Gifted children and adolescents often score lower on measures of cognitive processing speed than on other broad abilities commonly assessed by intelligence tests. Multiple studies have demonstrated that gifted students as a group typically earn mean scores in the average or high average range on measures of processing speed (Doobay et al., 2014; Margulies & Floyd, 2009; Rowe et al., 2014) and that individual scores for these lower-order processing abilities range from superior to below average (Buzinkai, 2013; Vecchio, 2013). There is debate in the literature about why this phenomenon occurs. Some experts argue that gifted students work more carefully and slowly than non-gifted peers (Robinson, 2002), while others suggest gifted students may score lower on measures of processing speed because it is not a core reasoning ability associated with giftedness.
At last year’s conference, we presented a poster that attempted to determine why gifted children and adolescents tend to score lower on measures of cognitive processing speed than on other broad abilities commonly assessed by intelligence tests. In this poster, we tested whether this phenomenon could be the result of regression to the mean using simulated subtest scores for the 16 subtests on the Wechsler Intelligence Scale for Children—5th Edition. Because processing speed tasks load at a lower level on g, we expected that gifted students would show considerably lower scores on the Processing Speed Index than on the more cognitively complex Indexes. Processing Speed scores were, on average, 9 and 8.5 points lower than Verbal Comprehension and Fluid Reasoning scores, respectively, in the simulated data. In contrast, though, studies using real data have shown score differences as diverse as 2.63 (Rowe et al., 2014) and 17.47 points (Doobay et al., 2014). Therefore, the most we could say using the simulated data was that the lower scores of gifted students on measures of processing speed were only partially explained by regression to the mean, as some data from actual samples of gifted children showed larger differences than were shown in our simulated data.
For our current presentation, we will compare simulated data with real data from the same measures. Results from factor analyses of the WISC-IV and WISC-V will be used to generate simulated subtest scores for 10,000 “students” for each of the associated subtests. Subtest scores will be used to calculate composites reflecting Full Scale IQ as well as the Indexes associated with each of the WISC batteries used, as per the directions found in each manual. We will compute mean scores on the Indexes for students with FSIQs of 130 and above.
These findings will be compared to data from the gifted subsamples collected as validation subsamples for the WISC-IV and WISC-V. These gifted subsamples included students, categorized as gifted and who previously scored 130 or above on a measure of intelligence; all were subsequently administered the WISC-IV or WISC-V. The advantage of using these subsamples is that they resemble the gifted population in schools, who are frequently identified using a variety of measures, including group-administered intelligence tests. A repeated measures ANOVA will be used to test whether differences between simulated and real data are statistically significant. In particular, the analyses will focus on whether the processing speed scores are equally or less divergent in the gifted versus the simulated samples.
Group differences , Cognition and Attention , Measurement and Psychometrics
