Comparisons of the Factor Structure and Measurement Invariance of the Spence Children’s Anxiety Scale—Parent Version in Children with Autism Spectrum Disorder and Typically Developing Anxious Children
Comparisons of?the?Factor Structure and?Measurement Invariance of?the?Spence Children's Anxiety Scale-Parent Version in?Children with?Autism Spectrum Disorder and?Typically Developing Anxious Children
Magdalena?Glod 0 1 2 3 4
Cathy?Creswell 0 1 2 3 4
Polly?Waite 0 1 2 3 4
Ruth?Jamieson 0 1 2 3 4
Helen?McConachie 0 1 2 3 4
Mikle?Don?South 0 1 2 3 4
Jacqui?Rodgers 0 1 2 3 4
0 School of Psychology, Newcastle University , 4th Floor, Ridley Building 1, Queen Victoria Road, Newcastle upon Tyne NE1 7RU , UK
1 Anxiety and Depression in Young People (AnDY) Research Clinic, School of Psychology and Clinical Language Sciences, University of Reading , Earley Gate, Reading RG6 6AL , UK
2 Institute of Neuroscience, Newcastle University, Level 3, Sir James Spence Institute, Royal Victoria Infirmary , Queen Victoria Road, Newcastle upon Tyne NE1 4LP , UK
3 Present Address: Greater Glasgow and Clyde NHS, Glasgow , Scotland , UK
4 Institute of Health and Society, Newcastle University, Level 3, Sir James Spence Institute, Royal Victoria Infirmary , Queen Victoria Road, Newcastle upon Tyne NE1 4LP , UK
The Spence Children's Anxiety Scale-Parent version (SCAS-P) is often used to assess anxiety in children with autism spectrum disorder (ASD), however, little is known about the validity of the tool in this population. The aim of this study was to determine whether the SCASP has the same factorial validity in a sample of young people with ASD (n = 285), compared to a sample of typically developing young people with anxiety disorders (n = 224). Poor model fit with all of the six hypothesised models precluded invariance testing. Exploratory factor analysis indicated that different anxiety phenomenology characterises the two samples. The findings suggest that cross-group comparisons between ASD and anxious samples based on the SCAS-P scores may not always be appropriate.
Anxiety?; SCAS-P?; Measurement invariance?; Autism spectrum disorder?; Anxiety disorders
* Jacqui Rodgers
Anxiety is a common health concern in children with
autism spectrum disorder (ASD), affecting between
11?84% (White et? al. 2009) compared to 3?24% of
typically developing children (Green and Ben-Sasson 2010).
A meta-analysis (Van Steensel et? al. 2011) reported that
nearly 40% of individuals with ASD display clinical levels
of anxiety and anxiety is one of the most common
comorbid psychiatric disorders in children with ASD (Simonoff
et? al. 2008). Furthermore, anxiety problems can lead to
increased maladaptive behaviour (Kim et? al. 2000),
unemployment, and chronic mental health difficulties among
young people with ASD (Farrugia and Hudson 2006).
Although the recognition of anxiety problems in ASD has a
long history, starting as early as with the first description of
autism by Kanner (1943), the assessment and treatment of
anxiety in individuals with ASD has only recently begun to
receive the empirical attention it needs and deserves
(Rodgers et?al. 2012; White et?al. 2009). There remains a critical
need for the development of valid and reliable assessment
measures to accurately identify anxiety in children and
young people with ASD.
MacNeil, Lopes and Minnes (2009) reported that young
people with ASD have higher levels of anxiety than
typically developing children and comparable levels of
anxiety to typically developing clinically anxious children. As
is the case among typically developing populations, some
forms of anxiety appear to be more common than others in
children with ASD (Van Steensel et?al. 2011); for example
specific phobias are more common than separation anxiety
and panic disorder. Sukhodolsky et? al. (2008) report the
prevalence rates for specific phobias, separation anxiety
and panic disorder in children with ASD aged between 5
and 17, as 31, 10.5 and 0.0%, respectively. Rates reported
for obsessive?compulsive disorder (OCD), social anxiety
disorder (SAD) and generalised anxiety disorder (GAD)
vary widely across studies in ASD (2.6?36.7% for OCD;
0.5?27.3% for SAD; and 1.2?45.2% for GAD; Van
Steensel et?al. 2011). Understanding this variability is important
and it may be that it is influenced by a number of factors,
including the specific challenges of accurately measuring
anxiety in ASD.
The presentation of anxiety in children with and
without ASD shares some common features, such as social
fears that are characteristic of social phobia (Settipani et?al.
2012). However, there may also be some unique aspects of
anxiety in ASD, for example there is evidence for an
association between anxiety and both sensory over-responsivity
(Ben-Sasson et?al. 2008; Green and Ben-Sasson 2010) and
impairment in social functioning in ASD (Bellini 2004,
2006). Thus, young people with ASD may be predisposed
to anxiety as a result of a range of ASD-specific factors.
Furthermore, there is also evidence that anxiety can
exacerbate some of the features of ASD, such as repetitive
behaviours (Sofronoff et?al. 2005). Kanner (1943) observed
that ?an insistence on sameness, and the repertoire of fixed
behaviours and routines? appeared to have a strong
association with anxiety (Kanner 1943, as cited in; Gillot et?al.
2001, p.? 277). Features of ASD and symptoms of anxiety
may however overlap and prove difficult to delineate
(Gjevik et?al. 2010). For example, repetitive behaviours seen in
ASD can be difficult to differentiate from the compulsive
behaviours found in OCD (Zandt et? al. 2009). Also
atypical anxiety symptoms have been reported to be associated
with ASD symptomatology, strengthening the overlap and
relationship of anxiety and repetitive and restricted
behaviours in ASD (Kerns et? al. 2014). Furthermore, Mikita
et? al. (2016) suggested putative links between
predisposing ASD traits and subsequent anxiety responses,
possibly underpinned by a distinct pathophysiological
mechanism. The authors indicated a possibility of distinguishing
a distinct nosological category of individuals with ASD
and comorbid anxiety that should be researched in its own
right. That highlights the need for measures that include
anxiety-related items that are specific to the
phenomenology of anxiety in ASD (Rodgers et?al. 2016). Rodgers and
colleagues (2016) have recently developed the first
autismspecific anxiety scale (ASC-ASD) with evidence of good
reliability and validity.
Generally, the assessment of anxiety in ASD has relied
on measures originally validated for use in typically
developing populations (White et? al. 2009). Given the distinct
challenges of measuring anxiety in ASD, the precision of
these instruments has been called into question. Van
Steensel, Deutschman and B?gels (2013) evaluated the
parentreport Screen for Child Anxiety-Related Emotional
Disorders (SCARED-71; Bodden et? al. 2009) for use in ASD.
They reported that although psychometric properties of the
measure were comparable for ASD and anxiety-disordered
groups, alternative cut-off scores were recommended for
young people with ASD. White, Schry and Maddox (2012)
provided mixed evidence for the reliability and validity
of both the Multidimensional Anxiety Scale for Children
(MASC) and the Child and Adolescent Symptom
Inventory-4 ASD Anxiety Scale when used with adolescents
diagnosed with high functioning autism. The authors found
that the measures had acceptable internal consistency, and
there was evidence of discriminant validity, however,the
youth self-report was found to have a questionable
validity. Kaat and Lecavalier (2015) evaluated the self- and
parent-reported revised child anxiety and depression scale
(RCADS) and a more recent version of the MASC among
youth with ASD and raised some concerns regarding the
construct validity of anxiety in ASD as measured by these
scales. More concerns were particularly raised about the
interpretation and validity of child/youth self-report
anxiety screening measures in the ASD group (Mazefsky et?al.
2011; White et? al. 2012). Moreover, acceptable internal
consistency, modest convergent validity, and
questionable divergent validity in separating anxiety from attention
problems in ASD on the RCADS suggested that more
convincing evidence is needed to use the tool in ASD (Sterling
The Spence Children?s Anxiety Scale-Parent (SCAS-P;
Spence 1998) is frequently used in ASD research (Chalfant
et? al. 2006; McConachie et? al. 2014; Rodgers et? al. 2012;
Russell and Sofronoff 2005; Sung et?al. 2011). The SCAS-P
is a parent-completed questionnaire for assessing the
severity of a range of anxiety symptoms. It has been reported to
be a reliable and valid tool for screening anxiety
symptoms in typically developing children (Nauta et? al. 2004).
The parent-report measure also has high correspondence
with the well-validated self-report Spence Children?s
Anxiety Scale (SCAS; Nauta et? al. 2004). Russell and Sofronoff
(2005) found both parent and child versions of the
questionnaire had high internal reliability in ASD samples. Findings
from the recent psychometric work done on the
questionnaire showed that there was overall moderately good
agreement between caregivers? and ASD children?s reporting of
anxiety symptoms using the SCAS-P and the SCAS (Magiati
et?al. 2014); and suggested that the SCAS-P could be a
useful screening tool for anxiety disorders in ASD (Zainal et?al.
2014). A recent systematic review of outcome measures used
in anxiety intervention studies for high-functioning children
with ASD suggested that the SCAS-P, its revised version, the
RCADS, and the SCARED had the most robust measurement
properties (Wigham and McConachie 2014). However, there
was little or no evidence for some aspects (e.g.
responsiveness to change and content validity). Little is yet known about
the reliability or validity of the SCAS-P as a measure of
anxiety in children with ASD.
It remains unclear whether the SCAS-P measures the
same constructs in ASD as it does in typically developing
clinically anxious children (without ASD). Moreover, the
subsequent question of whether this instrument measures
the construct in the same way, should also be addressed to
enable valid comparisons of observed scores across groups
to be made. Further investigation is required to enable
confidence that the scale functions in the same way across
In order to establish whether a given measure of a
particular latent construct (such as anxiety) performs similarly
across the groups, it has been suggested that
measurement invariance should be first performed (Vandenberg
and Lance 2000). Only then can meaningful comparisons
between groups be made as measurement invariance
analysis indicates whether the instrument measures the same
construct in the same way across different populations or
groups (Millsap and Kwok 2004).For example, Garnaat
and Norton (2010) assessed measurement invariance of the
Yale-Brown obsessive compulsive scale across four racial/
ethnic groups (namely, White, Black, Asian, and Hispanic).
They found generally stable properties although
highlighted some concern that some scales may underestimate
diagnosis of OCD in Black groups.
To our knowledge, there has been no attempt to use
measurement invariance to compare separate clinical
groups. The aims of this study were two-fold. Firstly, to
determine the factor structure for the SCAS-P in a sample
of young people with ASD and to compare it with the
factor structure derived from a sample of clinically-anxious
young people without ASD, and in the combined sample to
ensure adequate fit to consider invariance. Secondly, to use
measurement invariance techniques to determine whether
SCAS-P items function in the same way in children with
ASD and anxious children without ASD, in order to
establish whether cross-groups comparisons using the SCAS-P
are appropriate and meaningful. Due to concerns raised
about both validity and interpretation child/youth
selfreport anxiety measures in the ASD group, the parent
version of the SCAS was the main focus of this study.
The Spence Children?s Anxiety Scale-Parent Version
(SCAS-P; Spence 1998) is a 38-item checklist, where
parents rate the frequency of occurrence of anxiety
symptoms on a four-point Likert-type scale, ranging from 0
(never) to three (always). Thus, higher scores indicate
increased levels of anxiety. SCAS-P mean norms for the
total score in healthy children and young people range
between 11.8 and 16, increasing to 30.1 to 33 in anxiety
disordered children and adolescents (Nauta et? al. 2004).
The scale provides a total anxiety score as well as six
subscale scores developed to reflect symptoms characterized
by the Diagnostic and Statistical Manual of Mental
Disorders, Fourth Edition (DSM-IV): panic and agoraphobia;
separation anxiety; social phobia; physical injury fears;
OCD, and GAD. The proposed 6-factor structure has been
supported by confirmatory factor analyses (Nauta et? al.
2004). The SCAS-P is reported to have satisfactory to
excellent reliability and shows acceptable validity for
anxious children (Nauta et?al. 2004).
The study involved analysis of archival data pooled from
several different settings.
This group consisted of parents of 285 children and
adolescents with ASD, recruited from four sources. Most
children and adolescents (211participants, 181 male, mean age
in months = 147.95, SD = 24.1; range 8?16?years old) were
seen by health and education teams in the North East of
England, recruited through Daslne (Database of Children
with Autism Spectrum Disorder Living in the North East);
McConachie et? al. 2009). The second group consisted
of those who took part in the Beating Anxiety Together
(BAT) project (McConachie et? al. 2014), an intervention
programme created for children and adolescents with ASD
who also had comorbid high anxiety (21 participants, 20
male, mean age in months = 137.05, SD = 16.22; range
8.92?13.58? years old). The third group (19 participants,
16 male, mean age in months = 139.74, SD = 29.66; range
8.83?15.58?years old) took part in the UK part of the ?Fun
and Games? study investigating decision making styles
used by individuals with ASD (Boulter et? al. 2014; South
et?al. 2014). Finally, 34 participants (29 male, mean age in
months = 139.50, SD = 35.90; range 7.05?17.09? years old)
were recruited for a study based at Newcastle University,
UK, investigating the relation between executive
functioning, sensory processing and anxiety (Darus, unpublished
PhD). All children were diagnosed through a
multidisciplinary team assessment following the guidelines of the UK
National Autism Plan for Children (Le Couteur 2003). All
met criteria for ASD on the Autism Diagnostic Observation
Schedule (ADOS; Lord et?al. 2000), administered and rated
from video by trained raters who maintained over 80%
agreement with consensus ADOS ratings. In all cases, one
parent completed the SCAS-P, reporting on their child?s
symptoms of anxiety. The mean of the SCAS-P total score
was 33.85 (SD = 19.65) in the ASD sample. The means of
subscales were as follow: Panic attack and agoraphobia:
4.75 (SD = 4.48), separation anxiety: 5.97 (SD = 4.12),
physical injury fears: 4.87 (SD = 3.24), social phobia: 7.26
(SD = 4.90), obsessive compulsive: 5.00 (SD = 3.93),
generalized anxiety disorder: 6.00 (SD = 3.76).
The anxiety-disorder group included data from parents
of non-ASD, clinically anxious children and adolescents
referred to the Berkshire Child Anxiety Clinic, University
of Reading, UK. SCAS-P data from this sample was
collected from parents of 224 (150 male) children and
adolescents with a mean age in months of 144.92 (SD = 32.82,
range 8?17?years old). The mean total score of the SCAS-P
was 38.47 (SD = 17.02). The means of subscales were as
follow: panic attack and agoraphobia: 5.44 (SD = 4.93),
separation anxiety: 7.56 (SD = 4.26), physical injury fears:
4.60 (SD = 2.77), social phobia: 9.11 (SD = 4.35),
obsessive compulsive: 3.98 (SD = 3.57), generalized anxiety
disorder: 7.78 (SD = 3.63).
For that sample, on receipt of referral, parents completed
a number of screening questionnaires to ensure that
anxiety was the primary concern. This screening included the
Social Communication Questionnaire to screen for
characteristics of ASD (Rutter et? al. 2003). Where children
scored above clinical cut-offs (?15) further investigations
were conducted to ensure that children did not meet
criteria for ASD. All children met diagnostic criteria for a
primary anxiety disorder as established by the Anxiety
Disorders Interview Schedule for DSM-IV structured interview
(ADIS-C/P; Silverman and Albano 1996), a structured
diagnostic interview with well-established psychometric
properties (Silverman et? al. 2001). Where children met
symptom criteria for a diagnosis they were assigned a
clinical severity rating (CSR) ranging from 0 (complete absence
of psychopathology) to eight (severe psychopathology). As
is conventional, overall diagnoses and CSRs were assigned
if the child met diagnostic criteria on the basis of either
child or parent report, and the higher CSR of the two was
taken. Only those who met symptom criteria with a CSR of
four or more (moderate psychopathology) were considered
to meet diagnostic criteria. Assessors (psychology
graduates) were trained on the administration and scoring of the
ADIS and ADIS-C/P through verbal instruction, listening
to assessment audio-recordings and participating in
diagnostic consensus discussions. The first 20 interviews
conducted were then discussed with a consensus team, led by
an experienced diagnostician (Consultant Clinical
Psychologist). The assessor and the consensus team independently
allocated diagnoses and CSRs. Following the
administration of 20 child or 20 parent interviews, inter-rater
reliability for each assessor was checked, and if assessors achieved
reliability of at least 0.85 they were then required to
discuss one in six interviews with the consensus team (to
prevent inter-rater drift). Overall reliability for the team was
excellent. As different assessors interviewed the parent and
child simultaneously reliability figures for parent and child
report were calculated separately. Reliability for presence
or absence of diagnosis on the ADIS-C/P was kappa = 0.98
(child report), 0.98 (mother report); and for the CSR
intraclass correlation = .99 (child report), 0.99 (mother report).
Reliability for presence or absence of maternal diagnosis
on the ADIS was kappa = 0.97; and for the CSR intra-class
correlation = .99. Primary anxiety diagnoses for the
sample were generalised anxiety disorder (n = 55), social
phobia (n = 61), separation anxiety disorder (n = 40), specific
phobia (n = 41), OCD (n = 3), agoraphobia without panic
disorder (n = 9), anxiety disorder not otherwise specified
(ADNOS; n = 5), and panic disorder (n = 10).
All analyses were conducted using SPSS 21 (IBM SPSS
Statistics for Windows, Released 2012) and AMOS 21.0.0
(Arbuckle 2012) software programs. There were missing
values only in our anxious sample. There were no
particular patterns in the missing data, allowing the data to be
treated as missing completely at random. Participants with
over 20% of missing item level data were removed (n = 3)
to minimalize randomness in our dataset. For the remaining
participants the maximum likelihood estimation method of
data imputation was used to complete the dataset.
Confirmatory Factor Analysis
In order to determine the factor structure of the SCAS-P, a
confirmatory factor analysis (CFA), using structural
equation modelling, in AMOS, was conducted with data from
the anxious and ASD samples separately, and then in the
combined sample, in order to determine the best-fitting
factor structure and assess invariance. Six hypothesised
models were tested subsequently. Five were the DSM-IV-based
symptom models suggested by Nauta et? al. (2004)
including: (1) one factor, (2) six uncorrelated factors, (3) six
correlated factors, (4) six correlated factors and one higher
order factor, and (5) five correlated factors and generalized
anxiety as one higher-order factor. For anxiety disordered
children, as suggested by Nauta et? al. (2004), support was
found for six intercorrelated factors (separation anxiety,
generalized anxiety, social phobia, panic/agoraphobia,
OCD, and fear of physical injuries) and a model with
generalized anxiety as the higher order factor for the other five
factors. There is no support in the literature that either of
the models would fit ASD sample. The sixth model tested
in this study was based on work done by Jamieson et? al.
(unpublished thesis, 2012) who suggested that five
correlated factors (with GAD subscale excluded) might be the
best-fitting factor structure for children and adolescents
with ASD. All models were tested in order to establish
whether any of the hypothesised models would provide the
fitting factor structure for either of the samples.
Model fit was evaluated using established
recommendations identified as ?best behaved? on the basis of previous
research (Brown 2006, p.? 85; Hu and Bentler 1999). For
example, we followed recommendations that ?2/df ratio
(Bryant and Yarnold 1995) should be close to zero and
that root mean square error of approximation (RMSEA)
values close to 0.06 represent good fit (Hu and Bentler
1999), whilst values less than 0.08 are indicative of
acceptable fit, and values between 0.08 and 0.10 represent poor
model fit (Browne and Cudeck 1993). It is recommended
that the comparative fit index (CFI) is greater than 0.95, but
a level greater than 0.9 being acceptable (Hu and Bentler
1999). It is also recommended that the Tucker-Lewis index
(TLI) is greater than 0.90 to demonstrate good fit (Brown
2006). The non-significant Chi square (?2) statistic (Brown
2006) may be used an indicator of fit, however, because it
is greatly influenced by sample size (Stevens 2002), we did
not use in isolation from other recommended goodness of
fit indices. The Chi square difference test was also used to
compare competing models.
The measurement invariance technique can be implemented
by running a multi-group analysis of the factor structure
that underlies the data of two groups (Byrne and
Campbell 1999). The following sequence of four nested models
is usually tested (see Cheung and Rensvold 2002; Schmitt
and Kuljanin 2008): configural invariance; metric
invariance; scalar invariance; and residual (uniqueness)
invariance. In the configural invariance model, the same factor
structure is implied for two or more groups of participants
entered into the analysis. The values of the parameters (i.e.
factor loadings, intercepts, residual variances) may vary
across the groups, as no equality constraints are imposed.
In the metric invariance model whether the values of the
factor loadings are the same across groups is tested; hence
item loadings are constrained to be equal across groups.
Scalar invariance tests latent factor mean differences across
groups and is evaluated by constraining the intercepts of
measures to be the same across groups. In the residual
model items unique variances are constrained to be equal
across the two (or more) comparison groups. As suggested
by Chen (2007), suggested differences in both CFI (delta
CFI <0.01) and RMSEA (RMSEA <0.015) values were
considered when comparing two nested models e.g. metric
and scalar invariance.
Preliminary and?Descriptive Statistics
Examining the SCAS-P samples, anxious and ASD
participants did not significantly differ on age. A significant
difference was found for gender, with more female
participants in the anxious sample. However, this difference
represents the general sex ratio typical for the ASD population,
with more males than females diagnosed with the condition
(Werling and Geschwind 2013).
Confirmatory Factor Analysis
In the anxious, ASD and combined (both anxious and
ASD) samples, six models, including: (1) one factor, (2)
six uncorrelated factors, (3) six correlated factors, (4) six
correlated factors and one higher order factor, (5) five
correlated factors and generalized anxiety as one higher-order
factor, and (6) five correlated factors (with GAD subscale
excluded), were tested. The goodness of fit indices are
summarised in Table?1.
Overall, fit indices fell below the generally
recommended ranges for good fit in each model. Due to poor
models? fit subsequent invariance testing was not conducted
as there was not enough evidence to assess invariance.
Due to the poor model fit with any of the six hypothesised
models, we investigated the factor structure of the
SCASP in the anxious and ASD samples with exploratory factor
analysis (EFA). Parallel analysis and Velicer?s minimum
average partial (MAP) test were performed to determine the
number of components in the factor analyses. These
validated procedures are superior to the eigenvalues
greaterthan-one rule (O?Connor 2000). In the ASD and anxious
sample parallel analysis indicated an eight factor solution.
The MAP test indicated six factors in the ASD sample and
seven factors in the anxious sample. When differences in
test results emerge, optimal decisions should be made after
considering the results of both analytic procedures bearing
in mind that the MAP test tends to underextract the
number of factors, whereas parallel analysis tends to overextract
the number of factors (O?Connor 2000). In both the eight
and seven factor solutions in the ASD sample and the eight
Table 1 Fit indices for six
hypothesised models for the
anxious, ASD and combined
Recommended goodness of fit indices values demonstrating good model fit: ?2/df ratio close to zero,
RMSEA <0.6, CFI >0.95 and TLI >0.9 (Brown 2006; Hu and Bentler 1999)
factor solution in the anxious sample, one factor consisted
only of two items. The six factor solution in the ASD
sample and seven factor solution in the anxious sample were
considered as the most optimal. Maximum Likelihood
extraction with oblique rotation was used because high
correlations between the components were found (above 0.4
and below ?0.4 in both groups) (Tables?2, 3).
For both groups the social phobia factor was derived and
was very similar to the original social phobia factor (Nauta
et?al. 2004), with only item seven (?My child is afraid when
(s)he has to use public toilets or bathrooms?) not loading
onto that factor. Also an OCD factor was derived that was
similar to the original suggested by Nauta and colleagues
(2004), however it consisted of only four items in the ASD
group and three items in the anxious group. For the ASD
group the other four factors comprised mostly of items
belonging to the OCD, GAD, panic attack and
agoraphobia, and separation anxiety subscales. Interestingly, panic
attack and agoraphobia items loaded on two different
factors. One factor included four items (item 19 ?My child
suddenly starts to tremble or shake when there is no reason
for this?, item 25 ?My child feels scared if (s)he has to travel
in the car, or on a bus or train?, item 27 ?My child is afraid
of being in crowded places (like shopping centres, the
movies, buses, busy playgrounds)? and item 28 ?All of a sudden
my child feels really scared for no reason at all?) grouped
together with one GAD item (item 22 ?when my child has
a problem, (s)he feels shaky) and one physical injury item
(item 21 ?My child is scared of going to the doctor or
dentist?). A second factor related to the majority of the
physiological symptoms of anxiety (item 32 ?My child?s
complains of his/her heart suddenly starting to beat to quickly
for no reason?, item 12 ?My child complains of suddenly
feeling as if (s)he can?t breathe when there is no reason for
this?, item 30 ?My child complains of suddenly becoming
dizzy or faint when there is no reason for this? and GAD
item 18 ?when my child has a problem, (s)he complains of
his/her heart beating really fast?). These three items relate
to physiological symptoms of panic experience,
including the ability to recognise those symptoms (e.g. increased
heart beat) and communicate those changes in the body
functions to others.
A split in the original panic and agoraphobia factor was
also found in the anxious sample. Some of the items loaded
on to a physiological symptoms of anxiety factor (with
additional items from the original GAD factor) while the
Table 2 Rotated factor loadings in exploratory factor analysis of SCAS-P in ASD sample
Table 2 (continued)
Small closed places
other factor was more agoraphobia specific (e.g. item 34
?My child is afraid of being in small closed places, like
tunnels or small rooms?). Also OCD items separated into two
distinct factors in the anxious typically developing group,
with one relating to compulsions (e.g. item 37 ?My child
has to do certain things in just the right way to stop bad
things from happening?), the other to obsessive thoughts
(e.g. item 17 ?My child can?t seem to get bad or silly
thoughts out of his/her head?). Another factor that was
indicated for the anxious group comprised of various
separation anxiety, GAD and panic attack and agoraphobia items
(e.g. item 33 ?My child worries that (s)he will suddenly get
a scared feeling when there is nothing to be afraid of?). The
last factor consisted of two separation anxiety items (item
five ?My child would feel afraid of being on his/her own at
home? and item 14 ?My child is scared if (s)he has to sleep
on his/her own?) and one physical injury fears item (item
two ?My child is scared of the dark?). Items from across a
range of the original subscales loaded on to the other
factors in the anxious sample, with factor four including items
ranging from separation anxiety to being scared of
darkness, and factor five including items related to anxious
thoughts and factor seven encompassing specific phobias.
The first aim of this study was to determine the factor
structure for the SCAS-P in a sample of young people with ASD
and to compare it with the factor structure derived from a
sample of clinically-anxious young people without ASD,
and in the combined sample to ensure adequate fit to
consider invariance. However, due to poor model fit and
inability to find an adequate baseline model for further
betweengroup model testing, measurement invariance analyses
could not be performed. Inability to find a model with a
fixed number of factors in each group for the measure that
has an established factor structure for use with typically
developing samples was an unexpected outcome. Similarly,
White et?al. (2015) could not pursue the multigroup
invariance factor analysis on the MASC parent version (but could
on the MASC self-report), because the CFA undertaken on
the typically developing anxious youth did not confirm the
conventional MASC-P structure. It is important to bear in
mind that parents might not always be aware of all
anxiety-related behaviours that children exhibit, unless they
verbalize their subjective and individual experiences. It is
likely, particularly for our ASD sample, that parents were
not aware of some of the symptoms or their severity and
frequency. The reason why we could not find the baseline
model of the SCAS-P in the anxious sample is unknown.
Using EFA, a six-factor model was established for
the ASD sample, and a seven-factor model was found to
describe the anxious sample best. The findings here for
both groups differ from the SCAS-P factor structure
suggested by Nauta et?al. (2004), who found that six correlated
factors fit the data obtained from the parents/caregivers of
anxiety-disordered children best. Indeed, for the clinically
anxious group we only found partial support for the panic
attack and agoraphobia, OCD and social phobia factors.
However, even within these factors some anomalies were
found. Even less support for the original factor structure of
the SCAS-P was found in the ASD sample.
The study showed limited support for the original
factor structure of the SCAS-P. It is a novel, inconsistent with
previous emotional functioning and personality literature
(e.g., Hoelzle and Meyer 2009; Hopwood and Donnellan
2010; O?Connor 2002) finding. Some concerns, however,
have been raised previously with regards to the validity
of the SCAS-P, particularly of the GAD subscale for use
with typically developing children. Spence et? al. (2001)
argued that this sub-scale could indicate more negative
affect and autonomic responding than generalized anxiety,
and found little support for a separate GAD-subscale. The
content validity of the GAD subscale has been also
questioned because it lacks overt reference to excessive worry
(Chorpita et? al. 1997), which is considered to be a central
feature of GAD in childhood and adolescence. Our findings
support these concerns, as a distinct GAD factor was not
found in either our anxious or ASD samples. The
physical injury fear factor was also not established for either of
the samples. The reliability of the subscale, however, has
been questioned previously, with unacceptable to
questionable Cronbach?s alpha reported across community and
clinical samples in various countries (Arendt et? al. 2014;
Whiteside and Brown 2008; Zainal et? al. 2014). Although
in the RCADS, a revised version of the SCAS-P, the
measurement properties of GAD appeared to have improved
Table 3 Rotated factor loadings in exploratory factor analysis of SCAS-P in anxious sample
Table 3 (continued)
(Wigham and McConachie 2014), evidence on
psychometric properties of this tool remains patchy and requires
According to our findings, further work is needed on
the SCAS-P to establish its reliability and validity,
particularly when used with the ASD population. Zainal
and colleagues (2014) reported in their preliminary
investigation, the SCAS-P might be a useful
screening tool of anxiety in children with ASD when
assessing elevated anxiety symptoms and relying on the total
score. We suggest that a further caution is needed when
using the tool to assess particular anxiety subtypes and
make cross-groups comparisons between children with
ASD and children diagnosed with anxiety disorder based
on the SCAS-P scores. Although Wigham and
McConachie (2014) reported that the SCAS-P was one of the
tools to have the most robust measurement properties in
comparison to other measures, there was lack of evidence
for a number of reliability and validity characteristics of
An important limitation to this study is that our anxious
sample consisted of clinically referred individuals; and our
ASD sample consisted of participants recruited to various
studies, hence our sampling procedure might have impacted
our findings. Further qualitative work is recommended to
explore the validity of SCAS-P items in ASD samples. In
line with other studies we recommend that the GAD and
physical injury fears subscales require additional reliability
and validity checks across clinical and community
samples. Adaptation of the questionnaire is needed for reliable
and valid use with ASD individuals. Qualitative interviews
with parents should be conducted to better understand the
context and particular situations in which caregivers base
The SCAS-P has been developed and validated for use with
typically developing youth. To use the scale as a reliable
measure of anxiety in young people with ASD further work
is needed. Researchers and clinicians should not rely solely
on the scores obtained from the SCAS-P when assessing
anxiety symptoms in individuals with ASD. Further and
more systematic quantitative and qualitative research would
be required to turn the SCAS-P into a robust measure of
anxiety for use in ASD practice or research.
Acknowledgments The authors thank Zoe Hughes for help with
data extraction, and the research managers and assessors at the
participating research clinics.
Author contributions MG performed the statistical analysis,
participated in the interpretation of the data and drafted the manuscript;
CC participated in the design, helped with data acquisition and
drafting the manuscript; PW, RJ, HM, MDS participated in the design and
helped with data acquisition; JR conceived of the study, participated
in its design and interpretation, and helped to draft the manuscript.
All authors read and approved the final manuscript.
Compliance with Ethical Standards
Conflict of interest The authors declare that they have no conflict
Ethical Approval All procedures performed in studies involving
human participants were in accordance with the ethical standards of
the institutional and/or national research committee and with the 1964
Helsinki declaration and its later amendments or comparable ethical
Informed Consent Informed consent was obtained from all
individual participants included in the study.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted
use, distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a
link to the Creative Commons license, and indicate if changes were
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