How is the weight of Weschler subscales calculated towards general IQ?

How is the weight of Weschler subscales calculated towards general IQ?

How do the Wechsler IQ (e.g. WAIS III) tests indices (e.g. verbal IQ, performance IQ) and sub-scales contribute to overall IQ? What are the weights of each? If a person scores above 99.9th (maximum) percentile on each index, could their total IQ be estimated on this basis, and if so, what would be their estimated result?

Individual Differences

Not all individuals contribute equally or in the same way to the human condition. It was necessary for a substantial number of people with exceptional knowledge, problem-solving ability, and skills to work as a team to transform Manhattan from a forest to a metropolis. Until now, we have been focusing on differences between Stone-Age and technologically-enhanced cultures in order to appreciate extreme variations of the human condition. We have not discussed differences between the individual members of a culture. Not every member of the Nukak is the same height and weight. Not all members of the Nukak are equally skilled in blowing darts or fashioning necklaces. Not all college students are the same height and weight. Not all college students are equally proficient at shooting free throws or playing a musical instrument.

Psychology can be described as the science of individual differences. In the prior examples, psychologists would look to hereditary and experiential variables as potential causes of behavioral variation. Before we consider some controversial issues, it should prove helpful to place these issues within the larger context of how to formulate useful questions regarding individual differences. Frequently, by being specific and clear when defining terms, it is possible to shed light and avoid heat, even with the most contentious of topics. The scientific method is our best strategy for obtaining useful information to address difficult theoretical and practical questions.

I will use the game of basketball as an example since it is an internationally popular sport among adult males and females. The objective of the game is to shoot a 9-1/2–inch diameter sphere through an 18-inch circular rim located ten feet off the ground. The easiest, and most certain way to accomplish this, is to hold the basketball in your hands and “dunk” (or “stuff”) it through the rim (“hoop”). Basketball is a game where “size matters” (especially height). It is an advantage to be as close to the rim as possible.

One has to be over seven feet tall to be able to dunk a basketball while still standing on the ground. How likely is it that a person grows to be over seven feet tall? To answer this question, it would be necessary to measure everyone’s height and divide the number of people who are seven feet or more by the total. A more analytic approach would be to create a frequency distribution of the number of people of different heights.

Figure 7.18 Normal curves for height.

Figure 7.18 is an example of frequency distributions for the heights of samples of American husbands and wives. The normal curve is a symmetrical bell-shaped curve characteristic of many variables in nature, including human characteristics and performance (e.g., height, reaction time, etc.). It is defined by a formula resulting in specific percentages of the area under the curve being related to distance along the X-axis. The distance is measured in standard deviation units, a statistical index of variability (i.e., consistency). The size of the standard deviation is based on the extent to which scores cluster around the mean. If scores tend to be close to the mean (i.e., are consistent), the standard deviation is low. If the scores vary widely from the mean, the standard deviation is high. The normal curve includes approximately two-thirds of the scores between plus and minus one standard deviation, and 95 percent of the scores between plus and minus two standard deviations.

One characteristic of any symmetrical curve is that the peak indicates the mean (i.e., average) score. Another characteristic of a symmetrical curve is how “spread out” it is. The male curve above seems more spread out than the “narrower” female curve. The narrowness of a curve indicates the extent to which the scores pile up close to the mean, that is, the consistency (or variability). The female scores are more consistent (i.e., less variable) in the figure. The average height for women in the figure is 65 inches with a standard deviation of 4, and the average for men is 71 inches with a standard deviation of 5. Assuming the distributions are normal, this would mean that approximately two-thirds of women are between 61 and 69 inches, and two-thirds of men are between 66 and 76 inches. A height of seven feet (84 inches) would be almost three standard deviations above the mean height for men. This would mean that only about one in five hundred men attain that height. No wonder extremely tall individuals tend to be favored draft picks in professional basketball. They are hard to find.

Sometimes a basketball scout remarks that a particular player “has what you can’t teach.” The implication is that height is entirely genetically determined. In fact, it has been reported that hundreds of genes influence human height (Lango, Estrada, and Lettre et al., 2010). Clearly, whether one does or does not possess the Y-chromosome matters. It needs to be emphasized, however, that even a physical characteristic such as height can be significantly affected by environmental factors. The Centers for Disease Control statistics (2012) indicate that overall, the average heights for American women and men have been stable for many years. However, the heights of recent immigrants show an increase. The apparent explanation is that those recently arriving react to the American diet. Those who have been exposed to this diet for extended periods have apparently approached their genetic potential.

If you cannot reach the basket while standing on the ground, it may still be possible to dunk the ball by jumping. An amusing basketball movie from several years ago was entitled “White Men Can’t Jump.” The implication of the title was that if one created frequency distributions for men of different races, one would see diverging curves similar to those for the height of women and men. Collecting such data and plotting the curves would determine the accuracy of the title. That is, it is an empirical question. Another empirical question would address the extent to which jumping is like height. Do you think nutrition might influence jumping ability? What about exercises designed to strengthen your leg muscles or improve flexibility? Is jumping something you can teach? Do you think there is such a thing as jumping technique? As you move further from the hoop, one’s height becomes less of an advantage and skill level increases in importance. Shooting ability is clearly a characteristic related to basketball performance which can be taught and practiced.

Intelligence Testing

We will now try to apply the approach used to address questions regarding basketball to issues related to human intelligence. Perhaps no term is more misunderstood or, as we shall see, more misused, than intelligence. It is common to describe ourselves or others as being “smart” (i.e., intelligent) or “not so smart.” A repeated lesson of this book is the need to be careful when labeling people. Labels can be used as pseudo-explanations, diverting us from searching for true explanations. Also, there is always the potential for self-fulfilling prophecies. When one attributes exceptionally good or poor performance to levels of “intelligence”, the search for another explanation ceases. Once one is labeled as intelligent or dull, this can have significant effects upon how they are treated by those with the best intentions.

Do you think people vary in intelligence the way they do with height, jumping ability, and shooting from a distance? If so, is intelligence more like height, jumping ability, or shooting from a distance? The first step in addressing this question requires defining what we mean by intelligence. Recall, an operational definition defines terms by the procedures used to measure them. For example, the definition of height would be the number of standardized units (e.g., inches) from the bottom of your feet to the top of your head when you are in an erect standing position. A person’s height is observable to someone else. We cannot directly observe intelligence as we do height. Intelligence is like learning, which is also not directly observable. Rather, it is operationally defined based on behavioral observations. Technically, we do not observe learning we observe learned behavior. Applying this same approach to intelligence, we need to observe intelligent behavior.

At the beginning of the twentieth century, many of the countries experiencing the Industrial Revolution implemented compulsory education to increase the knowledge and skills of future workers. The French government asked Alfred Binet, a psychologist, to develop an easily administered test to identify children requiring special assistance to succeed in the public schools. Binet (1903) formulated an ordered list of 30 questions addressing basic skills such as memory, problem-solving, and vocabulary. Examples of simple items include asking a child to point to his/her nose and to name a food. Examples of difficult items would be to use three different words in a sentence and to provide the definition of an abstract word. Scoring was based on the concept of mental age as determined by the average number of items children of different ages got correct. It must be emphasized that Binet formulated his test to address a practical problem, school readiness, not to assess native ability. The test was designed to serve a supportive function to diagnose the type of assistance a child needed to succeed. Binet anticipated the possibility of interpreting his test as measuring intelligence, but believed intelligence was multifaceted and fluid, rather than unitary and stable. He also believed intelligence was influenced by experience and that comparisons could only be made for people sharing similar environmental conditions (White, 2000).

Despite Binet’s (1903) stated reservations, the Stanford psychologist, Lewis Terman (1916), standardized his test on American children, calculated an IQ (intelligence quotient) score as proposed by William Stern (1912), and considered it to measure intelligence. The IQ score was obtained by dividing a child’s mental age by the child’s chronological age and multiplying by 100. For example, if a 4-year old tested at the level of an average 5-year old, the IQ score would equal 125 (5/4 X 100).

Unlike Binet, Terman believed his test items measured an inherited, unitary, and stable trait of intelligence. Based on this assumption, his standardization process produced IQ test results adhering to the normal curve with a mean of 100 and standard deviation of 15 (see Figure 7.19). This meant that a little more than 68 per cent of the scores were between 85 and 115 (i.e., from minus one to plus one standard deviation) and a little more than 95 per cent were between 70 and 130 (minus two to plus two standard deviations).

Figure 7.19 Normal curve for IQ.

The Stanford-Binet became the most popular intelligence test for decades. It is ironic that a test developed to address a practical concern and considered by its founder to be inappropriate as an index of intelligence, became the basis for the first operational definition of intelligence (i.e., IQ test score). Having an operational definition for intelligence, it becomes possible to ask if the questions on the test appear to be measuring something clearly biological such as height, something probably having a strong biological component such as jumping ability, or something clearly requiring skill development such as shooting from a distance. Terman believed and acted as though IQ score, despite being inferred from behavioral observations, measured something akin to height. Arguably, a memory test such as digit span seems akin to height or jumping ability. The amount of items one is able to repeat back is limited by the capacity of short-term memory. However, the great majority of IQ test questions are obviously influenced by experience. Children are taught to label and point to different body parts. Vocabulary and grammatical rules are learned. As described in Chapter 1, children must be taught to follow instructions and work to the best of their ability in order for the test to provide meaningful results.

Would it make sense to visit the rainforest and administer the Stanford-Binet to a Nukak child in English? Based on the test results, would it make sense to make important life decisions for the child? It is unfortunate that so much controversy and harm was introduced by redefining a procedure designed to assess school readiness as a test of intelligence. Terman believed “There is nothing about an individual as important as his IQ” (Terman, 1922). It is true that, IQ score is a better predictor of school performance at all levels and of job performance than any other test result (Schmidt & Hunter, 1998). This should not be surprising. Binet and his colleagues spent 15 years developing items to determine which children would require special assistance to succeed in school. Many jobs in a technologically advanced culture are dependent on the skills and knowledge acquired in schools.

Unlike Binet, whose goal was to identify school children requiring special assistance, Terman proposed using IQ tests to classify children and place them on separate educational and career paths. This was frequently recommended despite the fact that the children were unschooled or English was not their native language. Terman became an advocate of eugenics, proposing that IQ test results should be used as a basis for controlling reproductive and educational practices. According to him, “High-grade or border-line deficiency… is very, very common among Spanish-Indian and Mexican families of the Southwest and also among Negroes. Their dullness seems to be racial, or at least inherent in the family stocks from which they come. Children of this group should be segregated into separate classes… They cannot master abstractions but they can often be made into efficient workers… from a eugenic point of view they constitute a grave problem because of their unusually prolific breeding” (Terman, 1916, pp. 91-92). Tragically, thousands of poor African-American women were involuntarily sterilized as the result of such positions (Larson, 1995, p. 74).

In 1974, Leon Kamin published The Science and Politics of IQ questioning the motivations behind the use of IQ test results as the basis for social policy recommendations. Other similar articles and books soon followed (c.f., Block & Dworkin, 1976 Cronback, 1975 Scarr, & Carter-Saltzman, 1982). In 1994, Herrnstein & Murray published The Bell Curve: Intelligence and Class Structure in American Life, sparking further controversy regarding the interpretation of research findings and their social implications. In reaction to the increasingly heated public and professional debates regarding intelligence testing, the American Psychological Association appointed a Task Force chaired by the respected cognitive scientist, Ulrich Neisser. The Task Force was charged with reviewing the findings of the voluminous research literature, reaching conclusions, and making recommendations. The authors of the report concluded:

In a field where so many issues are unresolved and so many questions unanswered, the confident tone that has characterized most of the debate on these topics is clearly out of place. The study of intelligence does not need politicized assertions and recriminations it needs self-restraint, reflection, and a great deal more research. The questions that remain are socially as well as scientifically important. There is no reason to think them unanswerable, but finding the answers will require a shared and sustained effort as well as the commitment of substantial scientific resources. Just such a commitment is what we strongly recommend (Neisser et al., 1996).

In Chapter 1, we discussed the requirements of psychological explanations and the implications regarding nature/nurture controversies. Intelligence is frequently used in a circular manner as a pseudo-explanation for behavior. Why does someone obtain a high score on an IQ test? – Because she/he is intelligent. How do you know someone is intelligent? – Because she/he scores high on the IQ test. IQ cannot serve as both an independent and dependent variable. An IQ test consists of behavioral tasks presumed to require intelligence. As such, IQ test performance is something to be explained (i.e., a dependent variable), not in and of itself an explanation (i.e., an independent variable). As always, psychology looks to nature and nurture for its explanations. No single gene has consistently been reported to have a strong effect on IQ (Deary, Whalley, & Starr, 2009). Hundreds of genes have been found to impact upon human height (Lanktree et al., 2011). It is likely that thousands of the 17,000 or so human genes influence IQ test scores.

We described how pseudo-explanations can result in self-fulfilling prophecies. It might surprise you to know that such effects have been experimentally demonstrated to occur with regard to intelligence both in the laboratory and in the field. In one study, college students were told that they were given either “maze bright” or “maze dull” rats to run through a maze (Rosenthal & Fode, 1963). Even though the rats were randomly assigned to the categories, the “maze-bright” rats performed better than the “maze dull” rats. Presumably, the students’ expectancies influenced how they treated the rats and affected the results.

In an important book entitled Pygmalion in the Classroom, Rosenthal & Jacobson (1968) demonstrated the external validity of this finding with children in schools. After tests were administered to first- through sixth-grade students, teachers were told that the results indicated that some of their students would “bloom” that year. Randomly, 20 per cent of the students in each of the classes were designated as “bloomers.” Sure enough, upon re-testing at the end of the year, first- and second grade students designated as “bloomers” improved more than the control students. The same effect was not demonstrated in the students in the later grades. It was suggested that young children are especially sensitive to the types of behaviors related to teacher expectancies.

Rather than acting as though intelligence exists as a human characteristic akin to height, it is more accurate, as well as prescriptive, to consider intelligence akin to jumping or shooting a basketball from a distance. Research must be designed to analyze the specific genetic and experiential components of behaviors considered to be intelligent. For example, what genes and learning experiences are necessary for a child to respond to an instruction to touch his/her nose, or include three words in a sentence? This approach avoids unnecessary controversy concerning racial or ethnic differences in intelligence. Rather, research is conducted to determine the potential causal variables in the acquisition of culturally-defined intelligent behaviors. Such a strategy is grounded in the reality that both nature and nurture contribute to an individual’s responding to any item on an IQ test.

Analyzing Intelligence

Do you think there is a trait of athleticism that applies to all sports? Or, do you think that there are separate abilities and skills that apply to different sports? One of Alfred Binet’s initial suggestions was that intelligence is complicated and can be analyzed into separate abilities and skills. This differed from Terman’s belief that intelligence was a unitary aptitude applicable under all conditions. More than a century has passed since Binet implemented his test in the Paris school system. Since then, other more comprehensive tests permitting more analytic scoring and prescriptive applications, have been developed.

David Wechsler gained experience developing adult intelligence tests for the military during World War 1. While serving as Chief Psychologist at Bellevue Medical Center in New York City, he developed the Wechsler-Bellevue Intelligence Scale (1939). This was later published in 1955 as the Wechsler Adult Intelligence Scale (WAIS) and revised in 1981, 1997, and 2008. Wechsler agreed with Binet that intelligence was multi-faceted and included several diverse types of questions on his test. Wechsler also believed that the verbal abilities assessed on the Stanford-Binet were highly dependent on education and therefore culturally biased. He developed a combination of tasks which did not rely on verbal knowledge and that could produce a separate performance IQ score. Subsequent revisions of the WAIS included additional types of questions and more analytical scores.

Figure 7.20 Subscales of the Wechsler Adult Intelligence Scale.

Figure 7.20 provides an overview of the different categories and types of test items and the different scores (indexes in the Figure) one can obtain with recent versions of the WAIS. The WAIS and WISC (Wechsler Intelligence Scale for Children) are presently the most frequently administered intelligence tests (Kaplan & Saccuzzo, 2009, pp 250-251). One of the reasons for this popularity is the prescriptive capability resulting from the subscale indexes and the scores for different item types comprising each subscale. For example, a low score on the vocabulary items of the Verbal Comprehension index could suggest the benefit of working with flashcards whereas a low score on the information items might suggest assignment of reading material. A similar analytic and prescriptive approach would apply to the other indexes and item types.

It is possible to use the statistical technique of factor analysis to analyze intelligent behavior based upon the results of empirical research studies. Citing more than six decades of research evaluating human cognition, John Carrol (1993) obtained results supporting a three-stratum model of cognitive ability (see Figure 7.21). The first stratum consisted of a General Intelligence factor, consistent with Terman’s unitary approach. However, the results also suggested the eight “Broad Ability” factors listed above as well as 69 narrow abilities. Analyzing intelligence test performance into different components in this way reduces the controversy resulting from a single global score. Rather than generating questions regarding differences in “intelligence”, questions regarding differences in performance on different types of tasks are generated. This requires examination of the specific broad and narrow abilities involved in answering test items. Ultimately, the genetic (nature – e.g., parts of the brain) and experiential (nurture – e.g., learning experiences)) variables influencing the abilities impacting upon specific test items need to be specified.

Figure 7.21 Carroll’s three-stratum model of cognitive ability. Key: fluid intelligence (Gf), crystallized intelligence (Gc), general memory and learning (Gy), broad visual perception (Gv), broad auditory perception (Gu), broad retrieval ability (Gr), broad cognitive speediness (Gs), and processing speed (Gt). Carroll regarded the broad abilities as different “flavors” of g.

Different Types of Intelligence

Do you think the same type of athleticism applies to all sports? Or, do you think there are different forms of athleticism applying to basketball players, baseball players, soccer players, etc.? Relating this to intelligence, it is common for people to distinguish between “school smarts” and “street smarts.” Does that distinction make sense to you? It does to Howard Gardner. Wechsler disagreed with Terman’s belief that intelligence was unitary as opposed to multi-faceted. Gardner (1983) disagreed with Terman’s belief that the Stanford-Binet test measured the only important form of intelligence and proposed a multiple intelligence model (see Figure 7.22).

Figure 7.22 Howard Gardner’s Multiple Intelligence Model.

Verbal/linguistic intelligence, logical/mathematical intelligence, and to a lesser extent, visual spatial intelligence, are the domains emphasized on the majority of standardized tests. Again, this should not be surprising since Binet developed the original test to assess school readiness. Gardner believed it was necessary to also consider bodily/kinesthetic intelligence, musical/rhythmic intelligence, intra- and inter-personal intelligence, and naturalistic intelligence, in order to appreciate the full range of human intellectual ability and accomplishment.

Intelligence and Human Potential

I previously quipped that based on our DNA and the amount of brain space dedicated to our hands and speech-related body parts, the title of this book could be “Thumbs, Tongues, and Cortex.” Human potential and accomplishment is built upon this three-legged stool. Without the conceptual knowledge, problem-solving ability, imagination, and creativity permitted by our brains (i.e., what we usually consider “intelligence”), our speaking and tool-making capabilities would be very limited. Wechsler defined intelligence as “the global capacity of a person to act purposefully, to think rationally, and to deal effectively with his environment” (1939). Eat, survive, reproduce. When we examine the aptitudes and abilities required to obtain and prepare food, build and maintain shelters, establish and maintain cooperative relationships with relatives, friends, and significant others, and raise children, we can appreciate Gardner’s consideration of other, non-school related forms of intelligence. We had to be intelligent in order to survive on this planet for a very long time before we created schools. It is only in the past century that for many, adapting to the human condition became so related to the three “R”s and performing well on standardized and non-standardized tests. One can debate the appropriateness or inappropriateness of considering any of Garner’s eight “intelligences” as aptitudes, talents, skills, or traits. What cannot be debated is the essential role each has played in the totality of human achievement and the importance of each when considering our potential as individuals and a species. Much human achievement requires cooperation and teamwork. This is true in order to survive in the rainforest or to transform Manhattan Island. Our combined potential is greater than the sum of our individual potentials. The transformation of Manhattan required cooperation among diverse individuals possessing the different talents and skills required to plan, design, and create the impressive skyline. The best strategy for realizing our potential as a species is to act upon John Adam’s and Albert Binet’s desires to educate each and every individual.

Consideration of intelligence in this chapter is out of place with regard to the organization of the book. As described in Chapter 1 and in the material above, nature and nurture are involved in intelligent human behavior. This implies the Nature/Nurture section as being the appropriate location to include intelligence. Instead, I chose to discuss intelligence as a way of concluding the Mostly Nurture section.

The bottom line of Wechsler’s definition of intelligence is its adaptive nature. What is considered intelligent depends upon one’s physical and social environmental demands. Surviving in the rainforest requires very different behaviors than performing well in school. Performing well in school requires different behaviors than performing well on the job or in social contexts. It took millions of years of natural selection for the human being to evolve. The result was an animal capable of adapting to a wide range of environmental conditions. As social and communicating animals, humans profit from the experiences of others. Shared knowledge and skills have resulted in the accelerating development of life-transforming tools and technologies. There is no way to predict the environmental conditions humans will create in the future. We can predict the continued modification of and adaptation to a new world perhaps even new worlds!

Intelligence and Self-Control

God, give me grace to accept with serenity the things that cannot be changed,

Courage to change the things which should be changed,

and the Wisdom to distinguish the one from the other.

Do you think you can be more intelligent? Your answer to the question may depend on whether you agree with Terman’s or Binet’s assumptions. If, like Terman, you believe intelligence is unitary, inherited, and fixed, a passive serenity is called for. If you agree with Binet, that intelligence is multi-faceted and affected by experience, a more active, courageous approach becomes possible. We have seen that the science of psychology has resulted in knowledge regarding procedures effecting behavior change. This makes it possible for you to apply the self-control process described in previous chapters to change the behaviors you consider to reflect intelligence and develop your potential.

At the beginning of this chapter we saw how much of our knowledge consists of concepts and that adaptation may often be described as problem-solving. A college education is designed to expand your knowledge base as well as improve and add to your problem-solving skills. Review of the types of items tested on Wechsler’s IQ test (see Figure 7.10) reveals how attending college could improve your performance on each of the sub-scale indexes. Succeeding in college will require a significant amount of reading in diverse content areas. Along the way you will acquire many new concepts and expand your vocabulary. You will take math courses requiring quantitative reasoning and humanities courses requiring comprehension and critical thinking. You can maximize the benefit of your formal education by being an active student. Constantly test yourself for mastery of the material. Try to integrate the information acquired in different courses and consider how to apply the knowledge and skills beyond the classroom. Time permitting, read for pleasure. Whether you enjoy fiction or non-fiction, reading will expose you to new information and ideas. The more you learn, the more informed and thoughtful you will become and the more likely to fulfill your potential.


Figure 7.19 “Normal curve for IQ” by Dmcq is licensed under CC BY-SA 3.0

a symmetrical bell-shaped curve characteristic of many variables in nature the peak indicates the average score and the width indicates the extent to which the scores are close to the mean (i.e., the consistency).

statistical measure of variability (consistency)

obtained by dividing a child’s mental age, measured by a test, by the child’s chronological age and multiplying by 100

calculated an IQ (intelligence quotient) by dividing a child’s mental age, based on its score, by its chronological age

intelligence test separating performance on verbal and non-verbal tasks

statistical procedure designed to determine the interrelationships of different variables (factors)

Gardner proposed distinct types of intelligence: verbal/linguistic, logical/mathematical, visual spatial, bodily/kinesthetic, musical/rhythmic, intra- and inter-personal, existential, and naturalistic

The WPPSI-IV includes extensive content changes based on the most current literature in the field and feedback from experts and clinicians.


  • Identify and qualify students with cognitive delays for special services.
  • Evaluate children for cognitive delays, intellectual disabilities, autism and giftedness.
  • Determine admittance eligibility for private schools.
  • Determine the impact of traumatic brain injury on cognitive functions in children.
  • Determine cognitive ability of children in question during custody hearings.


The WPPSI-IV places a strong emphasis on child-friendly features. The latest version of this test also includes new processing speed tasks.

  • Simplified and shortened instructions for children.
  • Demonstration, sample and teaching items, used whenever possible to ensure clarity of task demands.
  • Two new working memory subtests that provide age-appropriate, engaging tasks for children as young as 2.5 years old.
  • Three levels of interpretation: Full scale, Primary Index scale and Ancillary Index scale levels.
  • Three new game-like subtests that offer engaging art and use an ink dauber to indicate responses, which minimizes fine motor demands.
  • New and separate visual spatial and fluid reasoning composites for ages 4:0–7:7.

Sample Reports

Score reports automatically convert total raw scores and sums of scaled scores, provide strengths and weakness analysis, perform score comparisons and generate score reports with tables and graphs.

Interpretive reports include full scoring information, narrative summary of the child's background, history, and test behaviors, interpretation of scores, recommendations and optional parent report.

Combination reports allow you to upgrade from a WPPSI-IV or WIAT-III score report to include a pattern of strengths and weaknesses analysis, and an ability achievement discrepancy analysis of the combined results.

A dimensional approach to assessing psychiatric risk in adults born very preterm

Individuals who were born very preterm have higher rates of psychiatric diagnoses compared with term-born controls however, it remains unclear whether they also display increased sub-clinical psychiatric symptomatology. Hence, our objective was to utilize a dimensional approach to assess psychiatric symptomatology in adult life following very preterm birth.

We studied 152 adults who were born very preterm (before 33 weeks’ gestation gestational range 24–32 weeks) and 96 term-born controls. Participants’ clinical profile was examined using the Comprehensive Assessment of At-Risk Mental States (CAARMS), a measure of sub-clinical symptomatology that yields seven subscales including general psychopathology, positive, negative, cognitive, behavioural, motor and emotional symptoms, in addition to a total psychopathology score. Intellectual abilities were examined using the Wechsler Abbreviated Scale of Intelligence.

Between-group differences on the CAARMS showed elevated symptomatology in very preterm participants compared with controls in positive, negative, cognitive and behavioural symptoms. Total psychopathology scores were significantly correlated with IQ in the very preterm group only. In order to examine the characteristics of participants’ clinical profile, a principal component analysis was conducted. This revealed two components, one reflecting a non-specific psychopathology dimension, and the other indicating a variance in symptomatology along a positive-to-negative symptom axis. K -means ( k = 4) were used to further separate the study sample into clusters. Very preterm adults were more likely to belong to a high non-specific psychopathology cluster compared with controls.

Very preterm individuals demonstrated elevated psychopathology compared with full-term controls. Their psychiatric risk was characterized by a non-specific clinical profile and was associated with lower IQ.


Wechsler's scale is founded on his definition of intelligence, which he defined as ". the global capacity of a person to act purposefully, to think rationally, and to deal effectively with his environment." [3] He believed that intelligence was made up of specific elements that could be isolated, defined, and subsequently measured. However, these individual elements were not entirely independent, but were all interrelated. His argument, in other words, is that general intelligence is composed of various specific and interrelated functions or elements that can be individually measured. [4]

This theory differed greatly from the Binet scale which, in Wechsler's day, was generally considered the supreme authority with regard to intelligence testing. A drastically revised new version of the Binet scale, released in 1937, received a great deal of criticism from David Wechsler (after whom the original Wechsler-Bellevue Intelligence scale and the modern Weschler Adult Intelligence Scale IV are named). [4]

  • Wechsler was a very influential advocate for the concept of non-intellective factors, and he felt that the 1937 Binet scale did not do a good job of incorporating these factors into the scale (non-intellective factors are variables that contribute to the overall score in intelligence, but are not made up of intelligence-related items. These include things such as lack of confidence, fear of failure, attitudes, etc.).
  • Wechsler did not agree with the idea of a single score that the Binet test gave. [4]
  • Wechsler argued that the Binet scale items were not valid for adult test-takers because the items were chosen specifically for use with children. [4]
  • The "Binet scale's emphasis on speed, with timed tasks scattered throughout the scale, tended to unduly handicap older adults." [4]
  • Wechsler believed that "mental age norms clearly did not apply to adults." [4]
  • Wechsler criticized the then existing Binet scale because it did not consider that intellectual performance could deteriorate as a person grew older." [4]

[ check quotation syntax ] These many criticisms of the 1937 Binet test gave rise to the Wechsler-Bellevue scale that was released in 1939. While this scale has been revised many times (resulting in the present day WAIS-IV), many of the original concepts Wechsler argued for have become standards in psychological testing, including the point-scale concept and the performance-scale concept. [4]

Wechsler Intelligence Scale for Children (WISC V)

The WISC-V is the brand new gold standard assessment tool designed to measure a child's intellectual ability. It is the latest edition to replace the existing WISC-IV assessment tool. It has more interpretive power, is more efficient and more user-friendly version of the Wechsler test and has updated psychometric properties.

The Wechsler Intelligence Scale for Children Australian and New Zealand Standardised, Fifth Edition (WISC-VA&NZ) is an individually administered comprehensive clinical instrument for assessing the cognitive ability/intelligence of children aged 6 years 0 months through 16 years 11 months (6:0 - 16:11).

The WISC-V provides subtest and composite scores that represent intellectual functioning in specific cognitive domains, as well as a composite score that represents the general intellectual ability. The WISC-V is composed of 16 subtests Subtests can be grouped into two general categories: primary or secondary.

Administration of the 10 primary subtests is recommended for a comprehensive description of intellectual ability. The 6 secondary subtests can be administered in addition to the primary subtests to provide a broader sampling of intellectual functioning and to yield more information for clinical decision making. The 10 primary subtests are used in certain combinations to derive the FSIQ, the five primary index scores and three of the five ancillary index scores. Seven of the ten primary subtests are used to derive the FSIQ.

This assessment provides the following scores:

  • A Composite Score that represents a child's overall intellectual ability (FSIQ)
  • Primary Index Scores that measure the following areas of cognitive functioning: Verbal Comprehension Index (VCI), Visual Spatial Index (VSI), Fluid Reasoning Index (FRI), Working Memory Index (WMI), and the Processing Speed Index (PSI).
  • Ancillary Index Scores are also provided: The Quantitative Reasoning Index (QRI) Auditory Working Memory Index (AWMI) Nonverbal Index (NVI) General Ability Index (GAI) and the Cognitive Proficiency Index (CPI).

Some other benefits of the WISC-V include:

  • Updated items and stimuli
  • Added interpretative information useful in assisting the diagnosis of reading disorders, language disorders, ADHD, nonverbal difficulties, visual vs auditory memory deficits, executive function difficulties and visual perception issues.

It is possible for intellectual abilities to change over the course of childhood. Additionally, a child's scores on the WISC-V can be influenced by motivation, attention, interests, and opportunities for learning. For these reasons, some scores might be slightly higher or lower if a child was tested again at another time. It is therefore important to view test scores as a snapshot of a child's current level of intellectual functioning. When these scores are used as part of a comprehensive evaluation, they contribute to an understanding of a child's current strengths and any needs that can be addressed.

Stanford–Binet Intelligence Scales

The Stanford–Binet Intelligence Scales (or more commonly the Stanford–Binet) is an individually administered intelligence test that was revised from the original Binet–Simon Scale by Lewis Terman, a psychologist at Stanford University. The Stanford–Binet Intelligence Scale is now in its fifth edition (SB5) and was released in 2003. It is a cognitive ability and intelligence test that is used to diagnose developmental or intellectual deficiencies in young children. The test measures five weighted factors and consists of both verbal and nonverbal subtests. The five factors being tested are knowledge, quantitative reasoning, visual-spatial processing, working memory, and fluid reasoning.

Stanford–Binet Intelligence scales
MeSH D013195

The development of the Stanford–Binet initiated the modern field of intelligence testing and was one of the first examples of an adaptive test. The test originated in France, then was revised in the United States. It was initially created by the French psychologist Alfred Binet, who, following the introduction of a law mandating universal education by the French government, began developing a method of identifying "slow" children, so that they could be placed in special education programs, instead of labelled sick and sent to the asylum. [1] As Binet indicated, case studies might be more detailed and helpful, but the time required to test many people would be excessive. In 1916, at Stanford University, the psychologist Lewis Terman released a revised examination that became known as the Stanford–Binet test.

Neuropsychological Testing

Other Intelligence Tests

The Wechsler scales are not the only method for the assessment of intellectual abilities. Due to space limitations we cannot list all tests of intelligence. However, other tests of intellectual abilities tend to follow two different assessment methods: tests of verbal abilities and tests of nonverbal abstraction. The first method, testing of verbal abilities, is represented by tests such as the Peabody Picture Vocabulary Test–Revised (discussed in the section on Perception and Construction Abilities), in which patients are asked to choose which of four pictures best identifies a spoken word. This method is based on the high correlation between vocabulary knowledge and IQ. 44 The second method, tests of nonverbal abstraction, is represented by the Raven's Progressive Matrices Test (discussed in the section on Reasoning and Problem Solving Through Concept Formation), in which patients must abstract general principles and rules from patterns. This method tends to correlate well with the Full Scale IQ from the Wechsler scales.

Currently, the scales are available in three versions, they include WAIS-III, which measures adult intelligence, WISC-III, which measures intelligence in children, and WPPSI-R, which is designed for children aged between 4 and 6 ? years (IUPUI, 2010). There have been several revisions to improve the test ability of the scales and to include more population groups since Wechsler published the first scale in 1939. The purpose of WAIS-III is to measure adult intellectual ability. The scale is in its third edition, and is designed for individuals aged between 16 and 89 years (Pearson Assessments, 2011).

The scale is administered in the form of visual, performance, and full tests for durations of between 60 and 90 minutes. The scale’s norms include IQ and index scores, which are all designed to test the individual’s intellectual ability in a comprehensive manner. The scale’s internal structure is composed of subtests that include tests on verbal comprehension, perceptual organization, working memory, processing speed and visual memory. The validity and reliability of WAIS-III are supported by correlations with previous editions of the intelligence scales and by clinical studies on adults with hearing impairments, retardation, and other forms of cognitive disabilities.

The scale’s validity and reliability are also promoted by the availability of multiple tests administered to people with multiple intellectual abilities. WISC-III Also developed by David Wechsler, the purpose of the third edition children’s intelligence scale, (WISC-III), is to test for verbal and performance abilities among children aged between 7 and 16 years. It includes tests on information, coding, arithmetic, vocabulary, and comprehension (Kamphaus, 2005). Verbal abilities are tested through oral subtests while performance abilities are tested through nonverbal problems.

Although all tests are timed, bonus points are awarded for faster work and older children have to earn much higher points to rank with the appropriate age group. The test has several subtests grouped into the general areas of verbal and performance scales. Verbal scales are designed to measure language, memory skills, reasoning and general knowledge while performance scales are meant to measure problem-solving, spatial, and sequencing skills. Administration of the test is done by trained examiners to individual examinees and a complex test material is usually required.

In scoring, the test scores are converted to standard scores and computed with a standard deviation of 3 and a mean score of 10. Scores in the subscales of verbal and performance areas are turned into IQ scores, and later summed to obtain the overall score. All scores obtained in the tests are normative with a standard deviation of 15 and a mean score of 100. The scores are then classified to indicate the individual’s class as follows: Beyond 130- gifted, 120-129- very high, 110-119- bright normal and 90-109- average (IUPUI, 2010).

Individuals who score 85-89 are considered low average, 70-84 are classed as borderline mental functioning, and scores below 50 indicate cases of mild, moderate, or severe retardation. The multiple tests incorporated within the intelligence

Aitken, K. J. (2018). Evidence-based assessment in autism spectrum disorder: What is available, what is appropriate and what is “Fit-for-Purpose.” Jessica Kingsley Publishers.

Allen, K. A., Bredero, B., Van Damme, T., Ulrich, D. A., & Simons, J. (2017). Test of gross motor development-3 (TGMD-3) with the use of visual supports for children with autism spectrum disorder: Validity and reliability. Journal of Autism and Developmental Disorders, 47(3), 813–833

Baio, J., Wiggins, L., Christensen, D. L., Maenner, M. J., Daniels, J., Warren, Z., et al. (2018). Prevalence of autism spectrum disorder among children aged 8 years - Autism and developmental disabilities monitoring network, 11 sites, United States, 2014. MMWR Surveillance Summaries, 67(6), 1–23.

Beery, K. E., Beery, N. A., & Buktenica, N. A. (2010). Beery VMI administration, scoring and teaching manual. (6th ed.). NCS Pearson Inc.

Begovac, I., Begovac, B., Majić, G., & Vidović, V. (2009). Longitudinal studies of IQ stability in children with childhood autism–literature survey. Psychiatria Danubina, 21(3), 310–319

Behan, S., Belton, S., Peers, C., O’Connor, N. E., & Issartel, J. (2019). Moving well-being well: Investigating the maturation of fundamental movement skill proficiency across sex in Irish children aged five to twelve. Journal of Sports Sciences, 37(22), 1–9

Benson, N. F., Kranzler, J. H., & Floyd, R. G. (2016). Examining the integrity of measurement of cognitive abilities in the prediction of achievement: Comparisons and contrasts across variables from higher-order and Bifactor models. Journal of School Psychology, 58, 1–19

Bhat, A. N., Landa, R. J., & Galloway, J. C. (2011). Current perspectives on motor functioning in infants, children, and adults with autism spectrum disorders. Physical Therapy, 91(7), 1116.

Breslin, C. M., & Rudisill, M. E. (2011). The effect of visual supports on performance of the TGMD-2 for children with autism spectrum disorder. Adapted Physical Activity Quarterly, 28(4), 342–353

Brown, T., & Lalor, A. (2009). The movement assessment battery for children-second edition (MABC-2): A review and critique. Physical and Occupational Therapy in Pediatrics, 29(1), 86–103

Bünger, A., Urfer-Maurer, N., & Grob, A. (2019). Multimethod assessment of attention, executive functions, and motor skills in children with and without ADHD: Children’s performance and parents’ perceptions. Journal of Attention Disorders, 25(4), 596–606

Caeyenberghs, K., Taymans, T., Wilson, P. H., Vanderstraeten, G., Hosseini, H., & Waelvelde, H. (2016). Neural signature of developmental coordination disorder in the structural connectome independent of comorbid autism. Developmental Science, 19(4), 599–612.

Canivez, G. L., & Watkins, M. W. (2016). Review of the Wechsler intelligence scale for children-fifth edition: Critique, commenary, and independent analyses. In A. S. Kaufman, S. E. Raiford, & D. L. Coalson (Eds.), Intelligent testing with the WISC–V. (pp. 683–702). Hoboken, NJ: Wiley.

Charman, T., Pickles, A., Simonoff, E., Chandler, S., Loucas, T., & Baird, G. (2011). IQ in children with autism spectrum disorders: Data from the special needs and autism project (SNAP). Psychological Medicine, 41(3), 619–627.

Chojnicka, I., & Pisula, E. (2017). Adaptation and validation of the ADOS-2, polish version. Frontiers in Psychology, 8, 1916

Craig, F., Lamanna, A. L., Margari, F., Matera, E., Simone, M., & Margari, L. (2015). Overlap between autism spectrum disorders and attention deficit hyperactivity disorder: Searching for distinctive/common clinical features. Autism Research, 8(3), 328–337.

Craig, F., Lorenzo, A., Lucarelli, E., Russo, L., Fanizza, I., & Trabacca, A. (2018). Motor competency and social communication skills in preschool children with autism spectrum disorder. Autism Research, 11(6), 893–902

Decker, S. L., Englund, J. A., Carboni, J. A., & Brooks, J. H. (2011). Cognitive and developmental influences in visual-motor integration skills in young children. Psychological Assessment, 23(4), 1010–1016

Diamond, A. (2000). Close interrelation of motor development and cognitive development and of the cerebellum and prefrontal cortex. Child Development, 71(1), 44–56.

Dietz, C., Swinkels, S. H., Buitelaar, J. K., Van Daalen, E., & Van Engeland, H. (2007). Stability and change of IQ scores in preschool children diagnosed with autistic spectrum disorder. European Child and Adolescent Psychiatry, 16(6), 405–410

Dowd, A. M., McGinley, J. L., Taffe, J. R., & Rinehart, N. J. (2011). Do planning and visual integration difficulties underpin motor dysfunction in autism? A kinematic study of young children with autism. Journal of Autism and Developmental Disorders, 42(8), 1539–1548.

Downey, R., & Rapport, M. J. (2012). Motor activity in children with autism: A review of current literature. Pediatric Physical Therapy, 24(1), 2–20.

Fennell, E. B., & Dikel, T. N. (2001). Cognitive and neuropsychological functioning in children with cerebral palsy. Journal of Child Neurology, 16(1), 58–63

Fournier, K. A., Hass, C. J., Naik, S. K., Lodha, N., & Cauraugh, J. H. (2010). Motor coordination in autism spectrum disorders: A synthesis and meta-analysis. Journal of Autism and Developmental Disorders, 40(10), 1227–1240.

Green, R. R., Bigler, E. D., Froehlich, A., Prigge, M. B., Travers, B. G., Cariello, A. N., et al. (2016). Beery VMI performance in autism spectrum disorder. Child Neuropsychology, 22(7), 795–817.

Goldstein, S. O. S. (2018). Assessment of autism spectrum disorder. (2nd ed.). The Guilford Press.

Gordon, B. (2004). Test review: Wechsler, D. (2002). The Wechsler preschool and primary scale of intelligence, (WPPSI-III). San Antonio, TX: The psychological corporation. Canadian Journal of School Psychology, 19(1–2), 205–220

Grondhuis, S. N., Lecavalier, L., Arnold, L. E., Handen, B. L., Scahill, L., McDougle, C. J., et al. (2018). Differences in verbal and nonverbal IQ test scores in children with autism spectrum disorder. Research in Autism Spectrum Disorders, 49, 47–55.

Henderson, S. E., Sugden, D. A., & Barnett, A. L. (2007). Movement assessment battery for children-2. (2nd ed.). Pearson Assessment.

Jeste, S. S. (2011). The neurology of autism spectrum disorders. Current Opinion in Neurology, 24(2), 132

Kaur, M., Srinivasan, S. M., & Bhat, A. N. (2018). Comparing motor performance, praxis, coordination, and interpersonal synchrony between children with and without autism spectrum disorder (ASD). Research in Developmental Disabilities, 72, 79–95.

Keith, T. Z., & Reynolds, M. R. (2010). Cattell–Horn–Carroll abilities and cognitive tests: What we’ve learned from 20 years of research. Psychology in the Schools, 47(7), 635–650

Kelly, L., O’Connor, S., Harrison, A. J., & Ní Chéilleachair, N. J. (2018). Does fundamental movement skill proficiency vary by sex, class group or weight status? Evidence from an Irish primary school setting. Journal of Sports Sciences, 1–9.

Kokštejn, J., Musálek, M., & Tufano, J. J. (2017). Are sex differences in fundamental motor skills uniform throughout the entire preschool period? PLoS ONE, 12(4), e0176556

Kopp, S., Beckung, E., & Gillberg, C. (2010). Developmental coordination disorder and other motor control problems in girls with autism spectrum disorder and/or attention-deficit/hyperactivity disorder. Research in Developmental Disabilities, 31(2), 350–361.

Lai, M. C., Lombardo, M. V., & Baron-Cohen, S. (2014). Autism. Lancet, 383(9920), 896–910.

Leitner, Y. (2014). The co-occurrence of autism and attention deficit hyperactivity disorder in children - What do we know? Frontiers in Human Neuroscience, 8(1), 268–268.

Licari, M. K., Alvares, G. A., Varcin, K., Evans, K. L., Cleary, D., Reid, S. L., et al. (2020). Prevalence of motor difficulties in autism spectrum disorder: Analysis of a population-based cohort. Autism Research, 13(2), 298–306

Liu, T., & Breslin, C. M. (2013). The effect of a picture activity schedule on performance of the MABC–2 for children with autism spectrum disorder. Research Quarterly for Exercise and Sport, 84(2), 206–212

Lloyd, M., MacDonald, M., & Lord, C. (2013). Motor skills of toddlers with autism spectrum disorders. Autism, 17(2), 133–146.

Losch, H., & Dammann, O. (2004). Impact of motor skills on cognitive test results in very-low-birthweight children. Journal of Child Neurology, 19(5), 318–322

Mattard-Labrecque, C., Ben Amor, L., & Couture, M. M. (2013). Children with autism and attention difficulties: A pilot study of the association between sensory, motor, and adaptive behaviors. Journal of the Canadian Academy of Child and Adolescent Psychiatry, 22(2), 139

Medda, J. E., Cholemkery, H., & Freitag, C. M. (2019). Sensitivity and specificity of the ADOS-2 algorithm in a large German sample. Journal of Autism and Developmental Disorders, 49(2), 750–761

Mefford, H. C., Batshaw, M. L., & Hoffman, E. P. (2012). Genomics, intellectual disability, and autism. The New England Journal of Medicine, 366(8), 733–743.

Memisevic, H., & Djordjevic, M. (2018). Visual-motor integration in children with mild intellectual disability: A meta-analysis. Perceptual and Motor Skills, 125(4), 696–717

Ohara, R., Kanejima, Y., Kitamura, M., & Izawa, P. K. (2020). Association between social skills and motor skills in individuals with autism spectrum disorder: A systematic review. European Journal of Investigation in Health, Psychology and Education, 10(1), 276–296

Ozonoff, S., Goodlin-Jones, B. L., & Solomon, M. (2005). Evidence-based assessment of autism spectrum disorders in children and adolescents. Journal of Clinical Child and Adolescent Psychology, 34(3), 523–540

Paquet, A., Olliac, B., Golse, B., & Vaivre-Douret, L. (2016a). Current knowledge on motor disorders in children with autism spectrum disorder (ASD). Child Neuropsychology, 22(7), 763–794

Paquet, A., Olliac, B., Bouvard, M. P., Golse, B., & Vaivre-Douret, L. (2016b). The semiology of motor disorders in autism spectrum disorders as highlighted from a standardized neuro-psychomotor assessment. Frontiers in Psychology, 7, 1292.

Perin, C., Valagussa, G., Mazzucchelli, M., Gariboldi, V., Cerri, C. G., Meroni, R., Grossi, E., Cornaggia, C. M., Menant, J., & Piscitelli, D. (2020). Physiological profile assessment of posture in children and adolescents with autism spectrum disorder and typically developing peers. Brain Sciences, 10(10), 681.

Piovesana, A. M., Harrison, J. L., & Ducat, J. J. (2019). The development of a motor-free short-form of the Wechsler intelligence scale for children-fifth edition. Assessment, 26(8), 1564–1572

Quintas, R., Blascovi-Assis, S., Santos, D., & Santos, C. (2018). Motor performance in children and adolescents with autism spectrum disorders. International Journal for Innovation Education and Research, 6(10), 273–286

Randall, M., Egberts, K. J., Samtani, A., Scholten, R. J., Hooft, L., Livingstone, N., et al. (2018). Diagnostic tests for autism spectrum disorder (ASD) in preschool children. Cochrane Database of Systematic Reviews.

Sherwell, S., Reid, S. M., Reddihough, D. S., Wrennall, J., Ong, B., & Stargatt, R. (2014). Measuring intellectual ability in children with cerebral palsy: Can we do better? Research in Developmental Disabilities, 35(10), 2558–2567

Simons, J. (2014). Introductie tot de psychomotoriek. (7th ed.). Garant.

Smits-Engelsman, B. (2010). Movement assessment battery for children-2. Dutch standardization. Pearson Education.

Smits-Engelsman, B., & Hill, E. L. (2012). The relationship between motor coordination and intelligence across the IQ range. Pediatrics, 130(4), e950–e956

Stevens, T., Peng, L., & Barnard-Brak, L. (2016). The comorbidity of ADHD in children diagnosed with autism spectrum disorder. Research in Autism Spectrum Disorders, 31, 11–18.

Surgent, O. J., Walczak, M., Zarzycki, O., Ausderau, K., & Travers, B. G. (2021). IQ and sensory symptom severity best predict motor ability in children with and without autism spectrum disorder. Journal of Autism and Developmental Disorders, 51(1), 243–254.

Travers, B. G., Mason, A. H., Gruben, K. G., Dean, D. C., & McLaughlin, K. (2018). Standing balance on unsteady surfaces in children on the autism spectrum: The effects of IQ. Research in Autism Spectrum Disorders, 51, 9–17.

Verschueren, K. (2016). Het CHC-model van intelligentie: Een introductie. TOKK: Tijdschrift Voor Orthopedagogiek. Kinderpsychiatrie En Klinische Kinderpsychologie, 3, 97–111

Visser, J. C., Rommelse, N. N., Greven, C. U., & Buitelaar, J. K. (2016). Autism spectrum disorder and attention-deficit/hyperactivity disorder in early childhood: A review of unique and shared characteristics and developmental antecedents. Neuroscience and Biobehavioral Reviews, 65, 229–263

Wechsler, D. (2012). WPPSI-IV technical and interpretive manual supplement: Special group validity studies with other measures. NCS Pearson.

Wechsler, D. (2014). WISC-5 technical and interpretive manual supplement: Special group validity studies with other measures and additional tables San Antonio. NCS Pearson.

Whyatt, C. P., & Craig, C. M. (2012). Motor skills in children aged 7–10 years, diagnosed with autism spectrum disorder. Journal of Autism and Developmental Disorders, 42(9), 1799–1809

Wilson, R. B., McCracken, J. T., Rinehart, N. J., & Jeste, S. S. (2018). What’s missing in autism spectrum disorder motor assessments? Journal of Neurodevelopmental Disorders, 10(1), 33

Woolger, C. (2001). Wechsler intelligence scale for children-third edition (wisc-iii). In W. I. Dorfman & M. Hersen (Eds.), Understanding psychological assessment. Perspectives on individual differences.Springer.

Yerys, B. E., Wallace, G. L., Sokoloff, J. L., Shook, D. A., James, J. D., & Kenworthy, L. (2009). Attention deficit/hyperactivity disorder symptoms moderate cognition and behavior in children with autism spectrum disorders. Autism Research, 2(6), 322–333

Yin Foo, R., Guppy, M., & Johnston, L. M. (2013). Intelligence assessments for children with cerebral palsy: A systematic review. Developmental Medicine and Child Neurology, 55(10), 911–918

Yu, T. Y., Chou, W., Chow, J. C., Lin, C. H., Tung, L. C., & Chen, K. L. (2018). IQ discrepancy differentiates levels of fine motor skills and their relationship in children with autism spectrum disorders. Neuropsychiatric Disease and Treatment, 14, 597

Zander, E., Willfors, C., Berggren, S., Choque-Olsson, N., Coco, C., Elmund, A., et al. (2016). The objectivity of the autism diagnostic observation schedule (ADOS) in naturalistic clinical settings. European Child and Adolescent Psychiatry, 25(7), 769–780

Watch the video: Stanford Binet test - Intro to Psychology (January 2022).