Research in Autism Spectrum Disorders 7 (2013) 265–275
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Research in Autism Spectrum Disorders
Journal homepage: http://ees.elsevier.com/RASD/default.asp
Do spoken nonword and sentence repetition tasks discriminate language
impairment in children with an ASD?
Keely Harper-Hill a,b,*, David Copland a,b,1, Wendy Arnott b,2
a
b
University of Queensland Centre for Clinical Research, Level 3, Bldg 71/918 RBWH Campus, Herston, Qld 4029, Australia
University of Queensland, School of Health and Rehabilitation, St Lucia, Qld 4072, Australia
A R T I C L E I N F O
A B S T R A C T
Article history:
Received 31 May 2012
Received in revised form 31 August 2012
Accepted 31 August 2012
The primary aim of this paper was to investigate heterogeneity in language abilities of
children with a confirmed diagnosis of an ASD (N = 20) and children with typical
development (TD; N = 15). Group comparisons revealed no differences between ASD and
TD participants on standard clinical assessments of language ability, reading ability or
nonverbal intelligence. However, a hierarchical cluster analysis based on spoken nonword
repetition and sentence repetition identified two clusters within the combined group of
ASD and TD participants. The first cluster (N = 6) presented with significantly poorer
performances than the second cluster (N = 29) on both of the clustering variables in
addition to single word and nonword reading. The significant differences between the two
clusters occur within a context of Cluster 1 having language impairment and a tendency
towards more severe autistic symptomatology. Differences between the oral language
abilities of the first and second clusters are considered in light of diagnosis, attention and
verbal short term memory skills and reading impairment.
ß 2012 Elsevier Ltd. All rights reserved.
Keywords:
Language impairment
Attention skills
Reading impairment
Nonword repetition
Verbal short term memory
1. Introduction
Understanding the language abilities of children with autism spectrum disorders (ASD) is clinically important but
not straightforward. One complicating feature is the heterogeneity inherent within language profiles of this population
(Tager-Flusberg, 2006). This cross-sectional study employs cluster analysis to investigate the profiles of structural language
abilities in children with a diagnosis of ASD and an age-matched sample of children with typical language abilities.
The heterogeneity within the language skills of the ASD population is multifaceted. Research has clearly identified that
there are correlations between measures of IQ and language abilities (Kjelgaard & Tager-Flusberg, 2001). Thus some of the
heterogeneity may reflect the significant variability in the cognitive abilities of individuals with ASD. However, this
correlation is equivocal as a lack of association between language abilities and measures of IQ have also been identified in
smaller cohorts within the broader category of ASD (Kjelgaard & Tager-Flusberg, 2001).
Historically, the nature of the relationship between autism and language impairment has been of interest. In his early
investigation into language and cognition in 47 children with autism and developmental aphasia, Rutter (1974) identified a
small group of children (N = 5) whose skills and behaviours transcended both ‘dysphasic’ and autistic groups, with each of
* Corresponding author at: University of Queensland Centre for Clinical Research, Level 3, Bldg 71/918 RBWH Campus, Herston, Qld 4029, Australia.
Tel.: +61 7 3346 6110; fax: +61 7 3346 5599.
E-mail addresses: [email protected] (K. Harper-Hill), [email protected] (D. Copland), [email protected] (W. Arnott).
1
Tel.: +61 7 3346 6110; fax: +61 7 3346 5599.
2
Tel.: +61 7 3365 9725; fax: +61 7 3365 1877.
1750-9467/$ – see front matter ß 2012 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.rasd.2012.08.015
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K. Harper-Hill et al. / Research in Autism Spectrum Disorders 7 (2013) 265–275
these five children demonstrating a unique pattern of the features they shared with the autistic and dysphasic groups. Bishop
(1989) proposed that Rutter’s mixed group of children with features of both Autistic Disorder and developmental aphasia
suggested that the aphasia seen in the paediatric population may not be a unitary, discrete disorder.
Since Rutter’s (1974) investigations into ‘dysphasic children’, classifications for language impairment have progressed
and current understanding of these suggest that specific language impairment can share features (e.g., literal interpretation
of metaphorical language) with language impairments in associated disorders, such as autism spectrum disorders (Bishop,
Chan, Adams, Hartley, & Weir, 2000; Tager-Flusberg & Caronna, 2007). Evidence exists for a number of different language
skill profiles within the broad pervasive developmental disorder (PDD) classification. Within a large cohort of children with a
diagnosis of PDD, Tager-Flusberg (2006) identified a subgroup of children whose impaired language shared characteristics of
specific language impairment, involving grammatical morphology (e.g., past tense markers) and deficits in phonological
processing as measured through the repetition of nonwords. Research has also identified a group of children with a diagnosis
of PDD who presented with linguistic abilities which were commensurate with those of typically developing controls (TagerFlusberg, 2006; Tager-Flusberg & Cooper, 1999; Tager-Flusberg & Joseph, 2003).
Deficits in nonword repetition are reliably associated with specific language impairment (Botting & Conti-Ramsden,
2001; McArthur, Atkinson, & Ellis, 2009; Velez & Schwartz, 2010; Whitehouse, Barry, & Bishop, 2007). According to Baddeley
and Hitch (1974), the ability to repeat phonological information (such as nonwords) is one component of the ‘phonological
loop’. This facility provides a temporary store for phonological information which decays within seconds of being heard. It is
the second component of the phonological loop – subvocal rehearsal – which retains the phonological information in the
store while it is processed. As such, nonword repetition is a skill of phonological processing, and is considered to replicate the
tendency of young children to repeat novel words (Gathercole, Willis, Baddeley, & Emslie, 1994) as part of the acquisition of
new vocabulary (Baddeley, Gathercole, & Papagno, 1998).
Deficits in the ability to repeat spoken sentences have also been associated with specific language impairment (Archibald
& Joanisse, 2009). Botting and Conti-Ramsden (2003) used the Recalling Sentences subtest of the revised edition of the
Clinical Evaluation of Language Fundamentals (CELF-R; Semel, Wiig, & Secord, 1987) in conjunction with measures of
past tense use and nonword repetition to investigate differences in the language profiles of participants identified as having
specific language impairment, ASD, and pragmatic language impairment (PLI). Of the three measures used, the Recalling
Sentences subtest was most accurate in differentiating the language abilities of the ASD group from controls. This finding
suggests that the ability to repeat sentences may be sensitive in discriminating between language impairment in children
with ASD and their typically developing peers. Additionally, the pattern of differences between the performances of the ASD
and PLI groups on the Children’s Test of Nonword Repetition (Gathercole et al., 1994) supports the use of nonword repetition
to discriminate between different language abilities within a sample of children with ASD.
Whilst nonword repetition is one measure of phonological processing associated with language impairment, other
authors have included expressive phonology as a marker of language impairment in ASD (e.g., Rapin, Dunn, Allen, Stevens, &
Fein, 2009). It is important to note, however, that phonological processing impairment may also be implicated in children
who have intelligible speech and an alternative marker of this would be performance on nonword repetition as used by
Botting and Conti-Ramsden (2003). Accordingly, within the context of the present study, the repetition of nonwords will
provide a measure of phonological processing.
Traditionally, group comparisons on language measures can be made to consider language profiles of children with
an ASD and their typically developing peers. Despite sampling efforts, group comparisons can mask the variability
within either of the samples under comparison. Therefore, to address likely variability within the ASD sample of the
present study, a cluster analysis was undertaken. Cluster analysis is a statistical tool which identifies subgroups, or
clusters of participants within a larger group of participants, based on their performance on nominated, relevant
independent variables (Burns & Burns, 2009). One of the potential limitations of cluster analysis is that it will always
identify clusters (Field, 2000) and as such, the choice of clustering variables needs to be driven by theoretical or
‘conceptual’ considerations (Cornish, 2007). The relationship between language impairment and (1) phonological
processing skills and (2) the ability to repeat sentences is theoretically and empirically supported. Therefore, in the
current study spoken nonword and sentence repetition were the clustering variables used to explore the presence of
suboptimal language abilities in children diagnosed with an ASD.
We identified two studies which utilised cluster analysis to explore language subtypes in ASD: Lewis, Murdoch, and
Woodyatt (2007) and Rapin et al. (2009). In addition to comparing language performance between children with an ASD and
those with typical development, Lewis et al. (2007) used cluster analysis to investigate the language profiles within their
cohort of 20 children with an ASD. This cluster analysis was driven by the five linguistic index scores of the CELF-R (Semel
et al., 1987). Whilst post hoc analysis identified statistical differences among clusters and broad descriptions of each cluster
were provided, which particular clusters differed from one another – and on which measures – were not specified.
Rapin and colleagues included a measure of expressive phonology, The Photo Articulation Test to drive a cluster analysis
on a sample of 62 children with a diagnosis of Autistic Disorder. Rapin et al. (2009) completed post hoc analysis between
their four clusters using a score representing performance on the PAT and a composite measure of language competency
using two subtest scores from the CELF. The work of both the Rapin et al. (2009) and the Lewis et al. (2007) studies could be
further progressed by conducting post hoc comparisons using more specific language measures such as performance on
individual subtests from the CELF-4, as opposed to composite scores. Furthermore, by completing comparisons between
identified clusters using measures which had not driven the initial cluster analysis, greater understanding of the variations
K. Harper-Hill et al. / Research in Autism Spectrum Disorders 7 (2013) 265–275
267
within each group could be gained. In the present study, post hoc analysis was used to identify significant differences
between the clusters on performances on individual sub-tests of assessments including the Clinical Evaluation of Language
Fundamentals (Semel, Wiig, & Secord, 2003), the Test of Everyday Attention in Children (Manly, Robertson, Anderson, &
Nimmo-Smith, 1999) and the Woodcock-Johnson III Diagnostic Reading Battery (Woodcock, Mather, & Shrank, 2004).
Rapin et al. (2009) acknowledged that the lack of a control group in their study limited interpretation of the identified ASD
clusters in light of the variability present in typical development (Rapin et al., 2009). Lewis et al. (2007) completed cluster
analysis on their ASD group but not on the controls. Available evidence clearly points to a subgroup of children with a
diagnosis of ASD whose language is comparable to that of typically developing peers. If clustering variables associated with
language impairment were used to drive cluster analysis on a combined sample of typically developing children and children
with an ASD, it may be expected that those children with typical structural language would be clustered together, regardless
of their diagnosis. In light of this rationale and supported by a recent example of such an approach in ASD (McCrimmon,
Schwean, Saklofske, Montgomery, & Brady, 2012) we sought in the current study to conduct cluster analysis on the ASD and
typically developing participants as one, combined group.
In summary, investigations into the language abilities of children with ASD have identified groups of children with typical
language development as well as those with atypical language profiles. Cluster analysis has previously been used to
investigate language profiles in children with ASD but with a number of limitations as outlined above. The purpose of this
study was to consider whether sub-groups of language ability, as determined by performance on the Children’s Test of
Nonword Repetition (Gathercole et al., 1994) and the CELF-4 Recalling Sentences subtest (Semel et al., 2003), would be
evidenced within a group which consisted of children with a diagnosis of ASD and those with typical language development.
It was hypothesised that some of the children with a diagnosis of ASD would have language abilities comparable to their
typically developing peers. Similar language profiles would mean that any cluster of children with normal expressive and
receptive language abilities, would be comprised both children with an ASD and control children. Given the evidence of an
association between spoken nonword repetition and sentence repetition with language impairment, it was hypothesised
that any participants with language impairment would have a diagnosis of an ASD and be clustered together, in one or more
clusters.
2. Materials and methods
2.1. Participants
A total of 24 individuals aged 9–16 years were considered for participation in the ASD group for this study. Written
parental consent was obtained for all participants. Participants were provided with written information about the study. The
first author read through this written information with each participant and answered any questions prior to that participant
providing consent.
All participants had received a clinical diagnosis of a pervasive developmental disorder by a paediatrician or psychiatrist.
All participants underwent clinical standardised observation using Module 3 of the Autism Diagnostic Observation Schedule
(ADOS; Lord, Rutter, DiLavore, & Risi, 1998) and four were excluded from the study on the basis of not meeting the ADOS cut
off for an autism spectrum disorder. In total, 20 participants were included, five of whom met the threshold for a
classification of Autistic Disorder (AD) on the ADOS. Fifteen participants met the cut off for an autism spectrum disorder on
the ADOS.
A further 15 individuals aged 9–16 years of age were included as control participants. Exclusion criteria for control
participants were a diagnosis of neurological impairment or diagnosed intellectual impairment. No controls had reported
hearing impairment and all had normal, or corrected to normal, vision.
The mean age of the ASD group (16 males: 4 females) was 11 years, 7 months (SD = 28.59 months, range 9 years,
0 months–16 years, 9 months). The mean age of the control group (10 males: 5 females) was 11 years, 3 months
(SD = 24.94 months, range 9 years, 0 months–16 years, 3 months). Nonparametric comparisons were completed due to
violations of normality and these revealed no significant age differences between the groups (U = 146.50, p = .907). The male
to female ratio differed between the groups (x2 = 6.42, p = .011). A measure of socioeconomic status, median weekly income
based on locality of dwelling, revealed no difference between the groups (Australian Bureau of Statistics, 2006), U = 131.00,
p = .525.
2.2. Ethics
This project received ethical clearance from the University of Queensland’s Medical Research Ethics Committee,
Education Queensland and Catholic Education, Archdiocese of Brisbane.
2.3. Standardised language and cognitive assessments
2.3.1. Measures of spoken language
Expressive and receptive language skills were tested using the Clinical Evaluation of Language Fundamentals, 4th
Edition – Australian standardisation (CELF-4; Semel et al., 2003). Within the CELF-4 manual, it is reported that the
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assessment is sensitive to the language difficulties experienced by children diagnosed with Autistic Disorder (Semel
et al., 2003). The four core language subtests from the CELF-4 were administered to each participant. For children aged
12 years and under, these subtests were Concepts and Following Directions, Recalling Sentences, Formulating Sentences
and Word Classes. For participants who were aged over 12 years of age, the Concepts and Following Directions subtest
was replaced by the Word Definitions subtest as per manual instructions. These subtests are the most discriminating
and sensitive subtests in identifying language disorder according to Semel et al. (2003). Subtest standard scores had a
mean of 10 and a standard deviation of 3. The CELF-4 core score comprises the summation of subtest standard scores
which are converted to a standard score with a mean of 100 and a standard deviation of 15.
In addition to the above measures of oral language, receptive vocabulary was assessed using the third edition of the
Peabody Picture Vocabulary Test (PPVT; Dunn & Dunn, 1997). Scores were standard scores with a mean of 100 and a standard
deviation of 15.
2.3.2. Measures of reading
2.3.2.1. Single word reading. Single word reading was assessed using the Letter-Word Identification subtest (hereafter
referred to as the Word Identification subtest) of the Woodcock-Johnson III Diagnostic Reading Battery (WJ III,
Woodcock et al., 2004). The Word Identification subtest assesses single word reading and includes regular and irregular
words. It provides a measure of automatic word-identification and the ability to decipher phonic codes and is described
as targeting a lexical level of processing (Woodcock et al., 2004). It is possible that some responses to unknown items
may reflect sublexical processing. Raw scores were converted into standard scores with a mean of 100 and a standard
deviation of 15.
2.3.2.2. Nonword reading. Nonword reading was tested using the Word Attack subtest of the WJ III (Woodcock et al., 2004).
The Word Attack subtest measures grapheme–phoneme decoding abilities at a sublexical level of processing. It requires the
child to fluently read through progressively more difficult single, legal nonwords. Scoring is the same as for the Word
Identification subtest.
2.3.3. Measures of cognition
2.3.3.1. Nonword repetition. Nonword repetition was tested using the Children’s Test of Nonword Repetition (CNRep;
Gathercole et al., 1994). The CNRep measures an aspect of working memory, that is, phonological short term memory at
a sublexical level of processing. The CNRep requires children to repeat nonwords (e.g., ballop, altupatory) which are
spoken by the examiner and places demands upon working memory, specifically verbal short term memory as required
for the rehearsal of novel words. Administration requires the examiner to hold up a piece of paper to cover their mouth
and so reduce the use of visual information by the child. After an initial negative reaction from the first participant with
an ASD, the researcher spoke the words whilst looking down at the stimulus list. This adapted presentation was used for
all participants. This test provides a mean and range of 1 standard deviation from the mean for ages ranging from 4 years
to 9 years, 11 months. As the lower age range of participants in this study was 9 years, scores used were raw scores out of a
possible 40.
2.3.3.2. Nonverbal intelligence. Nonverbal intelligence was tested using the Ravens Coloured Progressive Matrices (CPM;
Raven, Raven, & Court, 1998) or the Raven’s Standard Progressive Matrices (SPM; Raven, Raven, & Court, 2000). This test
provides a measure of nonverbal intelligence based on pattern completion trials. The child is shown three (CPM) or five
(SPM) series of patterns or configurations, from which there is a missing segment. The child is required to choose which
segment from a choice of four pieces (CPM) or six–eight pieces (SPM) would complete the configuration. SPM equivalent
scores were converted from the CPM raw scores as per manual instructions (Raven et al., 2000). The raw scores were then
converted into standard scores (M = 100, SD = 15) using the norms provided in the Australian Manual of the Standard
Progressive Matrices (DeLemos, 1994).
2.3.3.3. Attention. Attention skills were tested using two subtests from the Test of Everyday Attention for Children (TEA-Ch;
Manly et al., 1999). The TEA-Ch comprises a battery of tasks, each of which assesses a different attention capacity. The first of
the TEA-Ch subtests used was a sustained auditory attention task (Score!). This requires the child to count the number of
times they hear a target sound which is repeated with different length intervals between each sound. Due to the long pauses
between the sounds of each trial, the task is not intrinsically engaging and therefore provides a measure of self-sustained
attention using non-linguistic stimuli. No auditory discrimination is required as there are no distractor sounds: it is a simple
sound counting task. The maximum number of sounds per trial is 15 and there are 10 trials in total. The ability to count to 15
is confirmed prior to commencement of two practice trials.
The second of the subtests from the TEA-Ch (Manly et al., 1999) was a selective/focused visual attention task (Skysearch).
This brief, timed task requires the child to find as many target spaceship pairs as quickly as possible within an array of
distractor spaceships. A motor control item is incorporated in calculations of search speed. Standard scores were calculated
for both the Score! and the Skysearch subtests (M = 10; SD = 3).
K. Harper-Hill et al. / Research in Autism Spectrum Disorders 7 (2013) 265–275
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2.4. Procedure
Assessments were administered to all participants in accordance with relevant test manual instructions, except for the
CNRep as detailed in Section 2.3.3.1. Administration of standardised assessments to the ASD group occurred over several
sessions (range 3–6 sessions, M = 3.7) according to the tolerance threshold for assessment demonstrated by each child.
Sessions typically lasted between 30- to 90-min in duration. The threshold for each session for each participant was
determined through clinical observation of the child’s behaviour and ability to attend to the tasks and remain seated. The
order of administration of these assessments was pseudorandom, that is, all children were not administered assessments in
the same order. All assessments were scored by the first author. When an assessment was not administered by the first
author, the participant’s performance was audio recorded and then transcribed by the first author.
No data was available for one ASD participant’s performance on the Word Definitions subtest as this participant could not
be motivated to complete the subtest. Data were not able to be obtained for three ASD participants on the Word Attack
subtest of the WJ III.
2.5. Analysis
All analyses were conducted using the IBM SPSS 20.0 for Windows. Performance of the ASD and control groups was
compared using a series of independent t-tests for scores which were normally distributed and Mann–Whitney analysis
when conditions of normality were violated. In analyses making multiple comparisons, the Bonferroni adjustment to the
alpha value (a/No. of comparisons) resulted in a = .004 (original alpha value of .05/13 comparisons).
With reference to the likely heterogeneity of the ASD group as discussed in Section 1, cluster analysis was conducted to
explore the possible existence of subgroups. The scores obtained by participants on two test items were used to inform the
cluster analysis. As these scores represent variables within the SPSS framework they are referred to as clustering variables.
Following on from Section 1, the clustering variables required utility in discriminating potential subgroups based on markers
for language impairment (Botting & Conti-Ramsden, 2003; Rapin et al., 2009). Thus, the clustering variables chosen were
standard scores from the Recalling Sentences subtest of the CELF-4 and the raw scores from the CNRep. Cluster analysis was
then completed on all 34 cases (ASD and TD participants) using Z score conversions.
Hierarchical clustering methods can be divisive or agglomerative. Agglomerative hierarchical cluster analyses produce
the most cohesive clusters (Burns & Burns, 2009). Cases are identified as similar according to their similarity coefficient, or
the Euclidean distance between them. Agglomerative approaches begin with each isolated case considered as a cluster and
then the two cases with the highest similarity clustered together to form a new cluster. The Euclidean distance between this
newly formed cluster and other cases is then recalculated and the procedure repeated. Small clusters may be clustered
together and eventually become larger clusters and ultimately one cluster. This process is represented pictorially in a
dendrogram.
Within agglomerative cluster analysis, further options regarding methods are available. These options typically refer to
how the similarity coefficients between clusters and other cases or clusters are calculated. In the simple linkage method, a
case which is added to an established cluster has the greatest similarity with only one of the members of that clusters.
Consequently, the potential for dissimilarity between some members of the same cluster is high (Field, 2000). In Ward’s
method the similarity coefficient is determined by the case that would result in the smallest increase in the variance of the
cluster (Burns & Burns, 2009; Cornish, 2007; Field, 2000).
Initially, a hierarchical agglomerative cluster analysis using Ward’s method was conducted. This provided a list of
agglomeration coefficients which are a numerical value for the amount of new information that is added to each cluster as
the number of clusters increases. The difference between these coefficients with the formation of each new cluster allowed
identification of the optimum number of clusters (Burns & Burns, 2009). Agglomerative coefficients were used to identify a
two cluster solution as optimum. Having identified a likely two cluster solution, the agglomerative cluster analysis using
Ward’s method was then repeated and a two cluster solution specified.
3. Results
3.1. Comparison between ASD and control groups
Table 1 details group performances on the standardised language and cognitive measures. No significant difference was
identified between the groups’ performance on any measure. On the majority of assessment tasks there were 20 ASD
participants and 15 participants in the control group. On the CELF-4, exceptions to these N values included the Concepts and
Following Directions subtest of the CELF-4 (ASD, N = 16; TD, N = 13) and the Word Definitions subtest (ASD, N = 3; TD, N = 2).
Given these numbers, it was not possible to compare the two groups on the Word Definitions subtest.
3.2. Cluster analysis
The first of the two identified clusters (Cluster 1) comprised six participants and the second cluster (Cluster 2) comprised
29 participants. The cluster solution provided by Ward’s method is detailed in the dendrogram in Fig. 1. Cluster 1 was only
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K. Harper-Hill et al. / Research in Autism Spectrum Disorders 7 (2013) 265–275
Table 1
ASD and control group performance on measures of standardised assessment.
Subtest
Group
NT
M (SD)
ASD
M (SD)
CELFC&FD
RS
FS
WC-R
WC-E
WC-T
WD
Core
PPVT
WJIII
WI
WA
CNRep
Ravens
TEA-Ch
Score!
Sky search
Statistic (df)
p-Valuea
7.25
9.15
9.50
10.05
8.45
9.45
12.67
94.60
106.70
(3.44)
(3.81)
(4.39)
(2.66)
(3.23)
(2.74)
(1.53)
(21.89)
(15.95)
9.62
10.40
11.53
10.41
11.40
11.27
11.00
104.73
106.60
(2.14)
(2.16)
(1.76)
(1.42)
(2.02)
(1.66)
(1.41)
(8.11)
(10.35)
t (27) = 2.16
t (31.05) = 1.23
U = 118.50
U = 118.00
t (33) = 3.09
t (33) = 2.26
–b
t (25.45) = 1.70
t (33) = .021
–b
.098
.983
98.59
97.76
33.50
99.20
(14.42)
(14.98)
(5.82)
(18.03)
104.00
106.46
37.87
101.73
(3.98)
(5.60)
(1.55)
(16.18)
t (21.79) = 2.02
t (20.87) = 2.22
U = 77.00
t (33) = 0.429
.056
.014
.106
.670
U = 88.50
U = 122.50
.037
.352
8.12 (4.20)
8.30 (3.57)
10.66 (2.94)
9.66 (1.87)
.040
.230
.289
.278
.004
.128
Note: CELF: Clinical Evaluation of Language Fundamentals, 4th edition; C&FD: Concepts & Following Directions; RS: Recalling Sentences; FS: Formulated
Sentences; WC-R: Word Classes-Receptive; WC-E: Word Classes-Expressive; WC-T: Word Classes-Total; WD: Word Definitions; PPVT: Peabody Picture
Vocabulary Test; WJIII: Woodcock-Johnson Reading Diagnostic Battery, 3rd edition; WI: Word Identification; WA: Word Attack; CNRep: Children’s Test of
Nonword Repetition; Ravens: Ravens Standardised Progressive Matrices; TEA-Ch: Test of Everyday Attention in Children.
a
Bonferroni adjusted p value, a = .004.
b
SPSS unable to calculate statistics due to small numbers.
comprised participants with a diagnosis of an ASD. Cluster 2 was comprised participants with ASD as well as participants
with typical development. Table 2 details the composition of each cluster.
The mean chronological age of Cluster 1 (M = 124.83 months, SD = 22.53 months) was not significantly different from the
age of Cluster 2 (M = 140.59 months, SD = 27.09 months; U = 48.50, p = .092).
3.2.1. Standardised language and cognitive measures: comparisons of clusters
Cluster performances on the standardised language and cognitive measures are detailed in Table 3. No participants in
Cluster 1 completed the Word Definitions subtest as per administration guidelines (Semel et al., 2003). This precluded any
comparison between clusters on the Word Definitions measure.
The CNRep and Recalling Sentences subtest of the CELF-4 were used as clustering variables, and the performance of
clusters on these measures was significantly different. In addition to the differences between the clusters on the clustering
variables, Cluster 1 and Cluster 2 differed significantly on their performance on the Word Classes-Receptive subtest of the
CELF-4 (U = 23.00, p = .004) and on measures of single word reading (Word Identification subtest; U = .000, p = .000) and
nonword reading (Word Attack subtest; U = .000, p = .002).
4. Discussion
Using two clustering variables known to be associated with specific language impairment – spoken nonword and
sentence repetition – we conducted cluster analysis on a sample of verbal children who either had a diagnosis of ASD or a
history of typical development. Comparisons between clusters were made on the basis of performance on a number of
language measures, attention, reading and nonverbal IQ measures. As hypothesised, clusters of children who differed in
terms of their language abilities were identified. Further to this, and also as predicted, a cluster of children with language
Table 2
Composition of cluster membership according to ADOS classification.
Cluster
Clu 1
Clu 2
Total
N
6
29
ADOS classification
TD
N
AD
N
ASD
N
5
–
1
14
Note: ADOS: Autism Diagnostic Observation Schedule; AD: Autistic Disorder; ASD: Autism Spectrum Disorder; TD: Typical Development.
–
15
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271
Fig. 1. Dendrogram detailing two cluster solution including both ASD and TD participants.
impairment was comprised only of children with an ASD. Additionally, as anticipated some of the participants with an ASD
had normal language abilities and clustered with control participants who had no diagnosis of ASD and typical language
development. The following Sections 4.1–4.3 consider profiles of the identified clusters in light of diagnosis, oral language
skills, attention abilities and reading skills in turn.
4.1. ASD and oral language impairment
Our analysis revealed two clusters. The first of these clusters, Cluster 1, was comprised six participants from the ASD
group. Table 1 details the impaired language ability profiles of this first cluster. What is particularly striking is that five of the
six children in Cluster 1 were the only participants in the entire ASD sample to meet the threshold for Autistic Disorder on the
ADOS (Lord et al., 1998). The sixth member of Cluster 1 had met the threshold for Autism Spectrum Disorder on the ADOS.
Meeting the threshold for Autistic Disorder rather than ASD reflects greater severity in the autistic symptomatology. This
finding is consistent with Whitehouse, Barry, and Bishop (2008) who reported that the severity of autistic symptomatology
and deficits in nonword repetition are positively associated.
K. Harper-Hill et al. / Research in Autism Spectrum Disorders 7 (2013) 265–275
272
Table 3
Comparisons of performances on standardised assessments between clusters.
Sub/test
Clusters
Comparison
Cluster 1
CELFC&FD
RS
FS
WC-R
WCE
WC-T
WD
PPVT
WJIII
WI
WA
CNRep
Ravens
TEA-Ch
Score!
Skysearch
Cluster 2
p-Valuec
M
SD
M
SD
U-value
5.17
5.33
5.50
7.50
6.17
7.67
–a
98.17
3.92
2.73
4.97
3.02
4.16
3.62
–a
17.88
9.13
10.39
11.09
11.09
10.22
10.65
12.00
108.41
2.13
2.57
2.39
1.47
2.25
1.87
1.58
12.28
27.0
12.50
31.00
23.00
33.00
40.50
–b
60.50
–b
.246
83.25
78.75
25.50
94.50
9.54
13.62
3.21
21.21
103.68
105.33
37.41
101.48
8.71
7.80
1.73
16.26
5.00
2.00
.00
75.50
.000
.002
.000
.614
5.53
6.00
5.35
5.05
9.97
9.48
3.11
2.06
43.00
52.00
.051
.120
.023
.001
.013
.004
.017
.040
Note: CELF: Clinical Evaluation of Language Fundamentals, 4th edition; C&FD: Concepts & Following Directions; RS: Recalling Sentences; FS: Formulated
Sentences; WC-R: Word Classes-Receptive; WC-E: Word Classes-Expressive; WC-T: Word Classes-Total; WD: Word Definitions; PPVT: Peabody Picture
Vocabulary Test; WJIII: Woodcock-Johnson Reading Diagnostic Battery, 3rd edition; WI: Word Identification; WA: Word Attack; CNRep: Children’s Test of
Nonword Repetition; Ravens: Ravens Standardised Progressive Matrices; TEA-Ch: Test of Everyday Attention in Children.
a
No participants in Cluster 1 completed the Word Definitions subtest.
b
No comparisons could be made between Cluster 1 and Cluster 2.
c
Bonferroni adjusted p = .004.
In addition to greater autistic symptomatology, the scores of Cluster 1 on the Concepts and Following Directions,
Recalling Sentences and Formulating Sentences subtests were between 1.5 and 2 standard deviations below the mean
(Semel et al., 2003). On the basis of this performance, Cluster 1 can be characterised as having a moderate oral language
impairment. Accordingly, the profile of children in Cluster 1 are most similar to the language impaired children identified in
the mixed clusters one and two of the Lewis et al. (2007) study.
Traditionally, differences between control and clinical samples are identified using group-wise comparisons. That
previous authors have recognised language profile subgroups within the ASD population (Bishop & Rosenbloom, 1987;
Bishop et al., 2000; Kjelgaard & Tager-Flusberg, 2001; Lewis et al., 2007; Rapin & Allen, 1983, 1987; Rapin & Dunn, 2003;
Rapin et al., 2009; Rutter, 1974, 1978; Tager-Flusberg & Joseph, 2003; Tomblin, 2011; Whitehouse et al., 2008) led
us to anticipate a certain degree of heterogeneity in the language profiles of the ASD group. Hence, cluster analysis
was undertaken and as an extension to previous studies which used cluster analysis to investigate the language abilities of
children with ASD, control participants were included in the analysis. Furthermore, and matching the approach taken by
McCrimmon et al. (2012), the cluster analysis was applied to the ASD and control participants as one, combined group.
The resultant mix of ASD and NT children in Cluster 2 of the current study provides further support for recognising a group
of children with ASD whose structural language abilities are not distinguishable from their typically developing peers on
standardised test performance. The normal language abilities of Cluster 2 occurred within the context of age-appropriate
attention, single word and nonword reading skills and typical performance on a measure of nonverbal intelligence. Thus
there were two profiles of language abilities for the children with an ASD in the current study: one impaired and one typical.
These two profiles were masked within the initial group comparison, highlighting a benefit of cluster analysis and the need
to carefully consider the heterogeneous nature of this population.
In Section 1 the relationship between deficits in verbal short term memory, as measured by sentence and nonword
repetition, and a neurodevelopmental disorder, specific language impairment, was introduced. That fact that the children in
Cluster 1 were clustered on nonword and sentence repetition task performance, and that they presented with language
impairment implicates verbal short term memory. However, while it may appear that the language impairment in ASD and
specific language impairment share the same deficit in verbal short term memory, there is some evidence to the contrary.
Based on differing error rates as a function of word length in a nonword repetition task, Whitehouse et al. (2008) concluded
that the underlying neurocognitive mechanism responsible for nonword repetition deficits in some children with an ASD
was different from the mechanisms responsible for those deficits in SLI.
4.2. Attention deficits
The profile of language impairment evident in our first cluster was accompanied by demonstrable deficits in selective
visual and sustained auditory attention but an average nonverbal IQ. Bishop and Norbury (2005) concluded that whilst
attention difficulties may exist in children with autism spectrum disorders, they were not associated only with autistic
K. Harper-Hill et al. / Research in Autism Spectrum Disorders 7 (2013) 265–275
273
symptomatology but with a range of developmental disorders. The findings from the current study are further consistent
with Bishop and Norbury (2005) in that deficits on attention tasks were found only in participants with poor language. The
comorbidity of language impairment and attention deficits within this ASD cluster suggests a potential association between
severity of autistic symptomatology, attention deficits and language impairment.
Some authors identify that attention to nonspeech auditory information by children with an ASD is intact whilst attention
to speech is specifically impaired (Ceponiene et al., 2003; Dunn, Vaughan, Kreuzer, & Kurtzberg, 1999; Klin, 1991).
Subsequent to orienting and attending to auditory information is the processing of that auditory information. It is reasonable
to suppose that a failure to attend to the information will compromise subsequent processing. One area of auditory
processing which has received considerable attention is rapid temporal processing.
Rapid temporal processing refers to the ability to process rapidly changing auditory information (Gaab, Gabrieli, Deutsch,
Tallal, & Temple, 2007) and has been demonstrated in some children with an ASD and language impairment (Cardy, Flagg,
Roberts, Brian, & Roberts, 2005), children with language impairment and children with attention deficits (Cardy et al., 2005;
Cardy, Tannock, Johnson, & Johnson, 2010; Sean, Heather, & Sam, 2011) Deficits in rapid temporal processing are thought to
interfere with the development of the phonological system and consequently, with oral and written language (Tallal & Fitch,
2005). McArthur and Bishop (2001) suggest that poor performance on rapid temporal processing tasks in children with
language impairment may be influenced by attention.
Whether poor performances on tasks of rapid temporal processing arise from deficits in attention is not straightforward.
Cardy et al. (2010) observed the deficits in rapid temporal processing in children with language impairment to be different
from those in children with attention deficits and questioned whether the same phonological processing mechanisms are
involved in different neurodevelopmental disorders. If an association between language impairment and nonverbal auditory
processing exists, then a relationship between psycholinguistic markers of language impairment and auditory response
tasks could be anticipated. However, Bishop et al. (1999), did not find a close association between the psycholinguistic
marker of nonword repetition and performance on an auditory response task. This finding suggests that the transactional
relationship between auditory attention and phonological processing in ASD may not be the same as that observed in typical
development, language impairment, and other neurodevelopmental disorders. Further investigation is required to explore
the degree to which deficits in sustained auditory attention impact upon phonological processing skills in ASD in comparison
to neurodevelopmental disorders.
An influence of sustained auditory attention and/or short term verbal memory on other results in this study may also be
evident. Cluster 1 performed more poorly than Cluster 2 on the receptive component of the Word Classes subtest, which provides
a measure of receptive semantics. That Cluster 1 and Cluster 2 did not differ on another measure of receptive semantics, the PPVT
(Dunn & Dunn, 1997) appears to be contradictory. There are two possible explanations for this finding. Firstly, the PPVT may have
overestimated the abilities of the children in Cluster 1 (all ASD/AD), which has been suggested previously (Burack, Iarocci,
Bowler, & Mottron, 2002; Mottron, Dawson, Soulieres, Hubert, & Burack, 2006). Alternatively, the difference may reflect the
requirement for sustained auditory attention or the verbal short term memory demands of the WC-R task.
4.3. Reading impairment
In addition to impairments with oral language and attention, performance by Cluster 1 on measures of single and
nonword reading was greater than one standard deviation below the mean and significantly poorer than the scores obtained
by Cluster 2. In cluster analysis, the order that clustering variables are entered into the analysis influences the outcome. In
this analysis, the first clustering variable was spoken nonword repetition. The contribution of this first clustering variable on
the identification of clusters which differed on measures of reading can be explained by the recognised relationship between
spoken nonword repetition and reading ability. Spoken nonword repetition strongly, but not completely, relies upon
sublexical processing (Baddeley, 2003) and as such depends upon short term phonological representations (Gathercole &
Baddeley, 1990). Children with poor reading ability are consistently distinguished from typical readers by their poor
performance on nonword repetition tasks (Gathercole & Alloway, 2006; Gathercole et al., 1994).
In a review on reading abilities in ASD, Ricketts (2011) concludes that reading comprehension is an area of weakness in
some children with ASD, but not all. Furthermore, such difficulties in ASD can be predicted by performance on both word
recognition (decoding) and oral language measures (Nation, Clarke, Wright, & Williams, 2006). That reading comprehension
is the product of both decoding and oral comprehension skills is consistent with the Simple View of Reading (Gough &
Tunmer, 1986). The present study provides measures of decoding ability and oral comprehension skills and Cluster 1
presented with moderate impairments on both of these measures. However, no direct measures of reading comprehension
were obtained and this precludes evaluating whether moderate impairments in decoding ability and oral language skills lead
to a similar degree of impairment in reading comprehension in this population. Nonetheless, the profile of Cluster 1 as
informed by their performance on single word and nonword reading tasks is consistent with a subset of children with ASD,
identified by Nation et al. (2006), who presented with impaired nonword reading and impaired comprehension abilities.
4.4. Clinical implications
The clinical implications of these research findings are clear. Interventions for children with a diagnosis of an ASD
typically target pragmatic impairments which are pervasive in this disorder (e.g., Bishop & Norbury, 2002). However, the
274
K. Harper-Hill et al. / Research in Autism Spectrum Disorders 7 (2013) 265–275
present findings suggest that there is a portion of these children for whom intervention targeting phonological and linguistic
processing is equally as important.
The results of the present study highlight the need for research to demonstrate which elements of attention and linguistic
development are most receptive to early intervention and in so doing, highlight the need for sensitive, early identification of
those children at risk of comorbid language impairment and attention difficulties within the context of an ASD. The impaired
performance of the children in Cluster 1 of this study suggests that those children with an ASD and language impairment
may be at risk of the same sequela of reading impairment as are children with language impairment and no diagnosis of ASD.
Thus, whilst there is some evidence that deficits in auditory and phonological processing skills may resolve or manifest at a
subclinical level, there is clearly a need for early intervention to minimise subsequent effects of these. Interventions
targeting early phonological processing skills, as required for literacy attainment, are advocated. Within the theoretical
framework of reading comprehension models such as the simple view of reading (Gough & Tunmer, 1986), the performance
of Cluster1 demonstrate impairments in both decoding and oral comprehensions skills and this has implications for the
educational needs for some children with an ASD. These findings also support the call by Nation et al. (2006) that the
heterogeneity within reading profiles in this population be recognised.
5. Conclusions and limitations
The modest sample size in the present study is a clear limitation. A precedent does exist however for using cluster analysis
with small samples (Hinchliffe, Murdoch, Chenery, Baglioni, & Harding-Clark, 1998; Laasonen, Service, Lipsanen, & Virsu,
2012). The subsequent number of participants in Cluster 1 was small and any conclusions are tentative at this stage, pending
confirmation from larger studies.
The large scale replication of the current findings would help to confirm the utility of spoken nonword and sentence
repetition as clustering variables for the identification of those children on the autism spectrum with impairments in reading
and oral language. Whilst there are no current means for determining the validity or reliability of identified clusters (Burns &
Burns, 2009), a large sample would have enabled the cohort to be split, and cluster analysis conducted on each half of the
sample. Similarity of the results for each cluster could then be confirmed. In addition, future cluster analyses on the oral
language abilities and reading skills of children with an ASD would benefit from the use of a measure of reading
comprehension and a standardised measure of nonword repetition which is applicable across the age range of the sample.
In summary, the present study investigated an ASD cohort who performed within the normal range on measures of IQ,
attention and language. When the ASD and control cohorts were combined and subjected to cluster analysis, two clusters of
children were identified. Children in the first, ASD-only, cluster demonstrated moderate impairments in reading, receptive
and expressive language and on measures of attention. These impairments occurred within the context of typical nonverbal
intelligence. Contrastively, the second cluster consisted of children without impairments on any of these measures.
Therefore, in this sample of children with and without ASD, spoken nonword and sentence repetition discriminated a cluster
of children who presented with language impairment as well as deficits in attention and reading. The findings reiterate the
issue of heterogeneity in the ASD population, particularly for the purposes of research and the provision of appropriate
interventions.
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