abilities Index (GAI) and Cognitive Proficiency Index (CPI) have been advanced as possible diagnostic markers of Attention-Deficit Hyperactivity Disorder (ADHD). Diagnostic utility statistics were used to test the ability of GAI-CPI difference scores to identify children with ADHD. Participants included an ADHD sample (n = 78), a referred but non-diagnosed hospital sample (n = 66), and a simulated sample with virtually identical psychometric characteristics as the WISC-IV 2,200 child standardization sample. Receiver Operating Characteristic (ROC) analyses were computed to determine the utility of GAI-CPI difference scores to identify children with ADHD. The GAI-CPI discrepancy method had an AUC of .64, 95% CI [0.58, 0.71] for the ADHD sample compared to the simulated normative sample and an AUC of .46, 95% CI [0.37, 0.56] for the ADHD sample compared to the referred but non-diagnosed hospital sample. These AUC scores indicate that the GAI-CPI discrepancy method has low accuracy.