Revisiting cognitive and adaptive functioning in children and adolescents with autism spectrum disorder.

J Autism Dev Disord (2015) 45:138–156 DOI 10.1007/s10803-014-2200-0

ORIGINAL PAPER

Revisiting Cognitive and Adaptive Functioning in Children and Adolescents with Autism Spectrum Disorder Nicole L. Matthews • Elena Pollard • Sharman Ober-Reynolds • Janet Kirwan Amanda Malligo • Christopher J. Smith

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Published online: 13 August 2014 Ó Springer Science+Business Media New York 2014

Abstract Profiles of performance on the Stanford Binet Intelligence Scales (SB5) and Vineland Adaptive Behavior Scales (VABS) were examined in 73 children and adolescents with autism spectrum disorder. SB5 cognitive profiles were observed to be similar between participants with and without early language delay, but different between participants with and without intellectual disability. With few exceptions, the distribution and cognitive profiles of participants with specific nonverbal IQ–verbal IQ and abbreviated IQ–full scale IQ discrepancy patterns paralleled previous reports. A cognitive functioning advantage over adaptive functioning was observed to be strongest in participants without intellectual disability and older participants. The previously reported VABS ‘‘autism profile’’ was not observed. Current findings clarify previous research and will inform the diagnostic process and treatment planning. Keywords Autism spectrum disorder Cognitive functioning Intelligence Adaptive functioning Stanford-Binet Vineland Adaptive Behavior Scales

Introduction The diagnosis of autism spectrum disorder (ASD) is a multifaceted and complex process; as is the assessment of functioning throughout the lifespan, which is necessary to provide appropriate supports for individuals with the disorder. These processes often include the consideration of

N. L. Matthews (&) E. Pollard S. Ober-Reynolds J. Kirwan A. Malligo C. J. Smith Southwest Autism Research and Resource Center, 300 N. 18th Street, Phoenix, AZ 85006, USA e-mail: [email protected]

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cognitive and adaptive functioning (i.e., the age-related skills necessary for independent living; Filipek et al. 1999; Volkmar et al. 2014). Although commonly used in research and clinical practice, performance of individuals with ASD on the Stanford Binet Intelligence Scales, Fifth Edition (SB5; Roid 2003) has been understudied. Further clarification of adaptive functioning as it relates to intellectual ability and age is also necessary. The current study examined profiles (i.e., relative performance across subtests/domains) of cognitive and adaptive functioning on the SB5 and Vineland Adaptive Behavior Scales (VABS; Sparrow et al. 1984), respectively, among a well-characterized sample of children and adolescents with ASD. Specifically, SB5 profiles were compared between subgroups defined by language onset and intellectual ability. SB5 abbreviated IQ and full scale IQ scores were also compared. Last, VABS profiles were examined by intellectual ability and age. It was previously believed that the majority of individuals with ASD had an intellectual disability (ID; i.e., IQ \ 70), but more recent estimates indicate that about half of ASD cases present with IQs of 70 or above (Chakrabarti and Fombonne 2005; Charman et al. 2011). Additionally, a recent epidemiological report indicated that more than a quarter of participants with ASD have average or above average IQs (i.e., IQ [ 85) and\20 % have moderate to severe ID (i.e., IQ \ 50; Charman et al. 2011). Because individuals with ASD can present with a range of cognitive abilities, accurate assessment is important for the purposes of treatment planning. Guidelines for the screening and diagnosis of ASD also indicate the use of cognitive assessments during differential diagnosis and assessment of developmental profiles (Volkmar et al. 2014). The SB5 is a standardized measure of cognitive functioning that is commonly used in research and clinical

J Autism Dev Disord (2015) 45:138–156

settings. It includes ten subtests, five nonverbal and five verbal, which are used to calculate nonverbal IQ (NVIQ), verbal IQ (VIQ), and full scale IQ (FSIQ). Two subtests can be administered in isolation to generate an abbreviated IQ (ABIQ) when time, participant characteristics, and/or other factors do not allow for the administration of the full assessment. The SB5 technical manual presents brief descriptive statistics regarding performance of a subsample of 83 children with ASD (ages 2–17 years) included in the validation sample (Roid 2003, p. 98). Since the publication of the SB5 more than a decade ago, only two other published reports have specifically examined performance profiles of this population (Coolican et al. 2008; Lennen et al. 2010). A study conducted by Coolican et al. (2008) revealed areas of strengths and weaknesses for the ASD sample as a whole (n = 63; 3–16 years of age), considerable variability in cognitive functioning, and a group-level NVIQ advantage. Surpassing other recent estimates, but in line with the most recent report from the Centers for Disease Control and Prevention (CDC; U.S. Department of Health and Human Services 2014), 65 % of the sample had full scale IQs of 70 or above. Average NVIQ was observed to be significantly higher than average VIQ for the full sample. However, examination of individual performance revealed that only 43 % of the full sample had NVIQs that were significantly higher (i.e., a discrepancy of at least 9 or 10 points, depending on participant age; see Roid 2003) than their VIQs. This pattern did not differentiate diagnostic subgroups (37.8, 50, and 45.5 % in Autistic Disorder (AD), Asperger’s syndrome (AS), and Pervasive Developmental Disorder-Not Otherwise Specified (PDD-NOS), respectively). The majority of the sample demonstrated no difference between NVIQ and VIQ; very few children (n = 5) had VIQs that were significantly higher than their NVIQs. These results provide some support for the previously observed nonverbal advantage in children with ASD (Charman et al. 2011; Lincoln et al. 1995; Mayes and Calhoun 2003). However, there are contradictory findings in the literature from studies that did not find a nonverbal advantage (Lennen et al. 2010; Siegel et al. 1996). Notably, Siegel and colleagues used different tests of cognitive abilities, the WISC-R and the WAIS-R. Profiles on the ten SB5 subtests were strikingly similar among diagnostic subgroups in the Coolican et al. (2008) sample. All subgroups demonstrated relative strengths in nonverbal fluid reasoning and nonverbal and verbal quantitative reasoning when compared to the other seven subtests. It was observed that 23.8 % of the full sample had ABIQ scores that significantly overestimated their FSIQ scores, whereas 3.2 % had ABIQs that significantly underestimated their FSIQ scores. An examination of cognitive profiles by FSIQ–ABIQ discrepancy direction

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indicated that relatively strong performance on the nonverbal fluid reasoning subtest, which is one of two subtests used to calculate ABIQ, was likely responsible for significantly higher ABIQ scores. The authors cautioned against the sole use of SB5 ABIQ in children and adolescents with ASD, as it may overestimate their true abilities; however, replication of this finding is warranted. A second detailed examination of SB5 performance in ASD (n = 31, 3–15 years of age) was conducted by Lennen et al. (2010). This study compared ASD with and without ID; it did not examine ABIQ–FSIQ discrepancies. Unlike the findings reported by Coolican et al. (2008), there was not a significant NVIQ advantage among children with ASD with or without ID. Notably, NVIQ scores were higher than VIQ scores in each group; the differences may have failed to reach statistical significance due to small sample sizes (i.e., n = 21 and 10 for AD with and without ID, respectively). This study only investigated NVIQ–VIQ discrepancies at the group level and did not examine individual discrepancies. The comparison of SB5 subtest profiles between participants with ASD with and without ID revealed less similarity between intellectual ability groups (ID vs. no ID) than that observed among diagnostic subgroups (AD vs. AS vs. PDD-NOS) in the Coolican et al. study. In particular, participants with ASD and ID demonstrated a statistically significant strength in nonverbal visual spatial performance, whereas participants with ASD and no ID demonstrated a statistically significant weakness in verbal knowledge. Although informative, it is unclear whether findings from these two studies are contradictory. It is possible that the diagnostic groups in the former study were not representative of the cognitive functioning groups examined in the latter study and vice versa. Further examination of the SB5 among individuals with ASD is necessary given the widespread use of this measure. Some research indicates that IQ is positively associated with outcome in ASD such that individuals with higher childhood IQs have more positive adult outcomes than those with lower childhood IQs (Billstedt 2005; Nordin and Gillberg 1998). Yet, adaptive functioning has recently been reported to be more strongly related to optimal functioning in adulthood than cognitive functioning (Farley et al. 2009), making adaptive functioning an area ripe for intervention and an important variable to include when assessing long term outcomes (Kanne et al. 2011). Mounting evidence indicates a cognitive functioning advantage over adaptive functioning among cognitively able individuals with ASD, and this gap between cognitive and adaptive skills has been suggested to increase with age (Kanne et al. 2011; Perry et al. 2009). Most work in this area is cross-sectional; thus, it remains unclear whether this gap actually increases within individuals or is merely due

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to a cohort effect. Additionally, evidence regarding an autism-specific profile of adaptive functioning is mixed. Some studies report that individuals with ASD have higher daily living skills, followed by adaptive communication, and then socialization (Carter et al. 1998; Kraijer 2000; Sparrow et al. 2005). However, this proposed profile is not always observed in ASD samples, and may depend on whether standard or age equivalent scores are examined (Fenton et al. 2003; Kanne et al. 2011; Mervis and KleinTasman 2004; Perry et al. 2009). The VABS Interview Edition Survey Form (Sparrow et al. 1984) is a semi-structured caregiver interview designed to assess the skills necessary for age-appropriate independent living, including communication, daily living skills, socialization, and motor skills. Like the SB5, the VABS is commonly used in research and clinical practice to assess individuals with ASD. Previous work has documented a number of patterns among individuals with ASD on the VABS that may be clinically relevant. For example, the relationship between IQ and adaptive functioning appears to differ as a function of cognitive abilities. Young children with IQs of 70 or greater had standard adaptive behavior scores that were significantly lower than their IQ scores, whereas children with IQs of 54 or lower had adaptive behavior scores that were significantly higher than their IQ scores (Perry et al. 2009). This finding supports previous work that has documented an IQ-adaptive functioning discrepancy in higher functioning individuals with ASD, and emphasizes the importance of including both cognitive and adaptive functioning as outcome measures for this population (Kanne et al. 2011). Whereas some individuals may appear to have a positive outcome as measured by cognitive ability, their ability to live and function independently may be severely lacking. In contrast, IQ scores may underestimate adaptive skills among individuals with ASD and ID. A negative association between chronological age and adaptive functioning has been observed in individuals with ASD such that older individuals demonstrate lower overall standard adaptive behavior scores than younger individuals (Kanne et al. 2011; Klin et al. 2007; Szatmari et al. 2003). Consequently, discrepancies between cognitive abilities and adaptive functioning have been observed to increase with age (i.e., adaptive skills appear to decrease). This trend is problematic, as it indicates that individuals with ASD are developing cognitively at a speed that outpaces the development of their adaptive skills (Kanne et al. 2011). This does not necessarily indicate that individuals with ASD are losing acquired adaptive functioning skills as they age. Because standard scores reflect functioning compared to same-age individuals, it is likely that individuals with ASD continue to acquire skills, but at a slower pace than their same-age peers.

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In summary, existing research on SB5 performance among individuals with ASD is limited and potentially contradictory. Findings regarding relative NVIQ and VIQ performance are also inconsistent. A considerable volume of literature concerning adaptive functioning in individuals with ASD exists; however, findings have been mixed and the majority of previous work has focused on specific age groups (e.g., very young children, school-aged children). Additional research utilizing samples with a wider age range and adequate sample size is necessary to better characterize profiles of adaptive functioning and their correlates among children and adolescents with ASD. Ultimately, longitudinal research will be necessary to fully understand trajectories of adaptive functioning in this population. The current study examined the SB5 cognitive profiles of ASD subgroups defined by language onset and intellectual ability in a sample of children and adolescents. Additionally, SB5 ABIQ–FSIQ discrepancies were identified and examined. Last, adaptive behavior profiles on the VABS were examined as a function of intellectual ability and age. Replication of the findings reported by Coolican and colleagues and/or Lennen and colleagues would provide supporting evidence for their conclusions, which could immediately be translated into practice by clinicians and researchers who use the SB5 to assess individuals with ASD. Results pertaining to VABS profiles will aid in the clarification of existing mixed findings and will better inform treatment guidelines determined by intellectual ability.

Method Participants Participants were 73 children and adolescents with ASD (61 males; age M = 6.92 years, SD = 3.13) who participated in a larger genetic study conducted at the Southwest Autism Research and Resource Center. Participants were ascertained using opportunistic recruitment; specifically, fliers were distributed to families already known to the research center, at support groups, and at other autismrelated agencies. To be eligible for inclusion in the current analyses, participants had to meet criteria for autism or borderline autism (i.e., met onset criteria; scores on two out of three ADI-R behavioral domains were above cutoff criteria; below cutoff criteria in third domain by one point) on the Autism Diagnostic Interview-Revised (Rutter et al. 2003) and autism or ASD on the Autism Diagnostic Observation Schedule, Generic (ADOS-G; Lord et al. 1999), be 16 years of age or younger at the time of testing, and have complete data on the SB-5 and VABS Survey

J Autism Dev Disord (2015) 45:138–156 Table 1 Descriptive statistics: demographic and diagnostic variables for the full sample, language onset subgroups, and intellectual ability subgroups

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Full sample (n = 73)

LD (n = 58)

NLD (n = 15)

ID (n = 34)

No ID (n = 39)

M (SD)

6.92 (3.13)

7.03 (3.17)

6.50 (3.04)

6.76 (2.91)

7.06 (3.34)

Range

2–16

2–16

3–12

2–16

3–15

83.56

84.48

80.00

79.41

87.18

Chronological age (years)

Gender (% male) Race/ethnicity % African American

4.00

% Asian

1.14

% Caucasian

69.71

% Hispanic

18.86

% Other

2.29

% did not report 4.00 Annual household income (US dollars) % \20,000

4.11

% 20,001–40,000

9.59

% 40,001–60,000

16.44

% 60,001–80,000

17.81

% 80,001–100,000

10.96

% [100,001

26.02

% did not report

15.07

ADI-R LD language delay, NLD no language delay, ID intellectual disability, ADI-R Autism Diagnostic Interview-Revised, ADOS Autism Diagnostic Observation Schedule

Autism (n)

71

58

13

34

37

Borderline autism (n)

2

0

2

0

2

Autism (n)

59

47

12

30

29

Autism spectrum (n)

14

11

3

4

10

ADOS

Form assessments. All study procedures were prospectively approved by the Western Institutional Review Board (WIRB) and informed parental consent and child assent (when applicable) were obtained prior to assessment and testing. For the first set of analyses, participants were split into two language onset groups, defined by parental report of language delays on the ADI-R. The language delay (LD) group included 58 participants who did not meet one or both language milestones included in the ADI-R. The no language delay (NLD) group included 15 participants who attained single words by 24 months and phrase speech by 33 months of age. This set of analyses was conducted in an attempt to replicate the findings of Coolican et al. (2010). Language onset groups were examined rather than DSMIV diagnostic categories because of recent revisions to the DSM, which collapsed previously existing diagnostic categories into one category of ASD (American Psychiatric Association 2013). Additionally, the formation of subgroups based on language onset is likely more objective than the use of DSM-IV diagnostic categories, which have been previously demonstrated to be limiting and unreliable (Wing et al. 2011).

Participants were also split into two intellectual ability groups, defined by FSIQ scores on the SB5, for comparison to the Lennen et al. (2010) sample. The ID group included 34 participants whose FSIQ was lower than 70. The no ID group included 39 participants whose FSIQ was 70 or above. Demographic and diagnostic information for the full sample, language onset groups, and intellectual ability groups is reported in Table 1. Procedure As a part of the larger study, participants were assessed using the gold standard diagnostic measures for ASD. One parent of each participant was administered the ADI-R and each participant was administered the ADOS-G; administration and scoring of both measures were completed by research reliable raters. Additionally, participants completed the SB5 and one parent of each participant was administered the VABS Survey Form. Raters for the SB5 and the VABS had at least a Bachelor’s degree and were extensively trained and supervised by one of the current authors (C.S.). C.S. is a doctoral-level experimental psychologist with close to two decades of experience in the use

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of cognitive and adaptive functioning assessments among individuals with psychiatric disorders for research purposes. Assessments were completed in a quiet room over one to two study visits. Given that the current research questions were developed post data collection for the purpose of secondary data analysis, raters were unaware of the purpose of this study at the time of testing. Stanford-Binet Intelligence Scales: Fifth Edition The SB5 is a widely used assessment of intelligence and cognitive abilities with demonstrated validity and reliability throughout most of the lifespan (ages 2–85 years). The assessment yields multiple composite intelligence quotients, including NVIQ, VIQ, FSIQ, and ABIQ. Composite quotients are determined by each participant’s performance on ten subtests, which comprise two subscales: nonverbal and verbal. The fluid reasoning (FR) subtests (i.e., nonverbal and verbal FR) measure reasoning and novel problem solving ability, whereas the knowledge (KN) subtests measure accumulated knowledge. The quantitative reasoning (QR) subtests measure various aspects of mathematical thinking; the visual spatial processing (VS) subtests measure aptitude in visually detecting patterns, relations, and spatial orientations. Last, the working memory (WM) subtests measure the ability to manipulate information stored in short term memory. All subtest scores can be converted to standardized scaled scores with a mean of 10 and a standard deviation of 3. The five subtest scaled scores within each subscale (i.e., nonverbal and verbal) are summed and standardized to determine NVIQ and VIQ, respectively. Subtest scaled scores are summed across subscales and standardized to determine FSIQ. The ABIQ reflects performance on two of the subtests, nonverbal FR and verbal KN, and correlates strongly with FSIQ. These two subtests are referred to as the routing subtests, as they are also used to determine starting levels for the eight other subtests by estimating each participant’s ability. All of the composite quotients have a mean of 100 and standard deviation of 15 (Roid 2003). Each case was examined for a significant difference between NVIQ and VIQ (Coolican et al. 2008; Roid 2003). Depending on participant age, a significant NVIQ–VIQ difference at the .05 level requires a discrepancy of at least 9 or 10 points. Using these criteria, participants were placed into three NVIQ–VIQ subgroups: (1) NVIQ? (i.e., participants whose NVIQ scores were significantly higher than their VIQ scores); (2) VIQ? (i.e., participants whose NVIQ scores were significantly lower than their VIQ scores), and (3) NVIQ = VIQ (i.e., participants whose NVIQ scores did not differ significantly from their VIQ scores). Following a similar procedure, each case was examined for a significant discrepancy between ABIQ and FSIQ

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scores. The minimum difference required for significance at the .05 level was calculated (Coolican et al. 2008, p. 194; Roid 2003, p. 121); this number ranged from 10 to 13 depending on participant age. These criteria were used to split the full sample into three ABIQ–FSIQ groups: (1) ABIQ? (i.e., participants whose ABIQ scores were significantly higher than their FSIQ scores; (2) FSIQ? (i.e., participants whose ABIQ scores were significantly lower than their FSIQ scores), and (3) ABIQ = FSIQ discrepancy (i.e., participants whose ABIQ scores did not differ significantly from their FSIQ scores). To examine overall cognitive and adaptive functioning, the sample was split into four groups according to the criteria outlined by Perry et al. (2009). Specifically, participants were placed in the Average IQ (i.e., FSIQ C 85), Borderline IQ (i.e., FSIQ = 70–84), Mild ID (i.e., FSIQ = 55–69), or Moderate ID (i.e., FSIQ = 40–54) group. The minimum possible FSIQ score on the SB5 is 40; thus, there was not a severe ID or profound ID group in the current study. Vineland Adaptive Behavior Scales: Interview Edition Survey Form The VABS Survey Form assesses adaptive functioning through a semi-structured interview with a parent or caregiver. The VABS has established validity and reliability for measuring adaptive functioning from birth to adulthood. Prior to a recent revision to the VABS (i.e., Vineland-II; Sparrow et al. 2005), the VABS Survey Form was widely used to identify strengths and weaknesses in four domains: communication, daily living skills, socialization and motor skills. The motor skills domain is generally only administered when assessing children under the age of 6 years, and was thus excluded from the current analyses (Sparrow et al. 1984). Derived standard scores and age equivalent scores are available for each domain and the Adaptive Behavior Composite (ABC). Standard scores are age-based, range from 20 to 160, and have mean of 100 and standard deviation of 15. In contrast, age equivalent scores correspond to the average raw score for individuals of each age in the standardization sample (Sparrow et al. 1984). Research indicates that the previously observed VABS ‘‘autism profile’’ may be an artifact of the psychometric properties of age equivalent scores (Perry et al. 2009). Thus, both standard and age equivalent scores were examined in the current analyses. Little variability was observed between profiles of performance when examining standard and age equivalent scores. For parsimony, findings regarding standard scores are reported in detail. The few slight discrepancies between standard and age equivalent scores are reported in the Discussion. Plots depicting


the models for both standard scores and age equivalent scores are reported (Figs. 5, 6).

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Results Stanford-Binet Intelligence Scales: Fifth Edition

Data Analysis Stanford-Binet Intelligence Scales: Fifth Edition To facilitate comparison between the current findings and the two previous microanalyses of the SB5 in ASD, similar statistical tests were used when possible and results are presented in a similar format. The Coolican et al. (2008) and the Lennen et al. (2010) studies are henceforth referred to as the ‘‘Coolican’’ or ‘‘Lennen’’ study/sample for the purpose of brevity. First, separate one-sample t tests were conducted to compare average SB5 quotients of the current sample to the SB5 validation sample (Roid 2003) and the Coolican sample. The Lennen study did not report descriptive statistics for their full ASD sample and was thus not included in this comparison. Then, nonverbal and verbal subtest profiles of the current sample were examined using pairwise comparisons with a Bonferroni correction (i.e., p B .005 required for statistical significance). A significant difference between overall NVIQ and VIQ was observed [t(72) = -4.06, p \ .001]; thus, subtest scaled scores were compared within each subscale (i.e., nonverbal and verbal) using separate paired-sample t tests. Separate mixed analyses of variance (ANOVAs) were used to compare cognitive profiles between language onset groups, intellectual ability groups, and among NVIQ–VIQ discrepancy subgroups. Last, SB5 FSIQ–ABIQ discrepancies were calculated and examined using a linear regression model, paired t tests, and a Pearson correlation. Vineland Adaptive Behavior Scales: Interview Edition Survey Form Separate Pearson correlations were conducted to examine associations among chronological age, SB5 FSIQ, and VABS domain standard scores. Then, a mixed analysis of covariance (ANCOVA) was conducted to examine patterns of overall cognitive functioning and adaptive behavior among participants with varying levels of intellectual ability, controlling for chronological age. Last, an ANOVA was used to compare patterns of overall cognitive functioning and adaptive behavior between participants under 6 years of age and 6 years of age and older. For all ANOVA/ANCOVA models in the current study, a Huynh–Feldt correction was used when violations of sphericity were observed (Huynh and Feldt 1970). p values from post hoc pairwise comparisons were adjusted with a Bonferroni correction and the chronological age covariate was centered on its mean. For all analyses, the significance level was set at p \ .05 (unless otherwise noted). Comparisons that approached statistical significance (i.e., p B .10) are also described.

Table 2 reports the descriptive statistics for each of the composite quotients and subtests for the full sample, language onset groups, and intellectual ability groups. Of the full sample, 46.6 % (n = 34; 27 boys) had a FSIQ lower than 70, whereas 53.4 % (n = 39; 34 boys) had a FSIQ of 70 or above. Reported in Table 3, there were no significant differences between the current sample and the SB5 validation study sample (Roid 2003). In contrast, the current sample had significantly lower mean FSIQ, NVIQ, and VIQ scores than the Coolican sample. Cognitive Profiles Reported in Table 2, the nonverbal subtest profile for the full sample was almost identical to the Coolican sample. Specifically, FR, QR, and VS mean scores were significantly higher than the KN mean score. The FR mean score was also significantly higher than the WM mean score. Departing from the Coolican study, the FR mean score was significantly higher than the QR mean score. The verbal QR mean score was significantly higher than the verbal FR, KN, and WM mean scores within the full sample (see Table 2). In contrast to the Coolican study, no significant difference was observed between verbal QR and VS scores or verbal WM and FR mean scores. Cognitive Profiles by Language Onset and Intellectual Ability Groups Age (under 6 vs. 6 and older) was not related to subtest profiles and was not included in the analysis [F(1, 68) = .38, p = .54, d = .01]. Results of a 2 (language onset group: LD vs. NLD) X 10 (SB5 subtest scaled scores) mixed ANOVA revealed a statistically significant main effect of language onset group [F(1, 70) = 8.99, p = .004, g2 = .11] and SB5 subtests [F(7.34, 513.91) = 7.31, p \ .001, g2 = .09]. The interaction between these two variables was not significant [F(7.34, 513.91) = 1.44, p = .18, g2 = .02; see Fig. 1]. Although the NLD group demonstrated better performance than the LD group on the subtests, the profile of performance was similar between the two groups. Results of a 2 (intellectual ability group: ID vs. no ID) 9 10 (SB5 subtest scaled scores) ANOVA revealed a statistically significant main effect of intellectual ability group [F(1, 70) = 146.30, p \ .001, g2 = .68] and SB5 subtests [F(7.25, 507.18) = 11.26, p \ .001, g2 = .14]. The interaction between intellectual ability group and SB5

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Table 2 Descriptive statistics: SB5 IQs and scaled scores for the full sample, language onset subgroups, and intellectual ability subgroups Full sample (n = 73) M (SD)

LD (n = 58) M (SD)

NLD (n = 15) M (SD)

ID (n = 34) M (SD)

No ID (n = 39) M (SD)

Full scale IQ

73.59 (21.60)

69.86 (19.16)

88.00 (24.97)

54.62 (9.21)

90.13 (14.40)

Abbreviated battery IQ

78.37 (21.20)

76.28 (20.63)

86.47 (22.16)

60.18 (10.62)

94.23 (14.13)

Nonverbal IQ

78.05 (21.70)

74.83 (20.14)

90.53 (23.68)

60.12 (13.14)

93.69 (14.28)

Verbal IQ

71.74 (21.87)

67.84 (18.68)

86.80 (27.07)

53.24 (7.19)

87.87 (16.96)

Fluid reasoning (FR) Knowledge (KN)

7.67 (4.42)a 5.37 (3.29)

7.45 (4.45) 4.91 (3.04)d

8.53 (4.36) 7.13 (3.70)

4.27 (3.19)k 3.21 (2.22)d k

Quantitative reasoning (QR)

6.53 (4.16)ab

5.95 (3.87)

8.80 (4.60)

3.27 (2.05)d k

9.40 (3.48)

4.94 (3.88)

efk

9.33 (3.44)dek

3.70 (2.60)

g k

8.41 (2.96)defh ij

SB5 Intelligence Scales

Nonverbal subtests

Visual spatial processing (VS) Working memory (WM)

7.27 (4.28)

a

6.21 (3.65)

b

6.72 (4.25) 5.52 (3.14)

8.87 (4.34)

10.64 (3.01)k 7.31 (2.81)d 9.44 (3.26)dek

Verbal subtests Fluid reasoning (FR) Knowledge (KN) Quantitative reasoning (QR)

4.80 (4.12)c

4.21 (3.71)j

7.07 (4.94)j

1.88 (1.39)defg

c

j

j

2.42 (1.52)

df g

7.44 (2.75)dfg

3.88 (2.23)

hik

9.67 (4.28)ehik

dgh

8.03 (4.06)dfg j

5.08 (3.39) 6.95 (4.53)

4.60 (3.19) 6.21 (3.89)

6.93 (3.62) 9.80 (5.74)

Visual spatial processing (VS)

5.65 (4.23)

4.79 (3.41)

8.93 (5.44)

2.85 (2.25)

Working memory (WM)

5.40 (3.96)c

4.97 (3.68)

7.07 (4.65)j

2.15 (1.70)

7.31 (4.08)dfgk

8.15 (3.15)

LD language delay, NLD no language delay, ID intellectual disability a

Significantly different from nonverbal KN in the full sample (ts(72) = 3.11–6.00, ps \ .001–.003, ds = .36–.70) Significantly different from nonverbal FR in the full sample (ts(72) = 2.87 and 3.80, ps B b.005, ds = .34 and .44) c

Significantly different from verbal QR in the full sample (ts(71–72) = 3.76–4.54, ps \ .001, ds = .44–.52)

d

Significantly different from nonverbal FR

e

Significantly different from nonverbal KN

f

Significantly different from nonverbal QR

g

Significantly different from nonverbal VS

h

Significantly different from verbal FR

i

Significantly different from verbal KN

j

Significantly different from verbal QR

k

Significantly different from verbal WM

d–k

p B .05

Difference indicated by lower case superscript is marginal (p B .10)

subtests was approaching statistical significance [F(7.25, 507.18) = 1.72, p = .10, g2 = .02; see Fig. 2]. Tests of simple effects are reported in Table 2. Starting with the nonverbal subtests, participants in the ID group demonstrated the strongest performance on the VS subtest, followed by FR, WM, QR, and then KN. In contrast, participants in the no ID group demonstrated the strongest performance on the FR subtest, followed by QR, VS, WM, and then KN. Regarding the verbal subtests, participants in the ID group demonstrated the strongest performance on the QR subtest, followed by VS, KN, WM, and then FR. Participants in the no ID group demonstrated the strongest performance on the QR subtest, followed by WM, VS, KN and then FR.

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Differences Between NVIQ and VIQ Each case was examined for a significant difference between NVIQ and VIQ (see Method). The frequency of each NVIQ– VIQ discrepancy pattern (i.e., NVIQ?, VIQ?, and NVIQ = VIQ) is reported in Table 4. NVIQ–VIQ discrepancy scores (absolute) were not significantly correlated with age (r = -.11, p = .34) or FSIQ (r = -.02, p = .90) and there was no significant difference in discrepancy scores between participants under the age of 6 years and participants who were 6 years of age or older [t(71) = .41, p = .68, d = .09]. A 2 9 3 Fisher’s exact test indicated that the two age groups did not differ in the frequency of cases representing each of the NVIQ–VIQ patterns (p = .94).

J Autism Dev Disord (2015) 45:138–156 Table 3 SB5 FSIQ, NVIQ, and VIQ and VABSa in the current sample and other published ASD samples

Variable

145

Current study (n = 73) M (SD)

Validation study (n = 83) M (SD)

Coolican et al. (2008) (n = 63) M (SD)

Age range (years)

2–16

2–17

2–16

Gender (% male)

84

79

81

SB5 FSIQ SB5 Stanford Binet Intelligence Scales, fifth edition, FSIQ full scale IQ, NVIQ nonverbal IQ, VIQ verbal IQ, VABS Vineland Adaptive Behavior Scales, survey form a

Current sample only

b

There was a marginal difference in NVIQ between the current sample and validation sample [t(72) = 1.87, p = .07]

c

The current sample had significantly lower FSIQ, NVIQ, and VIQ than the Coolican et al. sample [FSIQ: t(72) = -3.44, p \ .001; NVIQ: t(72) = -3.67, p \ .001; VIQ: t(72) = -2.88, p = .01)]

73.59 (21.60)c bc

NVIQ

78.05 (21.70)

VIQ

71.74 (21.87)c

82.29 (25.74)c

70.40 (21.20) 73.30 (20.80) 70.20 (20.80)

b

87.38 (26.20)c 79.11 (25.27)c

VABS Composite Standard score

59.63 (17.35)

Age equivalent

3.83 (1.99)

Communication Standard score

71.32 (21.21)

Age equivalent

4.60 (2.80)

Daily living scales Standard score

57.00 (22.41)

Age equivalent

3.73 (2.07)

Socialization Standard score

64.10 (15.77)

Age equivalent

3.06 (1.70)

Fig. 1 SB5 subtest profiles by language delay subgroups

Separate 3 (NVIQ? vs. VIQ? vs. NVIQ = VIQ) 9 5 (nonverbal or verbal subtests) mixed ANOVAs were conducted for each language onset group to examine nonverbal and verbal cognitive profiles by direction of NVIQ–VIQ discrepancy. In the LD group there was a significant main effect of discrepancy direction [F(2, 55) = 10.84, p \ .001, g2 = .28] and nonverbal subtests [F(4, 220) = 4.98, p \ .001, g2 = .08]. Additionally, there was a significant interaction between discrepancy direction and

Fig. 2 SB5 subtest profiles by intellectual ability subgroups

nonverbal subtests [F(8, 220) = 2.83, p = .005, g2 = .09; see Fig. 3]. Reported in Table 4, post hoc pairwise comparisons indicated that the NVIQ? subgroup had significantly higher average NVIQ scaled scores than the NVIQ = VIQ subgroup. There was no significant difference in average NVIQ scaled scores between the NVIQ? and VIQ? subgroups or the VIQ? and NVIQ = VIQ subgroups. A second set of pairwise comparisons was conducted on the nonverbal subtests for the full LD group. Reported in Table 2, the FR mean score was significantly

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146


Table 4 SB5 FSIQ and subscale scaled scores by NVIQ–VIQ discrepancy groups NVIQ–VIQ discrepancy groups NVIQ?a M (SD)

VIQ?b M (SD)

29, 40 %, 25

7, 10 %, 6

NVIQ = VIQc M (SD)

Full sample (n = 73) n, %, # males FSIQ Language delay (n = 58) n, %, # males FSIQ Nonverbal scaled score average Verbal scaled score average

79.24 (17.04)d

37, 50 %, 30

98.00 (20.27)d

23, 40 %, 20

4, 7 %, 3

64.54 (20.32)d 31, 53 %, 26

75.74 (13.61)

87.25 (16.52)

63.26 (20.56)

7.99 (2.11)e

7.05 (2.55)

4.59 (3.05)e

4.82 (2.18)

f

9.25 (2.52)

f

4.55 (3.17)f

No language delay (n = 15) n, %, # males

6, 40 %, 5

3, 20 %, 3

6, 40 %, 4

FSIQ

92.67 (23.21)

112.33 (16.86)

71.17 (19.30)

Nonverbal subscales average

10.33 (3.48)g

10.33 (2.42)

5.87 (2.89)g

Verbal subscales average

7.57 (3.67)

h

13.53 (2.93)

h

5.57 (2.96)h

SB5 Stanford Binet Intelligence Scales, fifth edition, NVIQ nonverbal IQ, VIQ verbal IQ a NVIQ was significantly higher than VIQ b

NVIQ was significantly lower than VIQ

c

NVIQ did not differ significantly from VIQ

d

There was a significant difference in FSIQ among NVIQ–VIQ discrepancy groups (F(2, 70) = 11.17, p \ .001). The VIQ? group had marginally higher and higher FSIQ than the NVIQ? group (p = .07) and NVIQ = VIQ group (p \ .001), respectively. The NVIQ? group had significantly higher FSIQ than the NVIQ = VIQ group (p = .01)

e

The NVIQ? group had significantly higher average NVIQ scaled scores than the NVIQ = VIQ group (p \ .001)

f

The VIQ? group had significantly higher average VIQ scaled scores than the NVIQ? and NVIQ = VIQ groups (ps = .01)

g

The NVIQ? group had marginally higher average NVIQ scaled scores than the NVIQ = VIQ group (p = .08) The VIQ? group had marginally higher average VIQ scaled scores than the NVIQ? group (p = .07) and significantly higher VIQ scaled scores than the NVIQ = VIQ groups (p = .01) h

higher than the KN mean score. The other comparisons were not significant. Tests of simple effects were not conducted due to small and unbalanced cell sizes. However, visual examination of Fig. 3 revealed that the three groups had similar patterns of performance on the FR, KN, and QR subtests (i.e., FR [ QR [ KN). VS and WM appeared to differentiate the discrepancy groups. Specifically, the VS subtest was a relative strength for the NVIQ? group and a relative weakness for the VIQ? group. The WM subtest was a relative weakness for the NVIQ? group and relative strength for the VIQ? group. The second 3 9 5 mixed ANOVA (verbal subtests) for the LD group revealed a significant main effect of discrepancy direction subgroup [F(2, 54) = 5.15, p = .01, g2 = .16] and verbal subtests [F(3.08, 166.25) = 5.09, p = .002, g2 = .08]. However, there was no significant interaction between discrepancy direction subgroup and verbal subtests [F(6.16, 166.25) = 1.15, p = .34, g2 = .04; see Fig. 3]. Reported in Table 4, post hoc pairwise comparisons indicated that the VIQ? subgroup had significantly higher average VIQ scaled scores than the NVIQ? and NVIQ = VIQ subgroups. There was no

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significant difference in VIQ scaled scores between the NVIQ? and NVIQ = VIQ subgroups. A second set of pairwise comparisons was conducted on the verbal subtests among the full LD group. Reported in Table 2, the QR mean score was significantly higher than the FR mean score and the KN mean score. Visual inspection of Fig. 3 revealed similar patterns of performance across groups, despite higher scores from the VIQ? subgroup. In the NLD group there was a significant main effect of discrepancy direction [F(2, 12) = 3.79, p = .05, g2 = .39]. The main effect of nonverbal subtests was not significant [F(4, 48) = 1.52, p = .21, g2 = .09], nor was the interaction between discrepancy direction and nonverbal subtests [F(8, 48) = 1.37, p = .24, g2 = .16]. Reported in Table 4, post hoc pairwise comparisons indicated that the difference in average NVIQ scores between the NVIQ? and NVIQ = VIQ subgroups was approaching statistical significance. Specifically, the NVIQ? subgroup had higher average NVIQ scaled scores than the NVIQ = VIQ subgroup. There was no significant difference in NVIQ between the NVIQ? and VIQ? subgroups or the VIQ? and NVIQ = VIQ subgroups.


Fig. 3 SB5 subtest profiles by NVIQ–VIQ discrepancy subgroups in the LD group

The mixed ANOVA used to examine verbal subtests in the NLD group revealed a significant main effect of discrepancy direction subgroup [F(2, 12) = 6.03, p = .02, g2 = .50] and verbal subtests [F(4, 48) = 4.33, p = .005, g2 = .23]. However, there was no significant interaction between discrepancy direction subgroup and verbal subtests [F(8, 48) = 1.41, p = .22, g2 = .14]. Reported in Table 4, post hoc pairwise comparisons indicated that the VIQ? subgroup had higher average VIQ scaled scores than the NVIQ = VIQ subgroup. Additionally, the difference in VIQ scaled scores between the VIQ? and NVIQ? subgroups was approaching statistical significance such that the VIQ? group had higher scores than the NVIQ? subgroup. There was no significant difference in VIQ scaled scores between the NVIQ? and NVIQ = VIQ subgroups. A second set of pairwise comparisons was conducted on the verbal subtests among the full NLD group. Reported in Table 2, The QR mean score was significantly higher than the FR, KN, and WM mean scores. Differences Between ABIQ and FSIQ In order to investigate the concordance of ABIQ and FSIQ, the sample was split into three groups based on the difference between ABIQ and FSIQ scores for each individual case; this process is described in detail in the Method section. The frequency of each ABIQ–FSIQ discrepancy pattern (i.e., ABIQ?, FSIQ?, and ABIQ = FSIQ) and descriptive statistics for each discrepancy group are reported in Table 5.

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Results of a linear regression indicated that ABIQ scores accounted for 82.3 % of the variance in the FSIQ scores of the full sample (R2 = .823). A paired samples t test indicated that ABIQ scores were significantly higher than FSIQ scores [t(72) = 4.42, p \ .01, d = .22]. However, the mean difference between ABIQ and FSIQ scores (4.78) was not clinically relevant based on the required difference of 10–13 points (outlined in the Method section). The cognitive profiles of each ABIQ–FSIQ discrepancy group are plotted in Fig. 4. Disproportionately high scores on the nonverbal FR subtest appeared to be responsible for ABIQ scores among the group of children who had significantly higher ABIQ than FSIQ scores. The ABIQ? group had significantly higher nonverbal FR scaled scores than the FSIQ = ABIQ group [t(69) = -4.12, p \ .001, d = 1.09]. Notably, the ABIQ? group also had higher verbal KN subtests scores than the FSIQ = ABIQ group; this difference was approaching statistical significance [t(69) = -1.77, p = .08, d = -.46]. Regardless, the mean difference between the nonverbal FR and verbal KN subtests (i.e., the routing subtests used to determine ABIQ scores) in the ABIQ? group was 4.6, which is clinically relevant ([3.04) according to the SB5 technical manual. Cases were examined to determine whether a discrepancy score [4 points on the routing subtests differentiated cases in the ABIQ? and ABIQ = FSIQ groups (Coolican et al. 2008). Among the ABIQ? group, 68.4 % (13/19) participants had a routing discrepancy score larger than 4. Among the ABIQ = FSIQ group, 30.8 % of (16/52) had a routing discrepancy score larger than 4. ABIQ–FSIQ discrepancy scores (absolute) were not associated with age (r = -.09, p = .46) and participants under the age of 6 and 6 years of age and older did not demonstrate significantly different discrepancy scores [t(71) = -.26, p = .79, d = -.06]. A significant small-to-medium positive association was observed between FSIQ and ABIQ–FSIQ discrepancy scores (r = .26, p = .03), such that participants who had higher ABIQs than FSIQs had lower FSIQs than participants with higher FSIQs than ABIQs. Vineland Adaptive Behavior Scales: Survey Form Descriptive statistics representing the VABS ABC and subdomain standard scores for the full sample are reported in Table 3. Among the full sample, participant age was negatively correlated with daily living skills standard scores (r = -.40, p \ .001), socialization standard scores (r = -.46, p \ .001), and ABC standard scores (r = -.37, p = .001). Specifically, older participants were reported to have poorer daily living skills, socialization, and overall adaptive functioning than younger participants. Participant age was not significantly associated with communication standard scores (r = -.18, p = .12) or FSIQ (r = .01,

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148


Table 5 SB5 ABIQ–FSIQ discrepancy patterns in the full sample (n = 73) n

%

# males

# with LD

FSIQ M(SD)

ABIQ M(SD)

ABIQ–FSIQ discrepancy M(SD)

ABIQ–FSIQ discrepancy Range

ABIQ–FSIQ discrepancy groups ABIQ?a

19

26

17

17

75.63 (20.04)

91.32 (21.33)

15.68 (3.46)

11–25

FSIQ?b ABIQ = FSIQc

2 52

3 71

2 42

1 40

110.50 (19.09) 71.42 (21.21)

86.50 (2.12) 73.33 (19.58)

24.00 (16.97) 4.52 (2.87)

12–36 0–10

SB5 Stanford Binet Intelligence Scales, fifth edition, NVIQ nonverbal IQ, VIQ verbal IQ, LD language delay, NLD no language delay a

Participants whose ABIQ was significantly higher than their FSIQ

b

Participants whose ABIQ was significantly lower than their FSIQ

c

Participants whose ABIQ did not differ significantly from their FSIQ

p = .91). FSIQ scores were positively associated with communication standard scores (r = .59, p \ .001), daily living skills standard scores (r = .41, p \ .001), Socialization standard scores (r = .42, p \ .001), and ABC standard scores (r = .54, p \ .001). Thus, participants with higher FSIQ scores had higher adaptive functioning across domains than participants with lower FSIQ scores. Adaptive Behavior Profiles by Intellectual Ability Groups A 4 (intellectual ability groups: Average IQ, Borderline IQ, Mild ID, and Moderate ID) 9 4 (FSIQ and VABS subtests) mixed ANCOVA was conducted, controlling for chronological age. Results indicated significant main effects of intellectual ability group and measure. The interaction between intellectual ability group and measure was also significant (see Table 6; Fig. 5). Reported in Table 6, post hoc pairwise comparisons indicated that average FSIQ and VABS standard scores were highest in the Average IQ group and lowest in the Moderate ID group. The borderline IQ group had higher scores than the the Mild ID group, which had higher scores than the Moderate ID group; however, these differences were only approaching statistical significance. Within the full sample, FSIQ did not differ significantly from communication, but was significantly higher than daily living skills and socialization. Communication scores were significantly higher than daily livings skills and socialization scores, and socialization scores were significantly higher than daily living skills scores. An examination of simple effects revealed a FSIQ advantage over adaptive functioning standard scores in the Average IQ and Borderline IQ groups. In contrast, an adaptive functioning advantage over FSIQ was observed in the Moderate ID group. Specifically, FSIQ was significantly higher than standard scores in all three adaptive behavior domains in the Average IQ subgroup. FSIQ was significantly higher than daily living skills and

123

Fig. 4 SB5 subtest profiles by ABIQ–FSIQ discrepancy subgroups

socialization, but not communication, in the Borderline IQ group. Besides a marginal difference between FSIQ and daily living skills, there was no difference between FSIQ and adaptive behavior in the Mild ID group. Last, FSIQ was significantly lower than communication and socialization in the Moderate ID group (see Table 6; Fig. 5). Regarding adaptive behavior domains, the Average IQ group demonstrated the most diverse profile, with highest standard scores in the communication domain followed by socialization and then daily living skills; all comparisons in this group were statistically significant. The Borderline IQ group demonstrated a flatter profile. Communication scores for this group were significantly higher than daily living skills and marginally higher than socialization; there was no difference between daily living skills and socialization. In

46.88 (1.86) 71.38 (.91)

Moderate ID (n = 17)

Full sample (n = 73)

69.95 (2.04)

56.28 (2.26)

def

43.08 (4.63)ef

55.45 (4.18)d

d ef

54.07 (4.64)

62.04 (4.78)

de

65.95 (3.99)def

VABS DLS M (SE)

63.71 (4.19)

73.49 (4.31)

87.15 (3.60)d

VABS Com M (SE)

63.42 (1.51)

de

55.50 (3.10)d

60.29 (3.11)

65.86 (3.20)de

72.04 (2.67)de

VABS Soc M (SE)

50.23 (2.62)abc

60.09 (2.62)ab

69.62 (2.70)a

81.10 (2.25)

Average M (SE)

f

Difference indicated by lower case superscript is approaching statistical significance (p B .10)

Significantly different from VABS Soc (p \ .05)

e

Significantly different from FSIQ (p \ .05)

Significantly different from VABS Com (p \ .05)

d

Significantly different from the Borderline IQ group (p \ .005)

Significantly different than the Mild ID group (p \ .05)

c

Significantly different from the Average IQ group (p \ .05)

b

a

FSIQ full scale intelligence quotient on the Stanford Binet Intelligence Scales, fifth edition, VABS Vineland Adaptive Behavior Scales, Com communication, DLS daily living skills, Soc socialization, Average main effect of measure (i.e., average of FSIQ, VABS Com, DLS, and Soc)

62.27 (1.86)

Mild ID (n = 17)

.18

.20 .56

FSIQ M (SE)

Within-group variables

77.09 (1.92)

\.001

\.001 \.001

g2

Borderline IQ (n = 16)

(8.52, 195.84)

(2.84, 195.84) (3, 68)

p

99.27 (1.60)

6.06

22.27 29.33

(df, df)

Average IQ (n = 23)

Intellectual ability groups

Simple effects

Measure 9 group

Interaction

Measure: FSIQ and VABS Intellectual ability groups

Main effects

Standard scores model

F

Table 6 Cognitive and adaptive functioning by intellectual ability groups: standard scores mixed ANCOVA model

J Autism Dev Disord (2015) 45:138–156 149

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150


Fig. 5 Cognitive and adaptive functioning by intellectual ability groups

Table 7 Cognitive and adaptive functioning by age group: standard scores mixed ANOVA model F

(df, df)

g2

p

Within-group variables FSIQ M (SE)

VABS Com M (SE)

72.76 (3.53)

75.11 (3.40)

VABS DLS M (SE)

VABS Soc M (SE)

Average M (SE)

Standard scores model Main effects Measure: FSIQ and VABS Age group

(2.69, 191.01) \.001

.24

4.55

(1, 71)

.04

.06

5.32

(2.69, 191.01)

.002

.05

24.10

Interaction Measure 9 age group Simple effects Age groups Under 6 years 6 years and older Full sample

74.49 (3.67) 73.62 (2.55)

67.20 (3.55) 71.15 (2.46)

64.24 (3.45)bcd b

69.45 (2.41)c

70.39 (2.63)

49.14 (3.59)

bcd

58.27 (2.51)bc

62.28 (2.74)a

56.69 (2.49)

bcd

bc

63.87 (1.74)

FSIQ full scale intelligence quotient on the Stanford Binet Intelligence Skills, fifth edition, VABS Vineland Adaptive Behavior Scales, Com communication, DLS daily living skills, Soc socialization, Average main effect of measure (i.e., average of FSIQ, VABS Com, DLS, and Soc) a

Significantly different from the younger age group (i.e., under 6 years; p \ .05)

b

Significantly different from FSIQ or MA (p \ .05)

c

Significantly different from VABS Com (p \ .05)

d

Significantly different from VABS Soc (p \ .05)

Difference indicated by lower case superscript is approaching statistical significance (p B .10)

the Mild ID group, communication scores were significantly higher than daily living skills scores. The difference between socialization scores and daily living skills scores was approaching significance such that socialization was higher than daily living skills. Communication and socialization did not differ but were both significantly higher than daily living skills in the Moderate ID group (see Table 6; Fig. 5).

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Adaptive Behavior Profiles by Age Subgroups In order to compare patterns of cognitive ability and adaptive behavior between the younger and older participants, a 2 (age group) 9 4 (FSIQ and VABS subtests) mixed ANOVA was conducted. Results revealed a significant main effect of age group such that the younger, group


151

Fig. 6 Cognitive and adaptive functioning by age subgroups

had significantly higher average FSIQ and VABS standard scores than the older group. There was also a main effect of measure and the interaction between age group and measure was significant (see Table 7; Fig. 6). Post-hoc pairwise comparisons indicated that within the full sample, the profile of standard scores was the same as the intellectual ability standard scores model described above (i.e., FSIQ = communication scores [ socialization scores [ daily living skills). An examination of simple effects indicated that with the exception of FSIQ, the two age groups had similar patterns of performance. Specifically, communication was significantly higher than daily living skills and socialization, and daily living skills was significantly lower than socialization in both age groups. In the younger group, FSIQ was significantly higher than daily living skills, but not communication or socialization. In contrast, FSIQ was significantly higher than standard scores in all three adaptive behavior domains in the older group.

Discussion The current study had two broad objectives: (1) to clarify and potentially replicate previously identified SB5 cognitive profiles (Coolican et al. 2008; Lennen et al. 2010), and (2) to examine profiles of cognitive and adaptive skills as a function of intellectual ability and age in children and adolescents with ASD. Findings supported limited existing research on the SB5 in ASD, including profiles on the verbal and nonverbal subscales, cognitive profiles by language-onset (current study) or diagnostic group (Coolican study), NVIQ–VIQ discrepancies, and ABIQ–FSIQ discrepancies. Results also suggest that cognitive profiles on the SB5 differ between individuals according to ID status

at the group level, a parallel finding to that of Lennen et al. (2010). Because ID status is considered when differentiating DSM-IV diagnostic subgroups, it previously appeared that the findings of the two existing studies on the SB5 in individuals with ASD were contradictory. The knowledge gained from the current study suggests that these findings were complimentary. Evidence for the previously reported cognitive functioning advantage over adaptive functioning was observed. As with previous studies, current findings suggest that this advantage increases with age and is more common among individuals with ASD without ID. Observed profiles of adaptive functioning did not support the previously proposed autism profile (i.e., daily living skills [ communication [ socialization). Taken together, findings of the current study clarify and extend previous research on cognitive and adaptive functioning in children and adolescents with ASD. In particular, the replication of previously reported SB5 cognitive profile lends credence to the results regarding adaptive functioning. Also, these findings allow for a more complete understanding of cognitive and adaptive functioning in individuals with ASD that researchers and clinicians can immediately translate into practice. Cognitive Profiles on the SB5 Commensurate with a growing body of research, about half of the current sample of children and adolescents with ASD did not demonstrate ID (Chakrabarti and Fombonne 2005; Charman et al. 2011; Coolican et al. 2008). The current sample did not differ significantly in FSIQ, NVIQ or VIQ from the SB5 validation sample (Roid 2003), but scored significantly lower than the Coolican sample on all three quotients. This is likely a result of the composition of the

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Coolican sample, half of which was diagnosed with either Asperger’s syndrome or PDD-NOS. In contrast, 80 % of the current sample was reported to have language delay characteristic of Autistic Disorder. Despite differences in overall intellectual ability, profiles of performance on the SB5 were quite similar between the current and Coolican samples. Average NVIQ was significantly higher than VIQ in the full sample suggesting a nonverbal advantage on the SB5 among individuals with ASD. This conclusion contrasts with the findings of Lennen et al. (2010), who did not observe a significant difference between NVIQ and VIQ on the SB5 in their subsamples of children with ASD with and without ID. Importantly, the subsample sizes in the Lennen study were relatively small; thus, the analyses may have been underpowered to detect small differences between NVIQ and VIQ. Cognitive Profiles by Language Onset and Intellectual Ability Groups Whereas Coolican and colleagues examined SB5 cognitive profiles by diagnostic subgroup (i.e., AD, AS, and PDDNOS), the current study examined SB5 cognitive profiles by language onset groups (i.e., language delay and no language delay). Despite this difference in methodology, both studies found significant group differences in performance on the subtests, but the subgroups in each study demonstrated parallel patterns of relative performance across subtests. The current findings support the conclusion made by Coolican and colleagues that diagnostic subgroup, which is determined in large part by language onset, does not appear to contribute substantially to variability in SB5 cognitive profiles. In contrast, the interaction between intellectual ability groups (ID and no ID) and SB5 subtests was approaching statistical significance. This partially replicates the findings reported by Lennen et al. (2010). In particular, both studies observed that nonverbal visual spatial reasoning was an area of relative strength for participants with ID, but not for participants without ID. The remaining subtest comparisons within intellectual ability subgroups did not replicate the Lennen study. However, considerably more variability was observed in cognitive profiles between the intellectual ability subgroups than the language onset subgroups in the current study. The observed lack of variability between language onset groups supports recent changes to ASD diagnostic criteria, which subsumed AD, AS, and PDDNOS under one diagnostic category, ASD (American Psychiatric Association 2013). With replication, the observed profiles of performance for individuals with ASD with and without ID could be informative for treatment and educational planning.

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Differences Between NVIQ and VIQ In addition to comparisons of NVIQ and VIQ at the group level, significant discrepancies between NVIQ and VIQ were examined at the individual level. Similar to the Coolican sample, slightly less than half of the participants in both the NLD and LD groups had a significantly higher NVIQ than VIQ, very few participants had significantly higher VIQ scores, and the remaining participants in each group did not show a significant NVIQ–VIQ discrepancy. These findings support previous claims that a large subgroup of individuals with ASD demonstrates a nonverbal advantage, and this advantage does not seem to be related to age, overall intelligence, or diagnostic subgroup. Taken together with the findings of the Coolican study, the current analysis of cognitive profiles by NVIQ–VIQ discrepancy group suggest that participants with either a significant nonverbal advantage or no advantage (verbal or nonverbal) on the SB5 have relatively reliable profiles of performance when measured at the group level. Participants with a significant verbal advantage demonstrated considerably different profiles across studies; however, cell sizes in both studies were small and unlikely to be representative. Future examinations of the SB5 should oversample for participants with a verbal advantage in order to conduct meaningful examinations of the cognitive profiles of this subgroup. Performance on the nonverbal visual spatial and working memory subtests appeared to differentiate the NVIQ? and VIQ? subgroups in the current sample. The NVIQ? subgroups demonstrated a relative strength in nonverbal visual spatial reasoning and a relative weakness in nonverbal working memory in both language onset groups, whereas the VIQ? subgroups demonstrated a relative weakness in nonverbal visual spatial reasoning (both LD and NLD groups) and a relative strength in nonverbal working memory (LD group only). For the most part, NVIQ–VIQ discrepancy subgroups demonstrated similar verbal subtest profiles in both the LD and NLD groups. Differences Between ABIQ and FSIQ The extent to which the SB5 ABIQ approximates FSIQ in ASD has been understudied. The distribution of FSIQ– ABIQ discrepancy patterns in the current study replicated the Coolican study. Specifically, 26 % of the current sample had ABIQ scores that were significantly higher than their FSIQ scores; 3 % had FSIQ scores that were significantly higher than their ABIQ scores, and 71 % had FSIQ and ABIQ scores that were not significantly different. ABIQ–FSIQ discrepancies were not observed to be related to chronological age; however, participants with significantly higher ABIQs than FSIQs were more likely to have


lower FSIQs than participants with no significant discrepancy or higher FSIQs than ABIQs. In both studies, scores on the nonverbal fluid reasoning subtest appeared to contribute to higher ABIQ scores in the ABIQ? group. The interpretation of these findings proposed by Coolican and colleagues was that caution should be used when using the SB5 ABIQ to approximate FSIQ in ASD, especially when discrepancies between the two routing subtests are significant and/or when overall cognitive functioning abilities are relatively low. An alternative explanation that has not, to our knowledge, been previously explored is that these ‘‘inflated’’ ABIQs may better represent cognitive functioning in individuals with ASD than FSIQ scores. A myriad of factors, including fatigue, attentional impairments, and specific areas of cognitive dysfunction (e.g., language impairment, weak central coherence) may contribute to artifactually dampened FSIQ scores in some individuals with ASD. Additional research is necessary to determine whether short-form or full scale IQ tests more accurately represent cognitive abilities in this population. Vineland Adaptive Behavior Scales The characterization of adaptive behavior in individuals with ASD is a topic of contemporary interest (Duncan and Bishop 2013; Kanne et al. 2011). The current study employed a sample with a wide range of intellectual abilities and age in order to compare overall cognitive functioning to adaptive functioning domains in individuals with ASD. Findings did not support the previously reported autism profile of adaptive functioning. The Communication domain was observed to be an area of relative strength within the adaptive behavior domains. In contrast, daily living skills and Socialization domains were observed to be a relative weakness when examining standard scores and age equivalent scores, respectively. Although varying somewhat in the size of the differences between domains, these patterns were observed across intellectual ability and age groups. These findings are in contrast to some previous reports that daily living skills are a relative strength in ASD (Carter et al. 1998; Kraijer 2000; Sparrow et al. 2005). In line with these previous reports, however, Communication scores were higher than Socialization scores in the current study. Although the currently reported profiles of adaptive functioning differ slightly from previous literature, the strong replication of previously reported cognitive profiles using the current sample supports the validity of these findings. These results provide supporting evidence to the caution extended by Perry et al. (2009) against the use of the proposed autism profile to confirm an ASD diagnosis. Additionally, they highlight the importance of recognizing daily living skills as an area of impairment in ASD, and the

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need for interventions that target these skills in those who are cognitively able (Duncan and Bishop 2013; Kanne et al. 2011). Adaptive Behavior and Intellectual Ability Overall cognitive functioning was observed to be stronger than adaptive functioning among the full sample, with the exception of standard communication scores, which did not differ from FSIQ scores. In general, the Borderline IQ and Mild ID groups demonstrated the flattest profiles of performance; specifically, the differences between cognitive functioning and adaptive functioning domains in these groups were smaller than those observed in the Average IQ and Moderate ID subgroups. The group with strongest cognitive skills (Average IQ) had an average FSIQ that was significantly higher than average standard scores in all VABS domains, whereas the group with the weakest cognitive skills (Moderate ID) had an average FSIQ that was significantly lower than average Communication and Socialization standard scores. Although not reported in the results, a similar pattern was observed among the Average IQ group when comparing mental age and VABS age equivalent scores. In contrast, mental age was significantly higher than daily living skills and socialization age equivalent scores, but did not differ from communication age equivalent scores in the Moderate ID group. When considering standard scores, these findings support previous reports of a cognitive functioning advantage over adaptive functioning in individuals without ID, and an adaptive functioning advantage for individuals with ASD with ID (Perry et al. 2009). This pattern of findings should inform intervention planning. In particular, adaptive functioning is a prime area for intervention among individuals with ASD and no ID. With further exploration and replication, these findings may also prove to be useful for clinicians and researchers without access to results from formal cognitive assessments. Given that there was no significant difference between FSIQ and VABS Communication scores within two of the cognitive subgroups and within the full sample, standard VABS Communication scores may provide a rough approximation of overall cognitive ability when a formal assessment is not feasible. Adaptive Behavior and Age The wide age range was a strength of the current study, as it allowed for a comparison of cognitive and adaptive functioning between younger and older participants. Profiles were flatter for the younger group (i.e., children under the age of 6 years) than the older group. Differences among cognitive functioning and VABS domains were smaller and often not significant in the younger age group. These

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findings support previous reports of a gap between cognitive and adaptive functioning that increases with age (Kanne et al. 2011; Klin et al. 2007; Szatmari et al. 2003). However, they do not necessarily indicate that individuals with ASD lose skills as they age. Post-hoc analyses indicated that age was significantly positively correlated with all nine VABS subdomain raw scores in the current sample (rs = .46 to .72, ps \ .001). This suggests that individuals with ASD continue to acquire adaptive skills as they age. However, VABS standard scores represent differences between individuals with ASD and their same-age peers. Thus, it appears that typically developing individuals are developing adaptive functioning skills at a rate that outpaces individuals with ASD. Limitations The reported analyses provide a rich description of cognitive and adaptive profiles among individuals with ASD. The large number of analyses used to provide this nuanced examination increased the likelihood of type I error. Thus, caution is warranted when interpreting these findings. Conventional statistical strategies were used to address this issue (i.e., Bonferroni corrections) whenever possible. Additionally, many findings replicate previous research, which suggests that these findings were not artifacts of multiple comparisons. The current sample size was commensurate with, or larger than, that of many studies in the literature. However, like most studies of individuals with ASD, the sample size was small by statistical conventions and potentially underpowered. This may have attenuated variance in demographic variables and the primary variables of interest, reducing the generalizability of the current findings. This possibility is discussed in detail below. Additionally, the cell sizes in some analyses were quite small due to the natural distribution of cognitive profiles in the sample (e.g., NVIQ–VIQ and ABIQ–FSIQ discrepancies). Future research should oversample for participants with specific cognitive profiles that have been underrepresented, and therefore understudied in the literature, in order to better understand the cognitive and adaptive abilities of these potential ASD subgroups. The first half of the current study focused on the examination of cognitive profiles by specific subgroups, yet the VABS profiles of these subgroups were not examined. Importantly, the comparison of language onset groups revealed no difference in cognitive profiles, and the natural distribution of NVIQ–VIQ and ABIQ–FSIQ discrepancy groups precluded meaningful analyses of adaptive functioning due to small and unbalanced sample sizes. Thus, the second half of the current study focused on an examination of adaptive profiles by nuanced intellectual ability subgroups, which were demonstrated to have differing cognitive profiles.

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The exclusion criteria for the current study resulted in the exclusion of participants who did not meet criteria for autism or autism spectrum on the ADOS and ADI-R. Potential participants who did not have complete data on the SB5 or VABS Survey Form were also excluded. It is possible that the exclusion of these individuals reduced the generalizability of the current findings. However, the current sample represented a wide range of cognitive and adaptive functioning. Many individuals with ASD do not test well for a variety of reasons (e.g., impairment in executive functioning, lack of compliance and motivation). A number of these individuals were excluded from the current analyses due to incomplete data on the SB5. However, it is possible that SB5 results for some included participants may be inaccurate for the abovementioned reasons. To address these potential issues, raters were trained to use positive reinforcement in order to encourage optimal performance, regardless of correct or incorrect responses. The demographic profile of the current sample revealed a preponderance of non-Hispanic whites and individuals from middle to upper-middle class families compared to census reports for the geographical region from which participants were recruited. This likely reflects previously reported barriers to diagnoses and services experienced by minority and/or lower class families (Magan˜a et al. 2012; Overton et al. 2007). This limitation certainly limits the generalizability of the current findings in these populations. Additionally, information about medication use and treatment history was not collected as a part of this study. It is possible that some variability in cognitive and adaptive functioning in individuals with ASD may be related to individual differences in treatment. Future research should take these variables into consideration. Last, results of the current cross-sectional design do not indicate whether adaptive functioning truly becomes more impaired with age relative to peers. Longitudinal studies are necessary to delineate whether this a true effect of age, or rather, a cohort effect.

Conclusion The SB5 and VABS Survey Form are commonly used to assess individuals with ASD. Results of these assessments are included as variables in research studies and are often used to inform diagnostic and treatment decisions. Yet, the performance of individuals with ASD on these measures has either been understudied or is poorly understood. The current study utilized a well-characterized sample of children and adolescents with ASD with a wide age range and diverse levels of functioning. With few exceptions, the current study replicated a previous detailed examination of


the SB5 (Coolican et al. 2008), which means that researchers and clinicians can apply these findings with greater confidence. Additionally, this replication provides validity to the current analysis of adaptive functioning. Cognitive profiles on the SB5 were observed to differ more between intellectual ability subgroups than language onset subgroups, suggesting that the practice of grouping individuals with ASD by intellectual ability may be more meaningful than grouping individuals by traditional diagnostic subgroups. Findings concerning adaptive behavior profiles detracted from previous claims of an autism profile on the VABS Survey Form and highlighted adaptive behavior (in particular, daily living skills) as an intervention target; especially in individuals who are cognitively able. Future research should examine the cognitive and adaptive profiles of the minority of individuals with ASD who present with significantly higher verbal IQs than NVIQs. In order to improve generalizability of findings in this area, researchers should strive to include traditionally underrepresented individuals with ASD and to better characterize participants in terms of treatment for autismrelated symptoms. Additionally, longitudinal examinations of cognitive and adaptive profiles in ASD are warranted in order to better understand the apparently contrasting developmental trajectories of these abilities. Acknowledgments We thank Autism Speaks and the Autism Genetic Research Exchange (AGRE) for financial support of this project. We gratefully acknowledge the contributions made by Southwest Autism Research and Resource Center research staff to data collection and coding. We also thank the families who participated in this study.

References American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Publishing. Billstedt, E. E. (2005). Autism after adolescence: population-based 13- to 22-year follow-up study of 120 individuals with autism diagnosed in childhood. Journal of Autism and Developmental Disorders, 35, 351–360. doi:10.1007/s10803-005-3302-5. Carter, A. S., Volkmar, F. R., Sparrow, S. S., Wang, J. J., Lord, C., Dawson, G., et al. (1998). The Vineland Adaptive Behavior Scales: Supplementary norms for individuals with autism. Journal of Autism and Developmental Disorders, 28, 287–302. Chakrabarti, S., & Fombonne, E. (2005). Pervasive developmental disorders in preschool children: Confirmation of high prevalence. The American Journal of Psychiatry, 162(6), 1133–1141. doi:10.1176/appi.ajp.162.6.1133. 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, 619–627. doi:10.1017/S0033291710000991. Coolican, J., Bryson, S. E., & Zwaigenbaum, L. (2008). Brief report: Data on the Stanford-Binet Intelligence Scales (5th ed.) in

155 children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 38, 190–197. doi:10.1007/s10803007-0368-2. Duncan, A. W., & Bishop, S. L. (2013). Understanding the gap between cognitive abilities and daily living skills in adolescents with autism spectrum disorders with average intelligence. Autism. Advance online publication. doi:10.1177/1362361313510068. Farley, M. A., McMahon, W. M., Fombonne, E., Jenson, W. R., Miller, J., Gardner, M., et al. (2009). Twenty-year outcome for individuals with autism and average or near-average cognitive abilities. Autism Research, 2, 109–118. doi:10.1002/aur.69. Fenton, G., D’Ardia, C., Valente, D., Vecchio, I., Fabrizi, A., & Bernabei, P. (2003). Vineland adaptive behavior profiles in children with autism and moderate to severe developmental delay. Autism, 7, 269–287. doi:10.1177/13623613030073004. Filipek, P. A., Accardo, P. J., Baranek, G. T., Cook, J., Edwin, H., Dawson, G., et al. (1999). The screening and diagnosis of autistic spectrum disorders. Journal of Autism and Developmental Disorders, 29, 439–484. doi:10.1023/A:1021943802493. Huynh, H., & Feldt, L. S. (1970). Conditions under which mean square ratios in repeated measures designs have exact F-distributions. Journal of the American Statistical Association, 65(332), 1582–1589. doi:10.2307/2284340. Kanne, S. M., Gerber, A. J., Quirmback, L. M., Sparrow, S. S., Cicchetti, D. V., & Saulnier, C. A. (2011). The role of adaptive behavior in autism spectrum disorders: Implications for functional outcome. Journal of Autism and Developmental Disorders, 41, 1007–1018. doi:10.1007/s10803-010-1126-4. Klin, A., Saulnier, C. A., Sparrow, S. S., Cicchetti, D. V., Volkmar, F. R., & Lord, C. (2007). Social and communication abilities and disabilities in higher functioning individuals with autism spectrum disorders: The Vineland and the ADOS. Journal of Autism and Developmental Disorders, 37(4), 748–759. doi:10.1007/ s10803-006-0229-4. Kraijer, D. (2000). Review of adaptive behavior studies in mentally retarded persons with autism/pervasive developmental disorder. Journal of Autism and Developmental Disorders, 30, 39–47. doi:10.1023/A:1005460027636. Lennen, D. T., Lamb, G. D., Dunagan, B. J., & Hall, T. A. (2010). Verbal prowess equals higher IQ: Implications for evaluating autism. Research in Autism Spectrum Disorders, 4, 95–101. doi:10.1016/j.rasd.2009.09.004. Lincoln, A. J., Allen, M. H., & Kilman, A. (1995). The assessment and interpretation of intellectual abilities in people with autism. In E. Schopler & G. Mesibov (Eds.), Learning and cognition in autism (pp. 89–117). New York: Plenum. Lord, C., Rutter, M., DiLavore, P. C., & Risi, S. (1999). Autism diagnostic observation schedule manual. Los Angeles, CA: Western Psychological Services. Magan˜a, S., Parish, S. L., Rose, R. A., Timberlake, M., & Swaine, J. G. (2012). Racial and ethnic disparities in quality of health care among children with autism and other developmental disabilities. Intellectual and Developmental Disabilities, 50, 287–299. doi:10.1352/1934-9556-50.4.287. Mayes, S. D., & Calhoun, S. L. (2003). Analysis of WISC-III, Stanford-Binet IV, and academic achievement test scores in children with autism. Journal of Autism and Developmental Disorders, 33, 329–341. Mervis, C. B., & Klein-Tasman, B. P. (2004). Methodological issues in group-matching designs: Levels for control variable comparisons and measurement characteristics of control and target variables. Journal of Autism and Developmental Disorders, 34, 7–17. doi:10.1023/B:JADD.0000018069.69562.b8. Nordin, V., & Gillberg, C. (1998). The long-term course of autistic disorders: update on follow-up studies. Acta Psychiatrica Scandinavica, 97, 99–108. doi:10.1111/j.1600-0447.1998.tb09970.x.

123

156 Overton, T., Fielding, C., & Garcia de Alba, R. (2007). Differential diagnosis of Hispanic children referred for autism spectrum disorders: Complex issues. Journal of Autism and Developmental Disorders, 37, 1996–2007. doi:10.1007/s10803-006-0349-x. Perry, A., Flanagan, H. E., Geier, J. D., & Freeman, N. L. (2009). Brief report: The Vineland Adaptive Behavior Scales in young children with autism spectrum disorders at different cognitive levels. Journal of Autism and Developmental Disorders, 39, 1066–1078. doi:10.1007/s10803-009-0704-9. Roid, G. H. (2003). Stanford-Binet Intelligence Scales (5th ed.). Rolling Meadows, IL: Riverside Publishing. Rutter, M., Le Couteur, A., & Lord, C. (2003). Autism diagnostic interview-revised (ADI-R) manual. Los Angeles, CA: Western Psychological Services. Siegel, D. J., Minshew, N. J., & Goldstein, G. (1996). Wechsler IQ profiles in diagnosis of high-functioning autism. Journal of Autism and Developmental Disorders, 26, 389–406. doi:10.1007/ BF02172825. Sparrow, S. S., Balla, D. A., & Cicchetti, D. V. (1984). Vineland Adaptive Behavior Scales, survey form manual. Circle Pines, MN: American Guidance Service.

123

J Autism Dev Disord (2015) 45:138–156 Sparrow, S. S., Cicchetti, D. V., & Balla, D. A. (2005). Vineland Adaptive Behavior Scales, survey forms manual (2nd ed.). Bloomington, MN: Pearson Inc. Szatmari, P., Bryson, S. E., Boyle, M. H., Streiner, D. L., & Duku, E. (2003). Predictors of outcome among high functioning children with autism and Asperger syndrome. Journal of Child Psychology and Psychiatry, 44, 520–528. doi:10.1111/1469-7610.00141. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention. (2014). Prevalence of autism spectrum disorder among children aged 8 years—Autism and developmental disabilities monitoring network, 11 sites, United States, 2010. Morbidity and Mortality Weekly Report, 63(2), 1–22. Volkmar, F., Siegel, M., Woodbury-Smith, M., King, B., McCracken, J., State, M., et al. (2014). Practice parameter for the assessment and treatment of children and adolescents with autism spectrum disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 53, 237–257. doi:10.1016/j.jaac.2013.10.013. Wing, L., Gould, J., & Gillberg, C. (2011). Autism spectrum disorders in the DSM-V: Better or worse than the DSM-IV? Research in Developmental Disabilities, 32, 768–773. doi:10.1016/j.ridd. 2010.11.003.

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