752098
GCQXXX10.1177/0016986217752098Gifted Child QuarterlyCederberg et al.
research-article2018
Article
ASD Screening Measures for High-Ability
Youth With ASD: Examining the ASSQ
and SRS
Gifted Child Quarterly
2018, Vol. 62(2) 220–229
© 2018 National Association for
Gifted Children
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https://doi.org/10.1177/0016986217752098
DOI:
10.1177/0016986217752098
journals.sagepub.com/home/gcq
Charles D. Cederberg1, Lianne C. Gann1,
Megan Foley-Nicpon1 , and Zachary Sussman1
Abstract
High-ability youth diagnosed with autism spectrum disorder (ASD) historically have been neglected within samples validating
ASD screening measures, and consensus for what constitutes high ability has not been established. The Autism Spectrum
Screening Questionnaire (ASSQ) and Social Responsiveness Scale (SRS) are two common screening tools for ASD used
within research and practice settings. We investigated the accuracy of the ASSQ and SRS for ASD identification among a
sample of 23 high-ability youth previously diagnosed with ASD. Results suggest both ASSQ and SRS measures inconsistently
screened for ASD. The high-ability students with ASD scored significantly lower on the SRS total score and social cognition,
communication, and motivation subscales, suggesting potential phenotypic differences among high-ability youth with ASD that
could inform assessment and intervention strategies.
Keywords
ASSQ, SRS, twice-exceptional, intellectual giftedness, autism spectrum disorder
Children diagnosed with autism spectrum disorder (ASD)
possess marked social and communication difficulties
accompanied by a restricted pattern of repetitive and stereotyped behaviors (American Psychiatric Association [APA],
2013). Over the past 50 years, the process by which researchers, clinicians, and educators characterize and understand
ASD has radically evolved. Early autism epidemiology studies conducted in the 1960s and 1970s approximated 2 to 5
out of 10,000 individuals met criteria for the disorder
(Gillberg & Wing, 1999). Researchers during this era generally considered ASD as a condition accompanied by severe
intellectual disability (Gillberg & Wing, 1999). Today, we
know this condition is heterogeneously presented across
individuals, and has a strong genetic component with characteristics generally identified in various stages of early childhood (Kopp & Gillberg, 2011). ASD also is observed across
a wide range of cognitive abilities, from those identified with
coexisting intellectual disability to those who have ability
within the gifted range. Over the past several decades, rates
of ASD diagnoses have substantially increased among the
general population (Christensen et al., 2016). Most recent
estimates suggest an overall prevalence of ASD to be approximately 1 in 68 children, with an estimated half of which
possess intellectual abilities within the average to above
average range (Christensen et al., 2016). Despite these
diverging variants among ASD characteristics, there exists
support for autistic traits sharing a unitary underlying factor
structure that can be measured with appropriate tools
(Constantino et al., 2004).
Researchers have increasingly investigated the high-ability subset of children who meet criteria for both ASD and
intellectual giftedness (Assouline, Nicpon, & Doobay, 2009).
Gifted youth with ASD often show high intellect accompanied by intense interests, asynchronous development in abilities, inability to communicate and behave in a socially
conventional manner, and extreme rigidity for rules and
structure (Rubenstein, Schelling, Wilczynski, & Hooks,
2015). Other characteristics include deficits in social awareness, social reciprocity, emotional regulation, recognizing
the emotions of others, and sensory integration (Kuo, Liang,
Tseng, & Gau, 2014; Weinfeld, Barnes-Robinson, Jeweler, &
Roffman-Shevitz, 2006). Gifted youth with ASD may
endorse experiencing depression and social withdrawal
(Doobay, Foley-Nicpon, Ali, & Assouline, 2014); however,
they often show strengths in the areas of reading, mathematics, memorization of facts, and verbal fluency (Kuo et al.,
2014; Neihart, 2000; Weinfeld et al., 2006). These unique
1
University of Iowa, Iowa City, IA, USA
Corresponding Author:
Charles D. Cederberg, Department of Psychological and Quantitative
Foundations, The University of Iowa, 361 Lindquist Center, Iowa City, IA
52242-1529, USA.
Email: charles-cederberg@uiowa.edu
Cederberg et al.
characteristics commonly present challenges for parents and
educators to provide effective support and advocacy, develop
strengths, and address the individual areas of deficit for the
child (Dare & Nowicki, 2015; Rubenstein et al., 2015).
Within this population, the absence of a linguistic or intellectual impairment, or a complex behavioral presentation,
may also make identification more difficult (Assouline et al.,
2009; Neihart, 2000). There exists a need for valid and reliable screening tools for the range of autistic disorders across
intellectual abilities from the general population to clinical
settings (Ehlers, Gillberg, & Wing, 1999).
In 2013, the publication of the fifth edition of the
Diagnostic and Statistical Manual of Mental Disorders
(DSM-5; APA, 2013) consolidated the previous diagnoses of
autism disorder, pervasive developmental disorder–not otherwise specified (PDD-NOS), and Asperger’s Syndrome into
one spectrum (ASD), while noting one of three possible levels of severity. This consolidation may raise challenges for
mental health professionals to screen accurately across the
wide phenotypical range of the ASD population. For instance,
Foley-Nicpon, Fosenburg, Wurster, and Assouline (2017)
found the Autism Diagnostic Observation Schedule (ADOS;
Lord, Rutter, DiLavore, & Risi, 2002), a gold standard
research and clinical autism instrument used for diagnostic
purposes, was insufficient as a single tool for diagnosing
ASD among high-ability youth under DSM-5 criteria. These
results raise potential questions that other widely used ASD
tools, such as screening measures, may also share similar
weaknesses for positively identifying ASD among high-ability youth.
Screening Measures
The Social Responsiveness Scale (SRS; Constantino &
Gruber, 2005) is one commonly utilized parent-completed
screening tool for ASD characteristics in youth aged 4 to 18
years. The form consists of 65 items scored on a 4-point
Likert-type scale, yielding a total score and five subscale
scores: social awareness, social cognition, social communication, social motivation, and autistic mannerisms. In 2012,
the Social Responsiveness Scale–Second edition (SRS-2)
was released as the successor to the original SRS, which was
subsequently renamed the School-Age Form (Bruni, 2014).
The School-Age Form remained unchanged with this transition and continued to cover age ranges 4.0 to 18.0 years.
Furthermore, the SRS-2 is unaltered from the original SRS
within this specified 4.0 to 18.0 age range (Frazier et al.,
2014). The main addition to the SRS-2 provided extended
age ranges up through adulthood and down through preschool. These extended age groups benefit from the inclusion of separate versions using new forms for respective
preschool and adult age groups (Bruni, 2014; Frazier et al.,
2014). The SRS-2 also provides more detailed qualitative
descriptors in delineating regions of clinical T-scores by separating “mild” and “moderate” into two categorically distinct
221
regions (Constantino & Gruber, 2012). Clinical T-scores also
have been renormed. Therefore, while a successor to the SRS
might be widely available, the use of the SRS as a screening
instrument remains relevant and timely.
Most researchers examining the SRS have broadly
claimed to include high-functioning individuals with ASD
within respective study samples. However, the term high
functioning encompasses an inconsistent range of abilities
among research participants. This has resulted in loosely
defined parameters for establishing diagnostic accuracy of
the SRS. Despite these challenges, researchers using the SRS
within the literature generally have included samples of children diagnosed with ASD as possessing an IQ greater or
equal to 75 (e.g., Armstrong & Iarocci, 2013; Hus, Bishop,
Gotham, Huerta, & Lord, 2013). This might suggest much of
the research utilizing the SRS may not be applicable to children diagnosed with ASD who demonstrate intellectual giftedness with superior IQ scores (IQ > 120).
This lack of inclusion of high-ability participants within
samples of extant studies investigating SRS screening accuracy is not intended to call into question the measure’s utility
for screening ASD among the general population. There is
support for the SRS being strongly correlated with the ADOS
(Lord et al., 2002) and the Childhood Autism Rating Scale
(Reszka, Boyd, McBee, Hume, & Odom, 2014), for example. However, every measure possesses its own unique set of
limitations. For instance, the SRS has been problematic for
differential diagnostic purposes. Researchers have suggested
the measure occasionally provides inaccurate diagnosis,
such as mislabeling those with anxiety disorders as having
ASD (Cholemkery, Mojica, Rohrmann, Gensthaler, &
Freitag, 2014). Furthermore, some research suggests the SRS
may overidentify children with ASD and that subscale measure scores should be interpreted with caution (Aldridge,
Gibbs, Schmidhofer, & Williams, 2012). Additionally, there
may be disagreement with new DSM-5 symptom criteria and
the SRS, especially for children (Reszka et al., 2014).
The Autism Spectrum Screening Questionnaire (ASSQ;
Ehlers et al., 1999) is another popular screening instrument
found to be reliable and valid in identifying a three-factored
structure of ASD characteristics in school-aged children
(Ryland, Hysing, Posserud, Gillberg, & Lundervold, 2012).
The ASSQ is purported to be valuable particularly in singling
out children in need of further assessment for an ASD diagnosis (Posserud, Lundervold, & Gillberg, 2006). Results
from a factor analysis suggest that ASSQ items diverge into
a three-factor model: social difficulties, tics/motor/obsessive–compulsive disorder, and autistic style (Posserud,
Lundervold, & Gillberg, 2009).
Similar to the SRS, the ASSQ is intended to screen for
ASD among high-functioning youth. However, researchers
who use the ASSQ have inconsistently established lower
limit IQ cutoff scores across study samples. Posserud et al.
(2009) found the ASSQ to be effective in screening ASD in
typical and low-functioning children, though no official
222
cutoff IQ score was reported outside of generalizing the
study’s findings to children with average intelligence to mild
intellectual disability (IQ > 50). Mattila et al. (2009) also
employed the IQ > 50 cutoff score in labeling “high-functioning” children, and recommended the questionnaire be
used with children of average intelligence or mild intellectual disability. Ehlers et al. (1999) included children with
mild intellectual disability within their high-functioning
sample due to observed similarities in presentation between
these children and children with higher IQs, although no cutoff score was reported. In a recent large validation study of
the ASSQ for both clinical settings and the general population in Finland, Mattila et al. (2012) established score tiers of
risk for ASD for children aged 7 to 12 years. In balancing
specificity and selectivity, the authors established combined
teacher and parent rating scores of 30 to meet criteria for
medium risk of ASD diagnosis. However, while Mattila et al.
(2012) were able to establish a cutoff of 22 in clinical settings for teacher ratings, they were unable to determine an
optimal cutoff for parent ASSQ ratings alone.
A further concern regards the lack of ASSQ sensitivity for
identifying girls who meet criteria for ASD (Posserud et al.,
2009). Specifically, Posserud et al. (2009) found that parent
and teacher ASSQ scores were particularly discrepant for
girls in a large sample of students. Kopp and Gillberg (2011)
later revised the ASSQ to accommodate for this discrepancy
with the aim of increased sensitivity to the presentation of
ASD characteristics within female child populations. These
modifications namely included the addition of 18 items conceptualized to better identify girls with ASD. Revisions also
identified a flexible cutoff score of IQ greater or equal to 70.
Consistently in the literature, the ASSQ is found to be useful
in screening for ASD characteristics with a wide range of
intellectual abilities; however, little to no research has investigated its sensitivity and utility for screening ASD in intellectually gifted children with IQ > 120. Additionally, the
broad cognitive diversity commonly used in ASSQ study
samples may hinder the identification of individuals with
higher cognitive functioning.
The current study examines the use of the SRS and ASSQ
among a sample of high-ability children with preexisting
diagnoses of DSM-IV-TR (APA, 2000) developmental disorders now subsumed under the ASD umbrella in the DSM-5
(APA, 2013). We are specifically interested in learning how
high-ability students diagnosed with ASD phenotypically
compare on SRS and ASSQ measures with non-high-ability
ASD clinical samples. The following research questions
were asked:
Research Question 1: How do the SRS and ASSQ compare in confirming diagnoses of ASD among previously
diagnosed high-ability students with ASD?
Research Question 2: How do SRS scores among the
high-ability sample compare with scores from a clinical
sample of non-high-ability children with ASD?
Gifted Child Quarterly 62(2)
Method
Measures
The SRS (Constantino & Gruber, 2005) is a quantitative
measure consisting of 65 items scored on a 4-point Likerttype scale using a “0” (not true) to “3” (almost always true).
Questions can be completed in 15 to 20 minutes and focus on
the child’s ability to engage in emotionally appropriate,
reciprocal social interactions during the past 6 months. It is a
parent- and/or teacher-report measure that assesses the
child’s capacity to engage in reciprocal social behavior,
interpret the emotional and interpersonal cues of others,
accurately interpret these cues, appropriately respond to
social cue interpretations, and possess motivation to seek out
social interactions with others (Constantino et al., 2003). The
measure provides a total score and five subscale scores,
including social awareness (e.g., understanding personal
boundaries of others or invading someone’s space), social
cognition (e.g., focusing disproportionately on “parts” of
things rather than “seeing the whole picture), social communication (e.g., demonstrating rigid or inflexible patterns of
behavior), social motivation (e.g., seeking out social interactions on one’s own volition), and autistic mannerisms (e.g.,
demonstrates odd, repetitive behaviors, such as rocking or
hand flapping). These raw scores are converted into T-scores
that in turn provide confidence indexes for different diagnostic categories (e.g., Asperger’s Syndrome, autistic disorder,
and PDD-NOS). The SRS authors report an overall clinical
internal consistency of 0.97 and a parent rating Cronbach’s
alpha score of .945 (Constantino & Gruber, 2005).
The ASSQ (Ehlers et al., 1999) can be completed in
approximately 10 minutes, and screens for possible ASD
characteristics in children and adolescent populations possessing higher intellectual functioning. The ASSQ is a parent- and/or teacher-report form consisting of 27 items that
requires the reporter to indicate for each item if “the rated
child stands out as different from other children of his or her
age.” A 3-point scale is used that ranges from No (Score 0
indicating normal), Somewhat (Score 1 indicating some
abnormality), or Yes (Score 2 indicating definite abnormality; Mattila et al., 2012). The ASSQ scores ranges from 0 to
54, and include cutoff scores of 19 for parent reporters and
22 for teacher reporters to identify the likely presence of
high-functioning ASD in clinical settings. The ASSQ is
designed to measure three factors, including “social difficulties” (e.g., difficulties forming friendships, engaging in prosocial behaviors, and social communication), “repetitive,
stereotyped behavior, and autism-associated problems” (e.g.,
motor difficulties, tics), and “autistic style” (e.g., social–cognitive speaking and mannerisms often encountered in highfunctioning individuals with ASD).
Research has shown the ASSQ to be an accurate screening measure, with good reliability, validity, and internal consistency (Posserud et al., 2009) for individuals with low to
average intelligence (Ehlers et al., 1999) across clinical
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Cederberg et al.
settings and the general population (Posserud et al., 2009).
Ehlers et al. (1999) reported high test–retest reliability (r =
.94 for parents, r = .96 for teachers). Interrater reliability
between parents and teachers was .66. Divergent validity
between parent ratings on the ASSQ and the Rutters and
10-item Conners scales, two widely used measures for
assessing behavioral disorders, were .75 to .58, respectively
(Ehlers et al., 1999). While the authors conceded that the
agreement between the ASSQ and Rutters scales was somewhat high, they attributed this to the relatively broad range of
behavioral characteristics captured by the Rutters scale,
some of which reflect features of ASD.
Table 1. Means and Standard Deviations for Study Sample
Participants’ Ability Scores.
Index
Mean
Standard deviation
Full-scale
General ability index
Verbal comprehension index
Perceptual reasoning index
140.00
138.33
123.15
122.58
6.14
10.02
15.39
13.59
Participants included a total of 23 parents whose children
and adolescents were identified with intellectual giftedness
(IQ score of 120 [93rd percentile] or above) and met diagnostic criteria for an ASD. Age ranges of child and adolescent participants were 4.0 years to 17.11 years, with a mean
age of 11.93 years and standard deviation of 3.43 years.
6.1 to 16.0 years, and Wechsler Adult Intelligence Scale–
Fourth edition (Wechsler, 2008) for adolescents older than
16.0 years, of the sample. ASD diagnosis was determined
through administration of a comprehensive evaluation,
including the ADOS (Lord et al., 2002), the Autism
Diagnostic Interview–Revised (ADI-R; Rutter, Le Couteur,
& Lord, 2003), and the Vineland Adaptive Behavior
Scales–Second edition (Sparrow, Cicchetti, & Balla, 2005).
Diagnoses were made by one of two licensed psychologists
who had received research training in administering the
ADOS and ADI-R.
Procedures
Statistical Analysis
Participants were recruited from two separate avenues. First, a
grant-funded study provided the opportunity for psychologists
with requisite clinical training to assess students with highintellectual ability accompanied by coexisting ASD diagnoses.
Second, additional students were recruited through a
Midwestern university-based child and adolescent psychology
clinic. These student diagnostic profiles were consistent with
high ability and an ASD diagnosis. Parents were initially contacted via e-mail with information regarding the study.
Interested participants were then mailed a packet with a cover
letter explaining the purpose of the study and instructions for
completing the assessments (order of completion was not
specified), consent and assent forms, the SRS and ASSQ, and
a return envelope. Individuals were asked to return all documentation (informed consent documents and study materials)
in the supplied envelope. If participants did not return the
envelope in 4 weeks, a reminder e-mail was sent. If a participant returned a packet with an incomplete informed consent
document, he or she was mailed the informed consent document a second time with a brief note indicating where a signature was required. If someone returned the informed consent
document, but not the study materials, he or she was sent a
brief e-mail reminder to return the study materials when
completed.
Intellectual ability for giftedness was determined
through administering Wechsler intelligence testing. Table
1 provides the means and standard deviations of the age
appropriate Wechsler test, Wechsler Preschool and Primary
Scale of Intelligence–Third edition (Wechsler, 2002) for
children up to age 6.0 years; Wechsler Intelligence Scale
for Children–Fourth edition (Wechsler, 2004) for children
We ran a cross-tabulation analysis for both ASSQ and SRS
measures with predetermined cutoff scores of 19 and 85, respectively. This analysis served to delineate which previously diagnosed ASD participant scores met inclusion for ASD based on
performance of the ASSQ or the SRS. Finally, two-tailed, one
sample independent t tests were used to compare mean total
scores and subscale (i.e., social awareness, social cognition,
social communication, social motivation, and autistic mannerisms) scores from the high-ability sample with the SRS sample
total and subscale scores provided by Cholemkery et al. (2014).
That is, a two-tailed, one sample independent t test was conducted to determine whether differences exist between the
gifted and typical ASD clinical samples for the total raw score
and each of the five raw subscale scores. Significance level was
set at α = .05 (uncorrected).
Participants
Results
We compared the ASSQ and SRS as tools in confirming
diagnoses of ASD among previously diagnosed high-ability
youth with ASD. We used cross-tabulation to examine the
effectiveness of confirming ASD diagnosis with each measure. Additionally, participant ASSQ and SRS scores were
entered in a cross-tabulation analysis after transforming the
raw scores from the ASSQ and SRS into categorical data
(i.e., diagnosis). The authors utilized a cutoff score of 19 for
the ASSQ, so positive cases of ASD were defined as a score
of 19 or higher. This cutoff score was determined by a sensitivity and specificity receiver operating characteristic curve
analysis conducted by Ehlers et al. (1999). Ehlers et al.
(1999) established this optimal cutoff score differentiating
224
Gifted Child Quarterly 62(2)
Table 2. ASSQ and SRS Diagnosis Cross-Tabulation Analysis.
SRS
ASSQ No dx
Count
% Within
ASSQ Yes dx
Count
% Within
Total
Count
% Within
No dx
Yes dx
Total
8
100
0
0
8
100
5
33.3
10
66.7
15
100
13
56.5
10
43.5
23
100
Note. ASSQ = Autism Spectrum Screening Questionnaire; SRS = Social
Responsiveness Scale.
ASD cases from other cases to be 19 for parent responders,
yielding an 82% correct identification of Asperger syndrome
in a validation sample. The authors utilized a cutoff score of
85 for the SRS based on the clinical cutoff recommendation
in the SRS and SRS-2 manuals (Constantino & Gruber,
2005, 2012). The ASSQ and SRS scores positively agreed
with ASD diagnosis across 10 total cases (43.5% of the total
sample). Individually, the ASSQ identified more positive
ASD diagnoses (n = 15) than the SRS (n = 10; see Table 2).
Furthermore, no SRS scores were found to positively identify ASD among participants who scored below the established ASSQ cutoff score. While both tests were associated
with each other (K = .582; p = .002), we suggest these findings indicate that the ASSQ may possess higher sensitivity
for correctly identifying ASD among high-ability youth as a
single-screening measure. Conversely, the SRS appears to be
more restrictive in positively identifying ASD among highability cases. Considering all cases were previously diagnosed with ASD by a licensed psychologist (n = 23), the
ASSQ and SRS might also be limited in their ability to screen
for ASD among high-ability individuals when used in
tandem.
In our second research question, we investigated how
SRS scores among the high-ability sample compared with
scores from a clinical sample of non-high-ability children
with ASD. We conducted one sample t tests of SRS subscale
mean scores in comparison with respective subscale mean
scores from the ASD clinical sample provided in Cholemkery
et al. (2014; see Table 3). The Cholemkery et al.’s study is to
our knowledge the only recently published article that provided descriptive statistics for subscale SRS scores and total
raw scores for a clinical ASD sample; no subscale-level
scores were provided by Constantino and Gruber (2005). We
also verified average intellectual Full Scale Intelligence
Quotient ability in the Cholemkery et al. study to ensure it
was composed of an ASD nongifted clinical sample (M =
102.15; SD = 16.23). We observed significantly lower scores
within our high-ability sample on the cognition (p = .046; d
= .446; 95% confidence interval [CI: −5.641, −0.49]), communication (p = .016; d = .556; 95% CI [−9.813, −1.13]), and
motivation (p = .001; d = .785; 95% CI [−6.546, −1.954])
SRS subscales. No significant differences were found
between the high ability and Cholemkery et al. groups for the
awareness (p = .287; d = .221; 95% CI [−2.093, 0.651]) and
autistic mannerisms (p = .795; d = .059; 95% CI [−2.737,
3.532]) subscales. Additionally, the total raw score for the
high-ability sample was significantly lower than the
Cholemkery et al. total raw score (p = .033; d = .484; 95% CI
[−24.612, −1.17]).
Discussion
In the current study, we examined the use of two ASD screening tools among high-ability populations suspected of ASD.
Specifically, we examined whether the ASSQ and SRS confirmed an ASD diagnosis among 23 high-ability youth previously diagnosed with ASD. We then investigated how the
SRS measure scores among a high-ability sample compare
with scores from a clinical sample of non-high-ability children with ASD. The goal was to determine the adequacy of
each screening measure and explore potential presentation
differences for clinicians and researchers to consider when
assessing for ASD in high-ability individuals. While these
findings are preliminary, the results yielded a number of
interesting findings that warrant consideration of the ASSQ
and SRS screening measures’ utility in accurately identifying
high-ability youth with ASD.
A licensed psychologist using gold standard ASD identification instruments prior to involvement in the study diagnosed all participants with ASD. Despite the utility of the
SRS and ASSQ as screeners, only a small percentage of
cases in the current sample were identified by both measures.
In comparing the SRS and ASSQ, the cross-tabulation analysis demonstrated that the ASSQ possessed a high degree of
sensitivity for positively identifying ASD. In contrast, the
SRS was more restrictive in identifying ASD compared to
the ASSQ. Therefore, it could be suggested that the ASSQ
was more fitting to identify ASD in a high-ability population
because it had a lower threshold and identified more potential cases of ASD. Despite the ASSQ’s lower threshold, both
measures yielded inconsistencies as screening measures as
evidenced by the ASSQ and SRS positively identifying ASD
in only 15 and 10 cases out of the total 23 participants,
respectively. Moreover, only 43.5% of the cases in the current sample were positively identified with ASD by both
screening measures. This presents a challenge because the
purpose of screeners is to balance a variety of costs and benefits for specificity and sensitivity in hopes of casting a wide
net for potential symptomology (e.g., Campbell, 2005;
Mattila et al., 2012). For example, the authors of the SRS
screener describe its purpose as to identify subthreshold abilities and deficits in social reciprocity that may be indicative
of a pervasive development disorder (Constantino & Gruber,
2005). However, if the cutoff score for screeners such as the
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Cederberg et al.
Table 3. One Sample t Test SRS Total and Subscale Scores Between ASD Gifted and Cholemkery et al. (2014) Normal Cognitive
Ability ASD Groups.
Total score
Social awareness
Social cognition
Social communication
Social motivation
Autistic mannerisms
SRS 2E (n = 23)
Cholemkery et al. (2014); ASD (n = 60)
M (SD)
M (SD)
85.74 (27.1)
11.61 (3.17)
15.43 (6.47)
28.48 (10.04)
13.00 (5.31)
17.22 (7.25)
98.63 (26.19)
12.33 (3.34)
18.28 (6.32)
33.95 (9.64)
17.25 (5.51)
16.82 (6.20)
p
.033*
.287
.046*
.016*
.001*
.795
Note. SRS = Social Responsiveness Scale; ASD = autism spectrum disorder.
*Significant at the .05 level.
SRS are too high, then potential cases of ASD may be missed
among high-ability students. In our study, 43.5% (n = 10) of
the sample scored above the recommended SRS total raw
score cutoff in clinical settings (T = 85; Constantino &
Gruber, 2005; 2012). Take as a whole, these findings suggest
that the overall level of symptomology and/or presence of
ASD diagnostic characteristics found in typical clinical samples may be more prominent than among high-ability samples. The cross tabs are a reflection of the restrictiveness of
the SRS to accurately identify ASD within the high-ability
sample, as 13 (56.5%) of the participants did not meet the
recommended SRS clinical cutoff threshold score of 85.
Our second objective of this study was to explore how the
SRS measure scores among a high-ability sample compared
with scores from a clinical sample of non-high-ability children with ASD. Specifically, SRS scores among the highability sample were compared with scores from a clinical
sample of non-high-ability children with ASD from
Cholemkery et al. (2014). The total raw SRS score of the
high-ability ASD sample was significantly lower than the
sample from the Cholemkery et al. (2014) study. Although
preliminary and from a small sample, these results may be
supportive of the “masking effect” (Assouline, Nicpon, &
Whiteman, 2010), which suggests high ability may sometimes “mask” symptomology. It also may be ASD characteristics are less severe in high-ability populations, consistent
with Level 1 in the DSM-5 (APA, 2013).
Analysis of the SRS subscale differences between the
high-ability sample and comparison sample further elucidates these differences. The SRS social awareness and social
cognition subscales measure the capacity to accurately recognize, understand, and react to social cues and engage in
reciprocal social interactions (Constantino & Gruber, 2005).
Each subscale focuses on reciprocal social behavior, with the
social awareness subscale focusing on the sensory aspects,
and the social cognition subscale relating to the cognitive–
interpretive aspects of the diagnosis. In the current study,
there were no significant differences in social awareness
between groups, suggesting the high-ability sample possessed similar deficits in social pragmatics and perspective
taking. The significantly lower scores obtained among the
high-ability sample on the social cognition subscale, which
captures the capacity to interpret social cues and reciprocal
social behavior, might be related to their higher intellectual
abilities. That is, the youths’ enhanced ability to learn from
feedback related to social skills might instill a greater capacity for interpreting social cues, cognitive flexibility, and
adapting to norms related to social behavior. While these
social deficits might still be apparent compared with youth
without ASD, the results suggest they might be less so compared with average ability populations with ASD.
Among the high-ability sample, social communication
and social motivation subscale scores were also significantly
lower than those reported in Cholemkery et al. (2014). The
social communication subscale measures the expressive
components of reciprocal social behavior, such as engaging
in appropriate turn-taking in conversations and understanding personal space boundaries. Social motivation pertains to
the extent that one is motivated to engage in interpersonal
interactions, and comprises elements of social anxiety and
empathy (Constantino & Gruber, 2005). These significantly
lower scores observed on these subscales further supports the
compensatory hypothesis. Social communication skills often
can be taught or learned from a variety of interventions and
naturally occurring social contexts, such as ecological variations, collateral skills interventions, child-specific interventions, and observed peer behavior (McConnell, 2002).
Likewise, intellectual giftedness might facilitate more efficient and expeditious acquisition of social communication
and social cognitive skills, and explain a heightened motivation for engaging in social interactions and initiating meaningful connections with peers. In sum, intellectual ability
may increase one’s capacity to learn from social communication opportunities and compensate for one’s inherent social
deficits. However, these hypotheses are extremely tentative
given the small sample size, lack of a true comparison group,
226
and methodological limitations that will be described in the
next section.
It is notable there were no mean group differences between
the groups on the autistic mannerisms subscale, which measures engagement in stereotypical behaviors or highly
restrictive interests. Although the phenotypical pattern of
symptom manifestation may be unique among high-ability
youth (excessive interest in unusual or specific topics as the
most frequently observed; Foley-Nicpon et al., 2017), this
result suggests the overall presence of symptoms within this
umbrella may be similar, regardless of cognitive ability. This
finding is generally aligned to the hypotheses suggesting the
existence of differential restricted and repetitive behavior
subgroups in children with ASD (Bishop et al., 2013). Motor
mannerisms, sensory seeking behaviors, and repetitive use of
objects might be separate from an insistence on sameness.
An insistence on sameness refers to cognitively rigid patterns of behavior marked by challenges with changes in routine and overreliance on rituals. Together, restricted repetitive
behaviors and insistence on sameness might underpin the
biological factors of ASD (Bishop et al., 2013; Neihart,
2000).
One final component in considering these findings pertains to the intersection of giftedness, social development,
and psychological adjustment. Generally, research suggests
high-ability children possess greater confidence and overall
higher self-concept than nongifted peers (Assouline &
Colangelo, 2006). This follows the idea that success achieved
in academic domains might be interconnected with social
and emotional development (Foley-Nicpon, 2015). Building
a capacity for perseverance and resiliency might be an important factor at work for some high-ability youth with ASD,
and developing individual strengths might compensate for
social weaknesses through trial and error learning that ultimately leads to effective social problem-solving strategies
(Willard-Holt, Weber, Morrison, & Horgan, 2013). It is
unclear if these processes were at play within our study,
though our findings raise compelling possible avenues for
future research.
Relevance to the SRS-2
At the time of the study proposal and data collection, the
SRS-2 had not been published. As previously described, the
SRS-2 expanded the age range of the instrument into three
distinct Preschool, School-Age, and Adult Forms that are
used to monitor social impairment across the life span.
Despite the addition of these separate Preschool and Adult
Forms, the original SRS validated for youth aged 4 to 18
years was simply retermed as the School-Age Form. The
65-item rating scale of the SRS School-Age Form remains
unchanged from the original SRS. However, the new SRS-2
manual provides updated norms and validation data for use
in clinical and school settings. Therefore, in order to boost
the utility of this study for practitioners working in these
Gifted Child Quarterly 62(2)
settings, we retroactively explored the possible implications
to our findings using the renormed clinical T-scores of the
SRS-2 School-Age Form. It is important to note that the
SRS-2 manual clearly advises using SRS raw scores (i.e.,
possible scores varied from 1 to 195, based on 0- to 3-point
Likert-type scale weighting for each of the 65 SRS test question responses; Constantino & Gruber, 2005, p. 14) for
research purposes in clinical populations (Constantino &
Gruber, 2012, p. 17). However, investigating clinical T-score
results may inform the decision-making process of clinicians
and educators who use this instrument in real-world
settings.
Using original SRS T-scores, we found 12 participants
fell in the “severe” range, 7 fell in the “mild to moderate”
range, and 4 fell in the “within normal limits” range. Using
SRS-2 renormed T-scores and the more detailed qualitative
descriptors, 10 participants fell in the “severe” range, 7 fell
in the “moderate” range, 2 fell in the “mild” range, and 4
were identified as “within normal limits.” Therefore, two
participants moved from “severe” to “moderate” using
renormed SRS-2 T-scores, while no new participants fell
“within normal limits.” The SRS-2 manual outlines detailed
interpretative text for each of the different SRS T-score
ranges. Those scores falling within the “severe” range are
highly associated with a clinical diagnosis of ASD, while
scores within the “moderate” range are common among children with ASD who demonstrate substantial deficits with
everyday social interactions (Constantino & Gruber, 2012).
These represented 17 of the 23 participants in our study.
Moreover, scores falling in the “mild” range warrant careful
clinical judgment, as these scores are also commonly
observed across a variety of non-ASD diagnoses such as
attention-deficit/hyperactivity disorder, obsessive–compulsive disorder, anxiety disorders, conduct disorders, and the
new DSM-5 social communication disorder. Considering all
23 of our participants began the study with a diagnosis of
ASD, this wide diversity of observed SRS-2 scores strengthens our findings that a high degree of care is needed among
clinicians and educators when recommending ASD evaluations to high-ability children. The variety of T-scores also
supports previous findings indicating ASD manifests distinctively among high-ability youth with ASD (Doobay et al.,
2014; Foley-Nicpon et al., 2010; Foley-Nicpon et al., 2017;
Neihart, 2000).
These findings are not meant to be an indictment of the
SRS-2 as a tool for screening ASD among high-ability youth.
The SRS-2 is specifically designed to assess for autistic
symptoms rapidly and reliably within a proper context, and it
also explicitly avoids “all or nothing” categorical diagnoses
(Constantino & Gruber, 2012). However, because ASD in
high-ability youth is so widely misunderstood among educators and the lay public (e.g., Dare & Nowicki, 2015;
Rubenstein et al., 2015), interpreting SRS-2 scores within
the context of a child’s ability is especially important. The
addition of social communication disorder in the DSM-5
227
Cederberg et al.
further complicates how practitioners in school and clinical
settings interpret scores from widely established ASD instruments (Foley-Nicpon et al., 2017), including the SRS-2. Had
our SRS-2 data been used to recommend comprehensive
ASD evaluations in the real world, it is quite possible 6 of
our 23 participants with ASD may never have received further ASD diagnostic services.
authors’ knowledge, the ASSQ is not normed (Campbell,
2005). This prohibits the availability of floor, ceiling, and
item gradient information. Despite these limitations, the
results of the study provide initial insight into the unique
symptom presentation of high-ability children with ASD
and the utility of ASD screeners with this specific
population.
Limitations
Conclusion
There are several limitations to this study that warrant
thoughtful discourse. First, the absence of a comparison
group consisting of children of average cognitive abilities
with ASD, or high-ability children without ASD, limited the
opportunity for alternative methodological approaches.
Second, participants in the current study were diagnosed
with ASD based on DSM-IV-TR criteria for autistic disorder,
Asperger syndrome, or PDD-NOS. It is not certain how
many of the participants would meet criteria for ASD under
the current DSM-5 diagnostic criteria, though Frazier et al.
(2012) found support for the validity of DSM-5 criteria for
ASD and for the continued utility of the SRS-2 as a screening
measure. Frazier et al. (2012) identified a two-factor model
composed of autistic mannerisms and social communication
that later informed the DSM-5 taxonomy. Additionally,
though specificity of the DSM-5 was found to be higher for
ASD diagnosis, sensitivity was lower when compared with
DSM-IV-TR criteria.
A third limitation is that comparison data derived from
the selected Cholemkery et al. (2013) study was not normed
and possesses its own set of methodological issues. This
study was conducted with a sample from Germany, so crosscultural differences in scores might confound results. The
choice to use the Cholemkery et al. (2013) article was due to
the unavailability of subscale clinical means for ASD samples within the SRS and SRS-2 manuals (Constantino &
Gruber, 2005). Therefore, we could not draw on such data
for the purposes of comparative analysis. Fourth, this study
possesses a small sample size and thus generalizability is
hindered. However, sample size problems in research are
commonly encountered issues among low incidence populations, such as high-ability children with ASD. Considering
the small sample size and challenges with statistical power,
utilizing a t test implies a high probability of committing
false positive Type II error (e.g., Cohen, 1970). Fifth, the
participant age range of 4 to 18 years was large, but this
range is within the range recommended for use of the SRS
and the SRS-2 School-Age Form instruments (Constantino
& Gruber, 2012). Sixth, there are many different measures
available to screen for ASD, and the SRS and ASSQ may be
less effective in identifying high-ability students with ASD
than other existing measures that were not included within
the scope of this study. However, the SRS and ASSQ were
chosen because both measures claim to screen for ASD in
high-functioning individuals (>70 IQ). Finally, to the
High-ability youth with ASD may present differently than
ASD youth without high ability, yet the majority of the measures used to screen ASD populations are created with samples of individuals with low average to average intelligence.
This presents problems when considering the unique social
challenges encountered by high-ability youth with ASD.
Future screening instruments should consider the social
communication, social motivation, and social cognition
characteristics that potentially distinguish high-ability children with ASD from other groups of youth with ASD.
Screening instruments should be developed that accurately
identify and meaningfully capture these phenotypic differences to avoid missed diagnosis among high-ability children
with ASD.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
ORCID iD
Megan Foley-Nicpon
https://orcid.org/0000-0002-8677-3578
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Author Biographies
Charles D. Cederberg received his MA in mental health counseling from Boston College and is currently completing a PhD in
counseling psychology in the Department of Psychological and
Quantitative Foundations at The University of Iowa. His areas of
professional interest include psychosocial well-being and talent
development of high-ability youth and twice-exceptional youth;
social class and classism; materialism, greed, and mental health;
and personal branding in professional psychology.
Lianne C. Gann received her MA in clinical mental health counseling and is currently a doctoral candidate in counseling psychology at The University of Iowa. Her areas of professional interest
include eating disorders, social media’s impact on eating and health
behaviors, and body image.
Megan Foley-Nicpon is an associate professor of counseling psychology and associate director for research and clinic at the Belin-Blank
Center for Gifted Education and Talent Development at The University
of Iowa. She is a licensed psychologist whose research and clinical
interests include assessment and intervention with high-ability students
with disabilities, and the social and emotional development of talented
and diverse students.
Zachary Sussman is a pediatric neuropsychologist and licensed
psychologist in private practice in Boulder, Colorado. He obtained
his Ph.D. in counseilng psychology from the University of Iowa.