Tests for the intellectual level have now entered clinical practice in developmental age, especially when the evaluation of a child or adolescent concerns the cognitive aspects.
A typical example is that of specific learning disorders: diagnostic assessments include, among other criteria, the exclusion of the presence of an intellectual deficit; for this purpose, the practice foresees the use of tests for the IQ (IQ), usually multicomponentials such as the WISC-IV. This test is based on the so-called CHC model to measure cognitive abilities restricted e ampie.
The CHC model foresees 3 hierarchical layers: at the top there is the g factor, the one we could refer to when we talk about the person's global intelligence, the one that presumably should result from the measurement of the QI; at the intermediate level there should be some less general but still broad factors (for example, fluid intelligence, crystallized intelligence, the learning and visual perception); at the lowest level there should be more specific skills (for example, spatial scanning, phonetic coding).
The WISC-IV, like other tests, focuses mainly on the two highest layers: the g factor (hence the IQ) and the enlarged factors of the second layer (for example, the verbal comprehension, visual-perceptual reasoning working memory and processing speed).
However, in many cases the IQ does not appear to be interpretable due to large discrepancies between the various scores obtained within the WISC-IV; this is the case of specific learning disorders (SLD): according to some estimates, in 50% the intellectual profile would show discrepancies that make the IQ a meaningless number. In these circumstances, psychologists who carry out this type of assessment tend to dwell more on the factors of the second layer, analyzing the strengths and weaknesses.
In all this speech, some aspects are often overlooked:
- How much is the intellectual level (QI) is globally associated with academic difficulties?
- How much i factors of the second layer, which are usually measured by multi-component IQ tests, are predictors of academic achievement?
In 2018, Zaboski and his colleagues tried to answer this question by reviewing published research on this topic from 1988 to 2015. Specifically, they looked at studies in which intellectual level was assessed with multicomponential scales so that IQ and others factors were related to school learning. In particular, in addition to the QI, research that took into consideration was selected fluid reasoning, general information (which we could also refer to as crystallized intelligence), long-term memory, visual processing, auditory processing, short-term memory, processing speed.
What have the researchers found?
Most expanded skills would be able to explain less than 10% of academic achievement e never more than 20%, regardless of the age considered (over a period of time ranging from 6 to 19 years of age). Instead, the IQ would explain on average 54% of academic achievement (ranging from a minimum of 41% for reading at the age of 6-8, up to a maximum of 60% for basic math skills, again at the age of 6-8).
Among the expanded skills, thegeneral information it appears to be the one most closely related to some school learning, in particular to reading skills and text comprehension; in both cases the variance explained is 20%.
On the other hand, it is interesting to note the poor correlations between the fluid reasoning and almost all school learning assessed in this meta-analysis. The only exceptions are basic arithmetic skills in the 9-13 age group (11% variance explained) and mathematical problem-solving skills in the 14-19 age group (11% variance explained).
This data requires a reflection on the use of monocomponential tests such as Raven's Progressive Matrices (still today often used as the only cognitive test in many diagnostic evaluations) which are focused solely on fluid reasoning.
The almost exclusive presence of weak relationships between the broadened skills of the CHC model and school learning, suggests caution in interpreting and making predictions based on these indicators (for example, on academic performance or on the possible presence of learning disabilities).
In summary, according to the data of this research, the total score of the multicomponential intellectual scales, that is the IQ, seems to be the only data strongly connected to school performance.
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