developmental; week 2; identifying atypical development

reasons for atypical development

• Pre-natal effects (e.g. exposure to teratogen)

  • Fetal Alcohol Spectrum Disorder

• Environmental effects (e.g. complications during birth)

  • Cerebral Palsy

• Genetic effects

  • Hereditary

  • Spontaneous mutations (e.g. Copy Number Variants)

• Unknown (likely multifaceted) effects:

  • Autism Spectrum Conditions

  • ADHD

Genetic reasons for atypical development

DNA > GENES > CHROMSOME > CELL

genetic abnormalities = too many or too few of particular genes resulting from:

• extra chromosome

  • e.g. Down’s Syndrome

• duplication of a certain part of a chromosome

  • e.g. 16p11.2

• deletion of a certain part of a chromosome

  • e.g. William’s syndrome

What is atypical development?

• Difficult to define “atypical development” in the context of

  a)Individual differences in the rate of development

  b)Individual differences in people’s traits, strengths and weaknesses

• Textbook Definition = “The extremes of individual differences in development”

• Can include: advanced development and delayed development

• Generally associated with neurodevelopmental conditions (e.g. autism spectrum conditions, ADHD, William’s syndrome, intellectual disability, etc).

Developmental regression

• Regression is typically seen in children with  autism spectrum conditions and / or intellectual disability.

• Regression is defined as a period where a particular skill is developing along a typical trajectory, but then a child loses aspects of this skill, e.g. stops speaking in two word phrases.

• Most often seen in language and in motor skills.

• 

Development occurs across multiple domains

• adaptive behaviour

  • daily living skills

  • independence

  • personal responsibility

  • managing money

  • personal safety

  • ability to work

  • functional decision making

• social

  • gestures

  • emotional IQ

  • turn-taking

  • non-verbal communication

  • social interactions

  • verbal communication

  • empathy

  • reciprocal eye contact

• cognitive

  • memory

  • IQ

  • attention

  • language

  • executive function

  • numerical ability

• physical

  • facial dysmorphism

  • macrocephaly

  • physical features

    • e.g. heart

  • microcephaly

• motor skills

  • fine motor skills

  • balance

  • gross motor skills

  • activity level

  • coordination

How can we identify if development is atypical?

• the normal distribution

  • obtained by testing many (100+s) participants

  • for many variables (e.g. height, weight, IQ, other cognitive abilities) samples from the population generate a normal distribution

  • ‘normal distribution’ - ‘normal curve’ = ‘bell shaped curve’

identifying and measuring atypical development (and example)

• Group comparisons against a representative (or ‘normative’) sample.

• It is important to choose an appropriate control group

Example:

• Claire is 10. She obtained 45 / 160 on a test which measures verbal reasoning. A representative sample of 500 other 10 year olds generated an average score of 100/160.

• Is Claire developing atypically? does she have advanced or delayed verbal reasoning?

• 

• Claire is 10. Claire has an intellectual disability and a mental age of 5. She obtained 45 / 160 on a test which measures verbal reasoning. A sample of 500 5 year olds generated a normal distribution with an average score of 50.

• Claire has average verbal reasoning abilities compared with her mental-age matched peers

Important point for identifying and measuring atypical development

• We can’t build a full profile and understanding of a child’s abilities unless we identify their strengths and weaknesses

• ‘Strengths’ can be subjective or ‘relative’

  • Something they’re good at compared to their other skills

  • Not necessarily a strength compared to other people

important point: example

Relative strength:

• Claire shows a strength in verbal reasoning relative to her overall IQ profile

• 

Whta to bear in mind when investigating atypical development

• it is important to compare performance against appropriate control groups and also to consider the child’s overall ability and profile of strengths and weaknesses

  • for example, is it usual to compare performance against two ‘control’ groups:

  • one group matched on chronological age, another group matched on mental age

• the examples only consider Claire’s ability at one point in time, but what about her developmental trajectory?

  • investigating skills and abilities over time provides insights into what we can expect of an individual’s development

what tools do we have to measure cognitive development

specific experimental designs

• Designed to investigate a specific research question or hypothesis; target specific behaviours

• The format can vary widely depending on the research question and methodology.

• Can compare participants’ results with a matched control group (e.g. age, gender, IQ)

• Examples include face recognition tasks, theory of mind tasks & executive function tasks

standardised tests

• Designed to measure knowledge or skills in a consistent and comparable way across a large population

• Follow a fixed format with specific instructions, questions, and scoring procedures

• Participants’ scores can be standardised i.e. assigned a value that indicates how well they performed compared to every other person who has taken the test (regardless of individual differences)

• Examples include generalised intelligence tests e.g.

• Weschler Adult Intelligence Scale (WAIS)

• Weschler Intelligence Scales for Children (WISC)

• British Ability Scales

specific Vs standardised tests

• Goal: Standardised tests aim to measure broader skills, while experiments aim to test a specific skill or test hypotheses.

• Scope: Standardised tests are broad in scope, covering a range of topics or skills, while experiments are focused on a specific research question.

• Generalisability: Standardised tests aim to generalise results to a larger population, while experiments may have limited generalisability depending on the sample and conditions.

Wechsler Intelligence Scale for Children (WISC)

• the Weschler Intelligence Scale for children is a widely used intelligence test for children aged 6 to 16

• it includes:

  • Working Memory Index

  • Verbal Comprehension Index

  • Processing Speed Index

  • Visual Spatial index

  • Fluid reasoning Index

Outline the indexes of the WISC

• Verbal Comprehension Index (VCI)

  • This index measures a child's ability to understand and use language, as well as their verbal reasoning skills.  

• Visual Spatial Index (VSI)

  • This index measures a child's ability to perceive, analyse, and manipulate visual information.  

• Fluid Reasoning Index (FRI)

  • This index measures a child's ability to solve novel problems and think flexibly.  

• Working Memory Index (WMI)

  • This index measures a child's ability to hold information in mind

• Processing Speed Index (PSI)

  • This index measures a child's ability to quickly and accurately process information.

Tasks of the WISC (Full scale)

verbal comprehension

• similarities

  • i.e. how are a lion and a rabbit similar?

  • how are happiness and anger similar?

• vocabulary

  • what does the word ‘coat’ mean?

  • what does ‘tradition’ mean?

• information

• comprehension

visual spatial

• block design

• visual puzzles

  • 

Fluid reasoning

• matrix reasoning

  • 

• figure weights

  • 

• picture concepts

• arithmetic

Working Memory

• digit span

  • i.e. repeat these numbers in the same order I say them to you

  • “3,7,14,9,10”

  • repeat these numbers in the reverse order

  • “7,9,12,19,8”

• picture span

• letter-number sequencing

processing speed

• coding

• symbol search

• cancellation

Measuring development: adaptive behaviour

Vineland Adaptive Behaviour Scale (VABS)- semi structures interview carried out with caregiver/ teacher

• Communication:

  • Receptive: what he or she understands

  • Expressive: What the individual says

  • Written: What he or she read and writes

• Daily Living Skills:

  • Personal: How the individual eats, dresses

  • Domestic: What household tasks the individual performs

  • Community: How the individual uses time, money, etc.

• Socialization:

  • Interpersonal Relationships: How the individual interacts with others.

  • Play and Leisure Time: How the individual plays.

  • Coping skills: How the individual demonstrates responsibility and sensitivity to others.

• Motor Skills:

  • Gross Motor: How the individual uses arms and legs for movement and coordination.

  • Fine Motor: How the individual uses hands and fingers to manipulate objects.

• Maladaptive Behaviour

  • Internalizing, Externalizing and other types of undesirable behaviour that may interfere with adaptive functioning

What about tests for non-verbal participants?

Wechsler Nonverbal Scale for Ability (WNV):

• Assesses non-verbal reasoning and problem-solving skills in individuals aged 4 to 21.

• It uses visual stimuli and requires minimal verbal instruction

• Consists of subtests such as Object Assembly, Block Design, and Picture Arrangement.

Leiter International Performance Scale- Revised (Leiter-R)

• Assesses cognitive abilities in individuals aged 3 to 75.

• It uses a variety of tasks, such as matching pictures, completing patterns, and solving mazes, to assess different aspects of intelligence.

• Particularly useful for assessing individuals with autism, language impairments, or hearing impairments.

What about tests for younger patients?

Bayley Scales of Infant and Toddler Development (Bayley-III):

• Infants and toddlers aged 1 to 42 months.  

• It evaluates cognitive, motor, language, social-emotional, and adaptive behaviour.  

• Tests include observation of motor skills (e.g. rolling), tests of cognition (e.g. attention span) and social interaction

Infant-Toddler Developmental Assessment (IDA):

• From birth to 36 months who are at risk of developmental delays or conditions

• It evaluates cognitive, motor, language, social-emotional, and adaptive behaviour through observation, parent report, and standardised tasks.

• The IDA is often used in early intervention programs to identify children who need additional support.  

Scoring standardised tests

• After completing the test add the scores together to create a raw score

• How useful is this score?

• Can we compare it to older individuals, for example?

• Do you think it’s fair to compare a 10 year old to a 30 year old?

  • No, it’s not comparable!

• So what do we do?

  • We convert the raw score to something called a ‘standardized score’

  • Standardising a score converts the raw score to a value that represents how a participant has performed compared to others of the same age/gender

  • This allows us to remove individual differences and generate a score that we can compare across participants

Why do we standardise scores?

Benefits of using standardised scores

• enables researchers/ clinicians to standardised performance across different groups, different tests, etc

• they provide a common language for discussing test performance regardless of how the actual test is designed

• easily interpretable for clinicians/ researchers

• there isn’t one set way of standardising, but they all end up allowing the same comparison

Reading- abstract

• WISC is the most widely used instrument in assessing cognitive ability, especially with children with autism spectrum disorder (ASD).

• There is a lack of research concerning the most recent iteration- WISC V.

• This study sought to identify the pattern of performance of children with ASD on the WISC-V using a classification and regression (CART) analysis.

• The current study used the standardization sample data of the WISC-V obtained from 62 children diagnosed with ASD, along with demographically matched controls, comprised the sample.

• Results revealed the Comprehension and Letter-Number Sequencing subtests were the most important factors in predicting group membership for children with ASD with an accompanying language impairment.

  • Children with ASD without an accompanying language impairment, however, were difficult to distinguish from matched controls through the CART analysis.

• Results suggest clinicians should administer all primary and supplemental subtests of the WISC-V as part of a comprehensive assessment of ASD diagnosis

  • this is best accomplished through evidence-based assessment practices, including the use of psychometrically sound standardized measures.

• cognitive assessment is only a piece of the assessment, but psychologists must understand how children with ASD perform on these measures to ensure an evidence-based assessment occurs and the most diagnostic clarity is possible.

Reading: the use of the Wechsler scales of intelligence for ASD

• The Wechsler Scales of Intelligence are the most commonly utilized tests of cognitive ability in an evaluation of ASD.

• Aiello et al. (2017) found the majority of school psychologists choose the WISC-IV when assessing the cognitive functioning of children with ASD.

• Little research, however, has been conducted using the WISC-V for children with ASD.

• Various patterns of performance have been identified using the past versions of the WISC, and these patterns can differentiate children with ASD from their peers

Cognitive profiles in ASD- IQ

• Children with ASD exhibit variability in cognitive skills across measures and time, which generates concern with using IQ as an outcome measure.

• Although cognitive profiles may vary, there is evidence of a cognitive phenotype of ASD.

• Many individuals with ASD fall 2-3 SDs below the mean on tests of intellectual functioning, representing an intellectual disability.

  • 1/3 of individuals with ASD perform two SDs below the mean on measures of cognitive ability, with 50% performing at least one SD below the mean.

• Support shows average to superior performance on fluid reasoning, visuospatial skills, and WM in some children with ASD.

• This may be accompanied by deficits in processing speed and verbal reasoning.

  • Due to the divergent profiles, the DSM-5 includes the specifiers “with or without accompanying intellectual impairment”.

• For children with ASD, IQ has been found to be the most highly correlated variable to symptom severity, such that as IQ increases, the severity of symptoms decreases.

Cognitive profiles in ASD- Comparing Wechsler Intelligence scales Indices (FDI, WMI, PSI, VCI)

The Wechsler profile among school-age children with ASD is characterized by lower scores on the Freedom from Distractibility Index (FDI) and the Working Memory Index (WMI) on the WISC-IV, and lower performance on the Processing Speed Index (PSI) in comparison to the Verbal Comprehension Index (VCI) and the Perceptual Reasoning Index (PRI) WISC-IV.

Most studies find relatively high scores on Block Design and low scores on Comprehension and Coding/Digit Symbol.

Mayes and Calhoun (2003) found no significant differences of the WISC-III Index scores for ASD children with IQ scores <80, whereas ASD children with IQ scores > or = to 80 exhibited significantly higher scores on VCI and POI over FDI and PSI.

Mayes and Calhoun found that those who had IQs of 80 or higher demonstrated significantly more scatter than the norm.

Children with high-functioning autism tend to demonstrate attention, graphomotor, and processing speed weaknesses in contrast to strengths in verbal and visual reasoning.

The different versions of the Wechsler Intelligence Scales

Utilizing the WISC-III, Mayes and Calhoun (2004) found 3 patterns of performance.

• They found low Coding and Comprehension scores characterized children with ASD; low Coding without low Comprehension scores identified children with ADHD and learning disability; and low Performance without low Coding characterized children who had a traumatic brain injury.

• Therefore, they were able to identify high-functioning autism with 73% accuracy based on low FDI, PSI, and Comprehension subtest scores.

Ehlers et al. (1997) aimed to evaluate the discriminating ability of the WISC-R (Swedish version) for autistic disorder, Asperger syndrome, and attention disorders through identifying characteristic WISC profiles within each of the three diagnostic groups.

• They found an overall rate of correct diagnostic classification of the 3 groups of 63%.

• They showed that discriminating across groups was suitable for 49% of cases when separating autism from Asperger’s and autism from attention disorders; however, only 16% of cases were able to be distinguished between Asperger’s from attention disorders.

An intragroup analysis showed Asperger syndrome was associated with good verbal ability but poor perceptual ability, whereas autism was associated with relatively superior ability in visuospatial skills.

• they differ in measures for crystallised intelligence

Children with high-functioning ASD performed significantly weaker on subtests making up the Verbal Comprehension Index, specifically Vocabulary and Comprehension as well as the Processing Speed Index consisting of Symbol Search and Coding.

Reading- purpose of the present study

Assessment of intellectual functioning is an important component of an educational evaluation of ASD. Tests of cognitive ability can help guide intervention planning, which results in more appropriate IEP goals for students with ASD.

Given the Wechsler tests are the most preferred cognitive ability measure for psychologists conducting autism evaluations, understanding how this new edition performs with the autism population will aid in its utility in the school and clinic settings.

Current knowledge of the WISC-V with children with ASD is limited. The present study explored the cognitive abilities of the individuals with ASD who participated in the standardization of the WISC-V through a classification and regression trees (CART) analysis.

Reading: methods: procedure

The present study employed the standardization sample data obtained from the publishers of the WISC-V. Data from this sample were obtained through the procedures specified in the Technical and Interpretative Manual of the WISC-V. The current study was approved by the researchers’ university’s Institutional Review Board.

Reading: methods: participants

62 children diagnosed with ASD and demographically matched controls were included. The sample was further described with accompanying language impairment and without accompanying language impairment.

30 children with ASD-LI and 32 ASD-NLI.

Children were initially excluded from participation if they had existing cognitive ability scores below a specified range indicating intellectual disability (<60 for ASD-LI and <70 for ASD-NLI), and the former group was required to have adequate language skills to participate.

Control participants were randomly selected from the standardization sample and matched on the following demographic variables: age, sex, race/ethnicity, parent education, and geographic region.

Reading: method: measures

The WISC-V is the most recent version of the Wechsler Intelligence Scale for Children, originally published in 1949.

The WISC-V is an individually administered intelligence test for children ages 6- 16 years, 11 months.

It includes 21 subtests with 10 considered primary and required for the 5 Primary Index scores (Verbal Comprehension, Visual Spatial, Fluid Reasoning, Working Memory, and Processing Speed).

Of the 10 primary subtests, 7 are used to calculate the Full Scale IQ (FSIQ). The five Primary Index scores and the FSIQ provide a comprehensive evaluation of a child’s intellectual functioning.

Reading- data analysis

To identify the test score combinations that differentiate the groups, classification and regression trees (CART) analysis was used.

CART easily identifies the interactions of test scores to identify if certain combinations classify the groups. The family-wise Type I error rate for the statistic determining the number and location of splits in the tree was controlled using the Bonferroni correction, and a Monte Carlo approach was employed to determine the level of statistical significance. 4 CART analyses were conducted. 2 of these focused on differentiating children with ASD from the matched controls using either the WISC-V subtests or the indexes, and the other two analyses were used to differentiate ASD-LI, ASD-NLI, and the matched sample using either the subtests or the indexes. To assess the overall performance of the models, both prediction accuracy rates using the sample and Cohen’s kappa statistic were calculated for each CART model.

Reading- results: ASD Vs matched controls

The first CART analysis focused on differentiating ASD and the matched controls based on the WISC-V subtests.

100% of the individuals with Comprehension scores < or = to 6 and Symbol Search scores < or = to 10 (n = 24) had ASD.

65% of individuals with Symbol Search scores > 6 and Letter-Number scores < or = to 9 (n = 30) had ASD.

In contrast, those with Comprehension scores > 6, and Letter-Number scores > 9 were less likely to have ASD (0-40%).

The model accurately classified group membership for 77.4% of the individuals in the sample, with greater accuracy for the ASD group.

Primary Index scores:

• The individuals with WM scores of 85 or less, and Verbal Comprehension scores of 95 or less had ASD (100%).

• Children with WM scores < or = to 85 and Verbal Comprehension scores > 95 had ASD in 62.5% of cases.

• Individuals with WM scores > 85, and Processing Speed scores > 86 were relatively unlikely to have been ASD (28.4%),

• 44% of those with WM scores > 85 and Processing Speed scores < or = to 86 were ASD.

• The model based on the index had a classification accuracy rate of 73.4%, with much higher accuracy for the ASD group.

Reading- results: ASD: second analyses

A second set of analyses was conducted where the ASD sample was divided into language impaired (LI) and non language impaired (NLI) and matched controls. CART was used to differentiate from among the 3 groups.

ASD-LI children were characterized with lower scores on Comprehension, Letter Number, and Cancellation.

ASD-NLI members:

• Those with Comprehension scores ≤ 9, Letter-Number scores ≤ 8, and Cancellation scores >7 were most likely ASD-NLI.

The prediction accuracy results demonstrate that the model was able to accurately classify group membership for 79% of the sample

• greatest accuracy for ASD-LI (95.8%),

• the lowest for ASD-NLI(62.1%).

Individuals with scores on WM ≤ 85 and Verbal Comprehension ≤ 96 were very likely to have been ASD-LI (83.3%), with 0% matched control group and 16.7% being ASD-NLI.

The nonclinical matched sample was most commonly associated with WM > 107, or WM > 85 and Processing Speed > 89.

The overall classification accuracy rates for this CART model was 67.7%, with a kappa of 0.477 (weak).

The model classified ASD-LI membership with 83.3% accuracy but not so well for the ASD-NLI group (41.2%)

Reading- discussion

Previous literature indicates that children with ASD have similar performance on the Wechsler Scales.

The WISC is the first-choice cognitive assessment within schools and clinical settings, so understanding the performance of children with ASD on cognitive ability tests is vital for school psychologists to make the most appropriate eligibility recommendations for the student’s educational needs.

This study was the first to look at the cognitive patterns of children with ASD through a CART analysis.

Overall, children with ASD with accompanying language impairment were most accurately classified as ASD based on Working Memory Index (WMI) and Verbal Comprehension Index (VCI) scores, but children with ASD without accompanying language impairment were less accurately classified compared to typically developing peers.

Verbal comprehension index (VCI) - children’s performance on the VCI and WMI in new WISC vs previous WISC

The WISC have been criticized for its high language load. The subtests that comprise the VCI require the child to comprehend the meaning of the verbal prompts for each item and provide a verbal response.

In a disorder characterized by social communication and language impairments, the expressive and receptive demands seem to place those with ASD at a disadvantage compared to their typically developing peers.

Our results indicated children’s performance on the VCI and WMI were most predictive in classifying children as ASD. Previous research suggests varied profiles based on diagnoses within the spectrum. Nader et al. (2015) found children with Asperger’s disorder performed better on the VCI compared to the other indexes.

Because these conditions have been collapsed into one spectrum of symptomology, it seems imperative clinicians use the language impairment specifier.

The most consistent area of cognitive weakness seen in children with ASD is language comprehension abilities and social reasoning. Researchers have demonstrated that the Comprehension subtest was significantly weaker than other verbal tests on previous editions of the WISC. Longer responses are often needed for obtaining full credit on a Comprehension item which helps explain this pattern.

Our results suggest that higher Similarities scores are indicative of children with ASD without a language impairment. The newest version only requires the administration of the Similarities and Vocabulary to obtain the VCI, which are strengths of a child with ASD so may hide their weaknesses and present an inflated idea of their verbal skills. Our results indicated that the WISC-V does exclude the weakest area of verbal ability from the VCI for individuals with ASD

Working Memory Index

Children with ASD were often differentiated from the matched controls on the WMI; however, a closer look at the CART analysis of the 3 groups suggests WM skills vary based on language skills.

When considering speech onset delay in an autistic group, Nader et al. (2015) found that WMI was significantly weaker than the FSIQ, and those with a speech delay exhibited lower scores.

Conversely, on the WISC-IV, Oliveras-Rentas et al. (2012) found that children with Asperger’s (i.e., those without language delays) demonstrated WMI scores within the average range.

At the subtest level, Letter-Number Sequencing was identified in the CART analysis as a significant factor. A high % of children with low Letter-Number Sequencing scores were either ASD-LI or ASD-NLI, indicating this subtest is a weakness for many individuals with ASD irrespective of language skills.

Processing speed index and the working memory index- coding and symbol search

The greatest number of children without ASD had a WMI standard score > 85 with a Processing Speed Index (PSI) standard score > 86. Previous literature suggests children with ASD perform poorly on the PSI. Oliveras-Rentas et al. (2012) examined the WISC-IV profile of children with ASD without an intellectual disability, and PSI was the greatest weakness.

Research suggests children with high-functioning ASD display the lowest index scores on the PSI or the WMI of the WISC-IV. The Coding subtest represents a significant weakness for the ASD population however, results indicated Symbol Search and Cancellation were more helpful in separating the groups. For instance, all children with low Comprehension and low Symbol Search scores were in the ASD group. This finding indicated Symbol Search was also a weakness of children with ASD.

Perceptual Reasoning Index, fluid Reasoning Index and Visual Spatial Index

Previous literature indicated children with ASD perform significantly better on the Perceptual Reasoning Index (PRI) compared to the VCI, and PRI is the highest mean index score.

The PRI has now been separated into two different indexes, the Visual Spatial Index (VSI) and Fluid Reasoning Index (FRI); thus, it is necessary to examine how the cognitive profile may differ to previous editions of the WISC.

The two CART analyses at the index level indicated that the VSI and FRI were not helpful in differentiating ASD-LI, ASD NL, and non-ASD. This finding is may be due to the reconceptualization of these abilities and inclusion of two new subtests to the WISC-V (Visual Puzzles and Figure Weights).

Additionally, the nonverbal strengths and weaknesses of children with ASD are now included in separate indexes. Prior to the WISC-IV, Block Design was the highest of the nonverbal subtests in children with autism. However, it appears that peak performance on the Matrix Reasoning subtest on the WISC-IV emerged instead.

Mayes et al. (2008) found that children with high-functioning autism scored the highest on Matrix Reasoning subtest in comparison to the other PRI subtests, and Block Design was found to be the lowest of the nonverbal subtests. Matrix Reasoning and Block Design are now included within different indexes of the WISC-V, which may have impacted the relevance of the index scores within the CART analyses.

Implications for practice

Overall, the CART analysis that resulted in the highest accuracy rate included all subtests and differentiated the sample into three groups.

This model resulted in 95% accuracy for predicting ASD-LI classification, much higher than for ASD-NLI. These results suggest that greater cognitive differences exist between children with ASD-LI and typically developing peers compared to those with ASD-NLI.

Given the supplemental status of several subtests of the WISC-V that were identified within the CART analysis, clinicians and school psychologists should administer extra subtests in cases where more information on a child suspected of having ASD is necessary. Specifically, these results suggest Comprehension and Letter-Number Sequencing should be included in a WISC-V administration and the child’s language abilities should be strongly considered.

Accuracy of the CART models was weak to moderate, with the greatest accuracy at the subtest level for children with ASD-LI, further supporting the administration of all primary and supplemental subtests for an ASD evaluation in schools and clinics.

Limitations and directions for future research

The current study was limited to the sample of children included in the standardization sample (ASD n = 62) of the WISC-V.

The prediction rates of the CART models may improve with a larger sample.

This study should be replicated with an independent sample of children with ASD as CART analysis appears useful for this population.

Children with other DSM-5 or educational categorizations should be included in a CART analysis to improve our understanding of the differences in cognitive functioning among children with ASD and other neurodevelopmental disorders.

Furthermore, children with IQs <60 were excluded from the study’s sample, limiting the ability to generalize these results to individuals with ASD who have low IQs.

Children capable of completing the WISC-V, but with IQs <60 should be included in future analysis to see if the CART results are similar if groups are divided up IQ rather than language impairment.

Future research should include various rating scales and assessment of academic skills in addition to intellectual functioning.

The current study was limited to only cognitive ability data and future research should examine the WISC-V profiles as well as adaptive skills, ASD symptomology, executive functioning, and communication skills.

Reading- conclusions

The current study was the first to examine the cognitive abilities of children with ASD on the WISC-V using a CART analysis.

Overall, the best classification accuracy was found in the model that included all subtests, and it most accurately identified children with ASD with a language impairment.

Children without accompanying language impairment were difficult to differentiate from the matched controls based on WISC-V index and subtest performance.

Although cognitive ability tests are not diagnostic, they determine the specific severity level and additional specifiers in ASD.

Proper conceptualization of the cognitive abilities of children with ASD will benefit clinicians as they can tailor intervention recommendations to the individual needs of the child.