Contents lists available at ScienceDirectResearch in Autis

Contents lists available at ScienceDirect
Research in Autism Spectrum Disorders
journal homepage: www.elsevier.com/locate/rasd
Self-reported emotion regulation in children with autism
spectrum
disorder, without intellectual disability
Talia Burtona,*, Belinda Ratcliffea,b, James Collisona, David
Dossetorb,
Michelle Wongb
a School of Social Sciences and Psychology, Western Sydney
University, Bankstown Campus, Locked Bag 1797, Penrith,
NSW 2751, Australia
b Department of Psychological Medicine, The Children’s
Hospital at Westmead, Locked Bag 4001, Westmead, Sydney,
NSW 2145, Australia
A R T I C L E I N F O
Number of reviews completed is 2
Keywords:
Autism spectrum disorder
Emotion regulation
Social skills
Mental health
Autism severity

A B S T R A C T
Background: Emotion regulation (ER) may be a critical
underlying factor contributing to mental
health disorders in children with Autism Spectrum Disorder
(ASD). Scant literature has utilised
self-reported ER in children with ASD and explored the
association between mental health and
social skills. This study explored the association between self-
reported ER skills, and parent/
teacher proxy reports of ER, social skills, autism severity and
mental health.
Method: The pre-existing data set included a community sample
of 217 students aged seven to
13-years (Mage = 9.51, SD = 1.26; 195 Male, 22 Female) with
ASD. The study employed a
correlational design, whereby existing variables were explored
as they occurred naturally (Hills,
2011). Children self-rated ER, while parents and teachers rated
ER, social skills, and mental
health difficulties via standardised questionnaires.
Results: Multiple regression analyses were conducted separately
for parent and teacher reports.
The linear combination of parent-reported emotion regulation,
social skills, autism severity, and
child-reported ER accounted for 46.5 % of the variance,
compared to 58.7 % for the teacher-
report analysis. Social skills appeared to be a stronger predictor
of mental difficulties than
emotional regulation irrespective of source.
Conclusions: The current study suggests self-reported ER to be
a significant contributor to mental
health when in isolation. However, in the context of social
skills and autism severity, ER is no
longer a significant contributor in a child and adolescent
community sample, in determining

mental health. This suggests, that for children aged seven to 13-
years with ASD, without ID, to
reduce mental health difficulties, social skills may be the focus
of intervention, with some focus
on ER ability.
1. Introduction
Autism Spectrum Disorder (ASD) is a neurodevelopmental
disorder characterised by difficulties in two core domains;
social-
communication and restricted/ repetitive patterns of behaviour,
interests or activities (American Psychiatric Association, 2013).
Compared to their typically developing (TD) peers, children
with ASD have difficulties in social-emotional reciprocity, non-
verbal
social-communicative behaviours, and developing and
maintaining relationships. They may also experience an
inflexible adherence
to routine, repetitive motor movements, and highly restricted/
fixated interests (American Psychiatric Association, 2013).
https://doi.org/10.1016/j.rasd.2020.101599
Received 7 January 2020; Received in revised form 8 May
2020; Accepted 3 June 2020
⁎ Corresponding author.
E-mail address: [email protected] (T. Burton).
Research in Autism Spectrum Disorders 76 (2020) 101599
Available online 13 June 2020
1750-9467/ © 2020 Elsevier Ltd. All rights reserved.
T

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Approximately 1.4 % of Australian school children have a
diagnosis of ASD (Aspect, 2018). Of those, some 72 % have at
least one
co-morbid mental health disorder (either internalizing or
externalizing), compared to just 14 % of TD children (Gurney,
McPheeters,
& Davis, 2006; Leyfer et al., 2006; Simonoff et al., 2008). Poor
social skills and autism severity may be significant contributing
factors
towards the very high level of mental health difficulties within
the ASD population (Ratcliffe, Wong, Dossetor, & Hayes, 2015;
Eussen
et al., 2012; Greenlee, Mosley, Shui, Veenstra-VanderWeele, &
Gotham, 2016; Mazzone et al., 2013; Ratcliffe, Wong, Dossetor,
&
Hayes, 2014; Van Steensel & Heeman, 2017). For example,
social skills explain between 49.7%–54.7 % of the variance in
mental
health scores among school-aged children with ASD, based on
parent- and teacher-reports (Ratcliffe et al., 2015). In addition,
while
some studies suggest greater autism severity indicates greater
mental health difficulties (Ratcliffe et al., 2015), and others
suggest
reduced severity indicates greater mental health difficulties
(Eussen et al., 2012; Mazurek & Kanne, 2010; Van Steensel &

Heeman,
2017), it is largely agreed upon that autism severity (i.e.,
whether this be greater or lower) plays a significant role in
mental health
outcomes.
Emerging evidence suggests that emotion competence skills
(e.g., emotion regulation) is also a critical capacity underlying
and
driving social communicative competence (Laurent & Rubin,
2004), which should also be considered as an underlying factor
con-
tributing to poor mental health in ASD (Rieffe et al., 2011).
Emotion regulation (ER) is the ability to control or modify the
intensity
and duration of an emotional response to achieve goal-directed
behaviour (MacDermott, Gullone, Allen, King, & Tonge, 2010;
Mazefsky et al., 2013). Successful ER thus requires an
individual to firstly recognise their emotional state (i.e.,
“emotional self-
awareness”), access self-soothing strategies for strong levels of
negative emotions or high arousal (i.e., “emotional control”),
and
simultaneously maintain progress in current activities while
behaving in a socially or situationally appropriate manner (i.e.,
“si-
tuational responsiveness”) (Berkovits, Eisenhower, & Blacher,
2016; MacDermott et al., 2010).
Children with ASD have significantly higher levels of emotion
dysregulation compared to their TD peers (Ashburner, Ziviani,
&
Rodger, 2010; Jahromi, Bryce, & Swanson, 2013; Samson et al.,
2014). Teacher-reports indicate children with ASD have higher
levels
of difficulties within behavioural, emotional, and academic skill

domains compared to their TD peers (Ashburner et al., 2010).
Emotional difficulties were notably expressed through frequent
temper outbursts, a tendency to cry, rapid mood changes, and
be-
coming easily frustrated when demands were not immediately
met. Parent-reports indicate a similar pattern, noting children
with
ASD have significantly lower ER than their TD peers (Jahromi
et al., 2013; Samson et al., 2014; Shields & Cicchetti, 1997).
Research
also indicates individuals with ASD to more frequently utilise
“maladaptive” (e.g., avoidance, negative rumination), and fewer
“adaptive” (e.g., acceptance, cognitive reappraisal) ER
strategies, compared to TD individuals (Mazefsky, Borue, Day,
& Minshew,
2014; Rieffe et al., 2011; Rieffe, De Bruine, De Rooij, &
Stockmann, 2014). Overall, with few exceptions (Pouw, Rieffe,
Stockmann, &
Gadow, 2013), research indicates ER difficulties to be a
significant issue in children with ASD (Cai, Richdale,
Uljarevic, Dissanayake,
& Samson, 2018), across differing age-groups, with some with
some even positioning poor ER as a core diagnostic feature of
ASD
(Ashburner et al., 2010; Samson et al., 2014).
In TD children, there is a strong link between poor ER skills
and the development of internalising disorders, such as anxiety
and
depression (Rieffe et al., 2011). As previously outlined, there is
a significantly high prevalence of internalising and
externalising
disorders in children with ASD, compared to TD children
(Leyfer et al., 2006; Simonoff et al., 2008; Van Steensel &
Heeman, 2017).
As poor ER has been observed to be a significant factor in ASD

(Ashburner et al., 2010; Samson et al., 2014), it could be that
poor ER
significantly contributes to the high prevalence of mental health
difficulties in children with ASD. For example, Berkovits et al.
(2016)
found 5-to 7-year-old children with ASD and poor ER, indicated
poorer social skills and increased internalising and externalising
behaviours across a ten-month time span. In addition, Jahromi
and colleagues (2013) found greater ER to be a significant and
positive
prediction of prosocial peer engagement, in 6- to 16-year-old
children. ER may therefore sit alongside social skills and ASD
symptom
severity as significant factors in determining mental health
(Laurent & Rubin, 2004). This recognition of ER playing an
important role
in mental health in ASD is reflected in the shift of focus from
targeting social skills development to emotion-based social
skills
development in interventions for children with ASD (Ratcliffe
et al., 2014; Samson et al., 2014).
1.1. Issues in the assessment of emotion regulation
The recommended “gold standard” approach to measuring ER in
children in both research and clinical practice is a multi -method
assessment (Mash & Hunsley, 2005; Mazefsky et al., 2013).
Multiple methods may be utilised to investigate ER, such as
direct,
naturalistic observation of behaviour, informant report (i.e.,
parent- or teacher-report), self-report, physiological measures
and open-
ended/ qualitative measures (Mazefsky et al., 2013; Weiss,
Thomson, & Chan, 2014).
Self-report in adults with ASD is supported for assessing

alexithymia (i.e., difficulty identifying and expressing emotion;
Berthoz &
Hill, 2005), internalising symptoms (Williams, 2010), quality of
life, and ASD traits (Baron-Cohen et al., 2011), assuming the
in-
dividual is of at least average intelligence (Hesselmark,
Eriksson, Westerlund, & Bejerot, 2015). TD children between
the ages of 10-
and 12-years are increasingly able to reflect upon their own
internal states and emotions (Harris, 1989; Rieffe et al., 2011).
As a core
difficulty in individuals with ASD is identifying and reflecting
on one’s own emotions, it is unclear whether ER self-report
measures
for children with ASD are valid (Mazefsky & White, 2014;
Rieffe et al., 2011). Nevertheless, self-reports are extremely
important in
both the assessment and treatment phases, as they provide data
reflective of the individual’s own experience (Hesselmark et al.,
2015).
Agreement between child- and parent/teacher-report has been
found to be greatest during middle childhood (i.e., between 6–
11-
years) and significantly greater for externalising problems,
when compared to internalising problems and social skills, for
children
with ASD and TD children (Renk & Phares, 2004; Stratis &
Lecavalier, 2015). While some studies have reported
inconsistency across
self- and parent-reports regarding “negative emotions” (e.g.,
nervous and upset; Samson, Harden, Lee, Phillips, & Gross,
2015), other
T. Burton, et al. Research in Autism Spectrum Disorders 76
(2020) 101599

2
studies have indicated good correspondence between self- and
parent-report (Khor, Melvin, Reid, & Gray, 2014), with Rieffe
et al.
(2011) emphasising the importance of recognising the child’s
perspective of their emotional experience, regardless of
discrepancies.
To gain a comprehensive picture of the child, the use of
multiple informants is critical, and considered a “gold standard”
approach
(Mash & Hunsley, 2005), as certain patterns of behaviour may
be present or absent depending on contextual factors (Stratis &
Lecavalier, 2015). A significant limitation of previous studies
measuring ER has been a lack of employing the “gold standard”
approach, with up to 75 % of studies including only one
methodological approach to ER measurement (Mazefsky et al.,
2014; Weiss
et al., 2014). Other limitations include utilising small sample
sizes (e.g., 31 participants; Jahromi et al., 2013; Khor et al.,
2014),
male-only samples (Cederlund, Hagber, & Gillberg, 2010; Pouw
et al., 2013), or samples that do not differentiate between ASD
with
and without intellectual disability for statistical analyses
(Berkovits et al., 2016). In addition, some studies that claim to
measure ER
fail to provide an adequate definition of the concept or simply
measure constructs of ER, such as, “coping style” (Cai et al.,
2018;
Mazefsky et al., 2014; Pouw et al., 2013; Rieffe et al., 2014;
Weiss et al., 2014).

1.2. The current study
Together, these studies reveal a notable gap in our
understanding of ER and its assessment, particularly among
children with ASD,
and how poor ER may be a core factor in developing mental
health difficulties. Further, scant literature has explored ER
(self and
teacher/ parent) alongside social skills in determining mental
health difficulties. This study will therefore investigate the
association
between ER ability, social skills, autism severity, and mental
health in a community sample of school-aged children. To
improve on
the limitations of existing studies, we will use a multi-informant
method, clearly define our sample as children without co-
occurring
ID and utilise a large sample size (i.e., > 200 participants).
More specifically, the study will explore self-reported ER, and
how this compares to parent/teacher-reported ER, social skills,
autism severity and mental health. We hypothesise that self-
reported ER will be of similar agreement to parent/teacher-
reports of ER,
as research indicates agreement to be greatest during middle
childhood, and higher than TD peers (Stratis & Lecavalier,
2015).
Secondly, it is anticipated that poorer self-reported ER ability
will be associated with increased mental health difficulties,
poorer
social skills, and greater autism severity for both parent- and
teacher-reports. Finally, we predict that self- and
parent/teacher-
reported ER would be significant contributors of mental health
difficulties among children with ASD alongside social skills
and autism

severity.
2. Method
2.1. Design
This study employed a natural-groups correlational design
(Hills, 2011). Dependent variables included ER measured via
the
Emotion Regulation Index for Children and Adolescents
(ERICA; (MacDermott et al., 2010) and the Emotions
Development Ques-
tionnaire (EDQ; Wong, Heriot, & Lopes, 2009), social skills
measured via the Social Skills Improvement System- Rating
Scales (SSIS-
RS; Gresham & Elliot, 2008), autism severity and social
responsiveness measured via the Social Responsiveness Scale
(SRS;
Constantino, 2002), and mental health difficulties measured via
the Strengths and Difficulties Questionnaire (SDQ; Goodman,
1997a).
2.2. Participants
Participants included 217 students aged 7- to 13-years (Mage =
9.51, SD = 1.26; 195 Male, 22 Female) with ASD, drawn from
the
Emotion-Based Social Skills Training (EBSST) in Schools study
databank, held and collected by the Children’s Hospital at
Westmead,
Australia. A range of demographic, social skills, and mental
health data is held within the confidential, non-identifiable
databank,
based on parent- and teacher-report, for students with ASD with
and without mild ID. All children in the databank attended NSW
Department of Education and Communities Primary Schools,

making the sample non-clinical and school-based.
As the EBSST trial pre-dated the most recent edition of the
Diagnostic and Statistical Manual of Mental Disorders (i.e.,
DSM-5;
APA, 2013), participants in the sample were included if they
had a confirmed or suspected diagnosis of ASD based on
diagnostic
nosology from the text-revised fourth-edition (i.e., DSM-IV-TR;
American Psychiatric Association, 2000). Hence, participation
was
limited to participants with a diagnosis of Autistic Disorder,
Asperger’s Disorder, and Pervasive Developmental Disorder -
Not
Otherwise Specified (American Psychiatric Association, 2000),
as assessed by a registered psychologist or specialist medical
prac-
titioner. Participants were classified as having no ID present,
with an Intelligence Quota (IQ) of above 70 on a standardised
measure
of cognitive ability, with no concurrent deficits in adaptive
behaviour (DSM-5; American Psychiatric Association, 2013).
Parent-
reported scores on the SRS (Constantino, 2002) were in the
severe range for all participants, indicating a strong association
with an
ASD diagnosis (Murray, Mayes, & Smith, 2011; Wilkinson,
2010a, 2010b). Informed consent was obtained from all
parents/guardians
of children involved in the EBSST in Schools trial, as well as
school principals, counsellors, and teachers.
2.3. Materials
2.3.1. Demographic information
Brief background information, including age, gender and school

grade was obtained via surveys from school counsellors,
teachers,
(2020) 101599
3
and parents.
2.3.2. Emotion regulation
The ERICA (MacDermott et al., 2010) is a 16-item self-report
questionnaire designed to assess ability to regulate emotions for
children and youth aged 9−16. It is a revised adaptation of the
Emotion Regulation Checklist for Adolescents (ERCA;
Biesecker &
Easterbrooks, 2001) and employs a 5-point Likert scale (i.e.,
“Strongly Disagree” to “Strongly Agree”). The questionnaire
yields an
overall emotion regulation index score with higher scores
indicating greater ability. Subscales of the ERICA are:
Emotional Control
(EC), Emotional Self-Awareness (SA), and Situational
Responsiveness (SR). The ERICA has demonstrated good test-
retest reliability,
internal consistency and convergent and construct validity in a
sample of youth aged 9–16. (MacDermott et al., 2010).
Presently,
Cronbach’s α = 0.80.
The EDQ (Wong et al., 2009) is a 29-item informant report,
comprising of both parent (EDQ-P) and teacher (EDQ-T) forms
to rate

the child’s emotional competence. Items on the scale include
questions such as, “How often can your child choose an
appropriate
strategy to manage their feelings?”. The teacher form includes
the same 29-items utilised for the parent form, with the
statement
“your student” replacing “your child”. The EDQ employs a 5-
point response scale (i.e., “Never” to “Always”) and includes a
sixth
option of “Don’t Know”. It has demonstrated excellent internal
consistency for parents and teachers in a sample of children
aged 7- to
13-years (Ratcliffe et al., 2014).
As a comprehensive measure of emotional competence, the EDQ
also includes questions that relate to the abilities of the parent/
teacher (e.g., “Do you talk to your student about their good
feelings?”). However, these items are irrelevant to the current
application
as a measure of emotional regulation and retaining them in the
analyses may skew the result. As per Wong et al. (2009), those
items
relevant to the parent or teacher emotion coaching style (i.e.,
items 1–7) were excluded from the analysis to ensure the EDQ
served as
a measure of ER. Presently, parent-report indicates Cronbach’s
α = 0.92 (29-items) and teacher-report indicates Cronbach’s α =
0.91
(30-items).
2.3.3. Social skills
The SSIS-RS (Gresham & Elliot, 2008) is a standardised, norm-
referenced assessment of social skills (children aged 3–18),
comprised of both parent (SSIS-P) and teacher (SSIS-T) forms.
The rating scale yields an overall social skills score (i.e., higher

scores
indicating better skills), which includes the subscales of
communication, cooperation, assertion, responsibility, empathy,
engage-
ment, and self-control. The SSIS-RS indicates good internal and
test-retest reliability, and adequate criterion and convergent
validity
(Gresham & Elliot, 2008). Presently, parent-report indicates
Cronbach’s α = 0.77, and teacher-report indicates Cronbach’s α
= 0.82.
2.3.4. Autism severity and social responsiveness
The SRS (Constantino, 2002) is a standardised, norm-
referenced, 65-item informant report of reciprocal social
interactions in
children aged 4–18, which are core difficulties in children with
ASD. The scale is comprised of both parent (SRS-P) and teacher
(SRS-
T) forms and is highly correlated with the Autism Diagnostic
Interview Revised (gold-standard diagnostic tool; Rutter, Le
Couteur, &
Lord, 2003). The scale has thus been utilised to capture the
severity of autistic behaviours (e.g., Constantino et al., 2003),
with
domains assessed including social awareness, social information
processing, reciprocal social communication, social anxiety/
avoidance, and stereotypic behaviours/ restricted interests. The
scale has satisfactory psychometric properties, including:
Cronbach’s
alpha = 0.97; internal consistency; predictive validity; inter-
rater reliability, parents and teachers r = 0.73 (Constantino,
2002).
Presently, Cronbach’s α = 0.95 for both parent and teacher
forms.

2.3.5. Mental health
The SDQ (Goodman, 1997a) is a brief, standardised norm-
referenced assessment of overall mental health, which is
commonly
utilised in Australian clinical practice for children aged 5–16
(Hawes & Dadds, 2004). The single version 25-item
questionnaire was
completed by both parents (SDQ-P) and teachers (SDQ-T),
which comprises five subscales: conduct problems,
hyperactivity, emo-
tional symptoms, peer problems, and prosocial behaviour. The
‘total difficulties score’ is determined by totalling the four
deficit
focused subscales (i.e., all except prosocial behaviour). Total
difficulties scores above 16 and 15 on parent- and teacher-
reports
respectively, are indicated to sit within the “abnormal” range
(Goodman, 1997b). Presently, parent-report indicates
Cronbach’s
α = 0.66, and teacher-report indicates Cronbach’s α = 0.75.
Previous studies have indicated overall acceptable internal
consistency,
with Cronbach’s α = 0.73 (Goodman, 2001).
2.4. Procedure
The appropriate baseline measure outcome data for students
without ID participating in the EBSST in Schools study was
retrieved
from the original database, and a new database was developed
for analysis purposes for the present study. The data is based on
a
secondary, non-identifiable databank originally collected by the
Children’s Hospital at Westmead. The Children’s Hospital at
Westmead and the Western Sydney University Human Ethics

Committee granted the use of such data, otherwise known as the
EBSST
in Schools databank.
2.5. Ethics
The current study is based on secondary data originally
collected by The Children’s Hospital at Westmead. The
previous research
(2020) 101599
4
involving the existing database gained consent from school
counsellors, school principals, parents and teachers for all
children
involved. The current study gained ethics approval from the
Human Research Ethics Committee at The Children’s Hospital
at
Westmead, and endorsed by Western Sydney University Human
Ethics Committee.
2.6. Data analysis
All analysis was done using IBM Statistical Package for Social
Sciences (SPSS) (Version 24.0 2016), with alpha set at .05.
Bonferroni adjustments were employed where to adjust for the
increased rate of Type 1 error when making multiple
comparisons
(Hills, 2011). Data screening did not reveal missing data.
Several measures allowed for a “don’t know” response, and thus
number of

participants vary slightly. Two univariate outliers were
identified (i.e., z-score > +/− 3.29;) and adjusted to one unit
smaller (or
larger) than the next most extreme score, as per Tabachnick and
Fidell (2013). Preliminary analyses were then conducted to
evaluate
required assumptions. Normality assumptions were satisfied for
all measures (i.e., p < .05; Shapiro- Wilkes, as per Hills, 2011),
with
some measures (i.e., SRS-P, SSIS-P and ERICA factors SA and
SR) showing a greater amount of non-significant negative
skewing than
others. Data transformations were unnecessary in the absence of
significant departures from normality, as per Tabachnick and
Fidell
(2013). Test specific assumptions are addressed during the
relevant analysis.
3. Results
3.1. Descriptive statistics
Means and standards deviations for all dependent variables are
reported in Table 1 below. A gender difference ratio of ap-
proximately 1:9 males was noted, which is consistent with
previous studies utilising an ASD population (Khor et al., 2014;
Rieffe
et al., 2011), but inconsistent with reported gender prevalence
rates more broadly (i.e., 1:4 males; Fombonne, 2009; Halladay
et al.,
2015; Werling & Geschwind, 2013). This difference was
significant, indicating that ASD may be more prevalent in
school-aged boys
than girls than has been previously reported, X 2 (1, N = 217) =
137.92, p < .001. Cohen’s w was 0.80, which is consistent with
a

large effect size (Cohen, 1988).
3.2. Self-reported emotion regulation and parent/ teacher-
reported emotion regulation, social skills, autism severity and
mental health
Pearson correlations were calculated (Hills, 2011) to assess the
strength of the relationship between self-reported ER and
parent/
teacher-reported ER, social skills, autism severity, and mental
health difficulties. Prior analyses ensured assumptions of
linearity and
homoscedasticity were not violated. As displayed in Table 2,
there were no significant correlations found between self-
reported ER
and either parent-reported ER or teacher-reported ER. Greater
self-reported ER was associated with greater social skills and
lower
mental health difficulties, for parent/ teacher-report measures.
Correlations between the self-reported ER and parent/ teacher-
re-
ported mental health and social skills ranged from r = .19
(weak) to r = .30 (medium), and were significant at the 0.01
level. Self-
reported ER however was not correlated with autism severity.
There were significant correlations between self-reported ER
and all subscales of the parent-reported mental health
questionnaire,
except the emotion symptoms (r = .001) and hyperactivity
subscales (r = −.14). Correlations ranged from r = −.17 for both
peer
problems, to r = −.23 for conduct problems subscale. There
were significant correlations between self-reported ER and all
subscales

Table 1
Descriptive Statistics for Demographic Data and Outcome
Measures.
M Range SD n
Sample
Age 9.51 7−13 1.26 217
Year at school 4.00 1−7 1.26 217
Measures
ERICA
Total 52.8 30−74 9.01 199
EC 21.0 9−35 5.69 199
SA 16.3 7−25 3.33 199
SR 15.6 5−20 2.77 199
EDQ-P 81.9 35−142 19.0 203
EDQ-T 76.5 35- 127 16.3 215
SSIS-P 67.0 18−116 20.8 190
SSIS-T 68.1 22−127 19.5 197
SRS-P 96.6 23−174 33.4 171
SRS-T 89.2 16−159 30.7 203
SDQ-P 20.5 4−35 6.30 192
SDQ-T 17.0 1−37 7.03 207
ERICA: Emotion Regulation Index for Children and
Adolescents; EDQ: Emotions Development Questionnaire;
SSIS-RS: Social Skills Improvement
System-Rating Scales; SRS: Social Responsiveness Scale.
(2020) 101599
5

of the teacher-reported mental health questionnaire, except
emotions symptoms (r = .03) and peer problems (r = −.14)
subscales.
Correlations ranged from r = −.19 for both the hyperactivity and
total difficulties subscales, to r = −.24 for the conduct problems
subscale.
3.3. Emotion regulation and mental health difficulties
Standard linear regression was employed to examine the
contribution of ER to mental health difficulties in children with
ASD
(Hills, 2011). Two standard linear regression analyses were
conducted first for parents and then secondly for teachers (i.e.,
four
analyses in total). The ERICA and EDQ were entered as
predictor variables, with SDQ scores as the criterion variable.
Multiple
regression analyses were then conducted to examine the
contribution of self- and parent/teacher-reported ER on mental
health, in the
context of social skills and autism severity. Thus, in a separate
analysis the SSIS, SRS, ERICA, and EDQ scores were entered
as
predictor variables, with SDQ scores remaining as the criterion
variable. Pearson correlations ranged from r = .02 and r = .74,
indicating singularity and multi-collinearity between dependent
variables was not present (Tabachnick & Fidell, 2013).
Inspection of the normal probability plot of standardised
residuals and the scatterplot of standardised residuals against
stan-
dardised predicted values indicated that the assumptions of
normality, linearity, and homoscedasticity of residuals were met

(Allen &
Bennett, 2008). Two multivariate outliers were identified in the
parent-report multiple regression (i.e., Mahalanobis distance >
critical χ2 = 18.47, p < .001; Tabachnick & Fidell, 2013) and
temporarily supressed for the parent-report multiple regression.
Multivariate outliers were not present in the teacher-report
analysis, nor was there evidence of multicollinearity in either
analysis
when assessed by the variance inflation factor (VIF; Tabachnick
& Fidell, 2013).
3.3.1. Parent report
Results of the first regression analysis indicated that ERICA
scores explained 4% of the variance in overall mental health
diffi-
culties, R2 = .04, F(1, 177) = 7.13, p = .008, whereas parent
EDQ scores was a non-significant predictor of overall mental
health
difficulties (p = .112). The multiple regression involving the
ERICA and EDQ-P, in addition to the SSIS-P and SRS-P,
indicated the
linear combination of predictors explained 46.5 % of the
variance in overall mental health difficulties, R2 = .47, F(4,
154) = 33.41,
p < .001. The EDQ-P was a significant predictor of overall
mental health difficulties, while ERICA scores became non-
significant.
The SSIS-P and SRS-P contributed significantly, positively and
negatively respectively, to overall mental health difficulties.
Autism
severity (SRS-P) recorded a higher standardised beta value than
social skills (SSIS-P), indicating a stronger predictor of mental
health
difficulties. Thus, in the context of context of social skills
(SSIS-P) and autism severity (SRS-P), while self-reported ER

(ERICA) is a
non-significant contributor of mental health difficulties in
children with ASD, parent-reported ER (EDQ-P) is significant.
Multiple
regression analyses are summarised in Table 3.
3.3.2. Teacher report
Results of the initial regression analysis indicated that ERICA
scores explained 3.7 % of the variance in overall mental health
Table 2
Pearson Correlations Between the ERICA Total and Sub-Scales
Scores with EDQ-No Emotion Coaching, SSIS-RS, SRS and
SDQ subscales.
Parent- and Teacher-Report Self-reported ER measure
ERICA-Total EC SA SR
ER measure
EDQ-P .095 .05 .13 .05
EDQ-T .116 .18* .03 −.02
Social skills measure
SSIS-P .25*** .24*** .16* .13
SSIS-T .30*** .27*** .20** .18*
Autism severity measure
SRS-P −.09 −.05 −.10 −.08
SRS-T −.12 −.07 −.10 −.14
Mental health measure
SDQ-P
Total difficulties score −.20** −.21** −.070 −.11
Emotional symptoms subscale .001 .04 −.06 −.01
Conduct problems subscale −.23** −.30*** −.02 −.10

Hyperactivity subscale −.14 −.21** .02 −.03
Peer problems subscale −.17* −.12 −.12 −.16*
SDQ-T
Total difficulties score −.19** −.16* −.15* −.13
Emotional symptoms subscale .03 .12 −.08 −.07
Conduct problems subscale −.24*** −.23*** −.14 −.13
Hyperactivity subscale −.19** −.21** −.10 −.08
Peer problems subscale −.14 −.13 −.10 −.08
*p < .05. **p < .01. ***p < .001.
(2020) 101599
6
difficulties, R2 = .04, F(1, 190) = 7.40, p = .007, whereas
teacher EDQ scores was a non-significant predictor of overall
mental
health difficulties (p = .775). The multiple regression
combining the ERICA, EDQ-T, SSIS-T and SRS-T indicated the
four predictors
explained 58.7 % of the variance in overall mental health
difficulties, R2 = .59, F(4, 173) = 61.43, p < .001. However,
only the
SSIS-T and SRS-T contributed significantly, negatively and
positively respectively, to overall mental health difficulties.
This indicates
that as autism severity (SRS-T) increases and social skills
decrease (SSIS-T), total mental health difficulties increase.
Similar to parent
analysis, autism severity (SRS-T) recorded a higher
standardised beta value than social skills (SSIS-T). Such

findings indicate that
within the context of social skills (SSIS) and autism severity
(SRS), neither self-reported or teacher-reported ER are
significant
contributors to mental health difficulties in children with ASD.
Multiple regression analyses are summarised in Table 3.
4. Discussion
The current study explored the association between self-
reported ER skills, parent- and teacher-reported ER, social
skills, autism
severity, and mental health in a community sample of children
with ASD. The hypothesis that self-reported ER would be
consistent
with parent- and teacher- reported ER was not supported.
Results partially supported the hypothesis that poorer self-
reported ER
ability would be associated with increased mental health
difficulties, poorer social skills, and greater autism severity for
parent/
teacher-reports. Only social skills and mental health difficulties
were associated with self-reported ER in children with ASD.
Finally,
the hypothesis that self- and parent/teacher-reported ER would
be significant predictors of mental health difficulties alongside
social
skills and autism severity was not supported. Multiple
regression analyses found parent-reported emotion regulation,
social skills,
autism severity, and child-reported ER accounted for 46.5 % of
the variance, compared to 58.7 % for the teacher-report
analysis.
However, in the context of social skills and autism severity,
self- and teacher-reported ER were not significant contributors
to mental

health.
The current study found that self-reported ER was not
associated with parent- and teacher-reported ER. Such findings
are
somewhat inconsistent with previous research (Renk & Phares,
2004; Stratis & Lecavalier, 2015) as research exploring
agreement
between child- and parent/teacher-report varies. This
inconsistency with previous research may be due to the current
study utilising
differing measures of ER for self-report, compared to
parent/teacher-report. However, the overall variability in
previous research
regarding agreement between child and parent/teacher report
may reflect a dearth of valid and reliable ER measures which
include
self-, parent- and teacher-report variations, and future studies
could benefit from developing a measure with such variations
(Weiss
et al., 2014).
Results indicated that parent- and teacher-reported social skills
and mental health are associated with self-reported ER in
children
with ASD. Consistent with previous research (Rieffe et al.,
2011) in TD children, current findings indicate poorer ER skills
(self-
reported) are associated with poorer mental health in children
with ASD. Upon further exploration of the mental health
subscales
(i.e., emotion symptoms, conduct problems, hyperactivity, peer
problems), emotion symptoms (e.g., depression and anxiety)
were
not correlated with ER skills (self-reported). This finding
contradicts previous research where emotional symptoms were

associated
with poorer ER in adolescents with ASD (e.g. Mazefsky et al.,
2014). There are several explanations for this discrepancy. For
example,
the current sample used a younger age group (i.e., 7−13 years
rather than 12–19 years), which may have influenced results.
This
suggests there may be differences in self-reported ER between
primary school age children and adolescents. The current study
also
utilised the SDQ, whereas previous research has utilised the
Child Behaviour Checklist (Achenbach, 1991; Mazefsky et al.,
2014;
CBCL). Research indicates SDQ as the preferred measure by
parents in comparison to the CBCL, and both are roughly
equivalent in
detecting internalising and externalising problems (Goodman &
Scott, 1999).
Autism severity was found to not be associated with self-
reported ER. Previous research suggests an unclear relationship
between
autism severity and ER (Mazefsky et al., 2014; Samson et al.,
2014) as well as with mental health (Eussen et al., 2012).
Discrepancies
in research may be due to many factors, such as the type of
assessment tools utilised, whether these have been validated for
the ASD
population, and how a mental health disorder and emotion
regulation have been defined (Eussen et al., 2012).
The current findings suggest that for a community, school-based
sample, social skills may be a stronger predictor of mental
health
Table 3

Standardised (β) Regression Coefficients, Standard Error (SE),
Squared Semi-Partial Correlations (sr2), and p-values For Each
Predictor in a
Regression Model Predicting Overall Mental Health Difficulties
for Parent- and Teacher-Report.
Variable β SE sr2 p
Parent-Report
ERICA −.054 .043 .003 .378
EDQ-P .164 .020 .027 .006**
SSIS-P −.255 .022 .043 .001**
SRS-P .478 .013 .158 < .001**
Teacher-Report
ERICA −.056 .040 .003 .281
EDQ-T .092 .022 .008 .067
SSIS-T −.264 .025 .035 < .001**
SRS-T .564 .015 .171 < .001**
*p < .05. **p < .01.
(2020) 101599
7
difficulties, compared to ER. In the context of social skills and
autism severity, self- and teacher-reported ER were not
significant
contributors to mental health. Interestingly, parent-reported ER
was not a significant contributor to parent-reported mental
health
when in isolation, however became significant when in the

context of social skills and autism severity. The current findings
also
indicated self-reported ER to be a significant contributor to
mental health when in isolation. However, when in the context
of social
skills and autism severity, ER is no longer a significant
contributor in a child and adolescent community sample, in
determining
mental health. This finding may be due to the utilisation of a
non-clinical, school-based sample, where findings may become
sig-
nificant in a clinical sample (i.e., participants with a co-
occurring mental health disorder, diagnosed upon clinical
examination). In
addition, the instruments utilised to measure social skills,
mental health difficulties and ER may not have been sensitive
enough to
exclusively distinguish between symptoms within each of these
areas, and a degree of symptom overlap may have occurred.
4.1. Limitations
The current exploratory study aimed to investigate the
association between ER, social skills, autism severity and
mental health in
a community-based sample of school-aged children with ASD,
without ID. A limitation in the current study was the utilisation
of
differing instruments to measure self-reported ER and parent/
teacher-reported ER. Despite this limitation, the current study
has the
strength of implementing a gold standard approach to measuring
ER (Mash & Hunsley, 2005), which highlights the importance
of
self-report within the assessment phase. Self-report has the
strength of providing data reflective of the individual’s own

views
(Hesselmark et al., 2015). It is also consistent with inclusive
research practice in ASD (Cooperative Research Centre for
Living with
Autism, 2016).
In addition, the study included no independent diagnostic
validation of each participant’s diagnosis of ASD and cognitive
ability,
instead utilising the SRS to measure ASD severity. Mental
health difficulties were measured via the SDQ and included no
clinical
examination for co-morbid mental health disorders. Although
increased scores on the SDQ are associated with mental health
dis-
orders, it is unclear whether symptoms may have overlapped
with ASD characteristics or with the ER measures. These are
both
significant limitations of the current study, and future studies
could implement a rigorous, clinical assessment of ASD
diagnosis,
cognitive ability and mental health via measures with sensitivity
and specificity for an ASD population, when available, to
explore
whether the current findings can be replicated. Future studi es
may also implement more objective measures of ER, in addition
to
informant report, to avoid overlap of measures. This might
include naturalistic observation of behaviour (Jahromi, Bryce,
& Swanson,
2012; Konstantareas & Stewart, 2006) and physiological
measures such as electroencephalography (EEG) of brain
activity, heart rate
variability and skin conductance as indexes of ER (Bal et al.,
2010; South, Newton, & Chmaberlain, 2012; Van Hecke et al.,
2009).

4.2. Clinical implications and future directions
The findings indicate that for a community, school-based
sample, social skills may be a stronger predictor of mental
health
difficulties, compared to ER skills. This suggests that for
children aged 7−13 years with ASD without ID, to reduce
mental health
difficulties, social skills may be the focus of intervention with
some focus on ER ability. Social skills techniques found to be
effective
for children with ASD include social stories (Ozdemir, 2008),
video modelling (Lantz, 2005) and visual activity schedules
(Betz,
Higbee, & Reagon, 2008). The findings also support
interventions which target both social skills and ER skills. For
example, EBSST
(Ratcliffe, 2011; Wong, Lopes, & Heriot, 2010) targets
emotional competency skills, such as emotional problem
solving, under-
standing other’s emotions and emotion regulation, in the context
of social situations.
In addition, this study applied the ERICA to measure self-
reported emotion regulation in children, aged 7−13-years, with
ASD.
The psychometric evaluation of the ERICA (MacDermott et al.,
2010) was based on TD children aged 9−16-years old. This is
the first
study to utilise this measure for an ASD population, and below
the age of 9-years old. Future research into the ERICA
(MacDermott
et al., 2010) as a self-report measure of ER in children with
ASD may be appropriate, whereby a comparative study of TD
children and

children with ASD is conducted. Future studies could also
compare the current findings to other neurodevelopmental
populations
with difficulties in ER ability, such as Attention-
Deficit/Hyperactivity Disorder (APA, 2013; Bunford, Evans, &
Langberg, 2018). This
may identify similarities and differences across
neurodevelopmental disorders within the ER domain, and thus
inform and direct
treatment intervention.
4.3. Conclusion
The current study was an investigation into the association
between self-reported ER and parent/teacher-reported ER, social
skills, autism severity, and mental health in school-age children
with ASD without ID. Poorer levels of self-reported ER were
asso-
ciated with decreased social skills and increased mental health
difficulties, as rated by both parents and teachers across the
sample. In
the context of social skills and autism severity, self- and
teacher-reported ER ability were non-significant factors in
explaining the
variance of mental health difficulties in children aged 7–13-
years with ASD. This suggests that for a community, school-
based sample,
social skills may be a stronger predictor of mental health
difficulties, compared to ER. This study provides a valuable
basis for future
research exploring ER in an ASD population.
CRediT authorship contribution statement
Talia Burton: Conceptualization, Methodology, Formal analysis,
Writing - original draft, Writing - review & editing,

(2020) 101599
8
Visualization. Belinda Ratcliffe: Conceptualization,
Methodology, Formal analysis, Writing - review & editing,
Visualization,
Supervision. James Collison: Methodology, Formal analysis,
Writing - review & editing, Visualization, Supervision. David
Dossetor:
Investigation, Writing - review & editing, Supervision. Michelle
Wong: Investigation, Writing - review & editing, Supervision.
Declaration of Competing Interest
No actual or potential conflicts of interest exist for this research
study.
Acknowledgements
We would like to gratefully acknowledge and thank the
Children’s Hospital at Westmead and Western Sydney
University for their
support of this project. We would also like to acknowledge and
thank the NSW Department of Education and Communication
for their
partnership in this project and their support in organising the
initial data collection and the time given by the school
counsellors,
teachers and parents.
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http://refhub.elsevier.com/S1750-9467(20)30089-
1/sbref0365Self-reported emotion regulation in children with

autism spectrum disorder, without intellectual
disabilityIntroductionIssues in the assessment of emotion
regulationThe current
studyMethodDesignParticipantsMaterialsDemographic
informationEmotion regulationSocial skillsAutism severity and
social responsivenessMental healthProcedureEthicsData
analysisResultsDescriptive statisticsSelf-reported emotion
regulation and parent/ teacher-reported emotion regulation,
social skills, autism severity and mental healthEmotion
regulation and mental health difficultiesParent reportTeacher
reportDiscussionLimitationsClinical implications and future
directionsConclusionCRediT authorship contribution
statementDeclaration of Competing
InterestAcknowledgementsReferences
Contents lists available at ScienceDirect
Research in Autism Spectrum Disorders
journal homepage: www.elsevier.com/locate/rasd
Writing research involving children with autism spectrum
disorder
without a co-occurring intellectual disability: A systematic
review
using a language domains and mediational systems framework
Matthew Carl Zajic*, Sarah Emily Wilson
Curry School of Education and Human Development, University
of Virginia, P.O. Box 800784, Charlottesville, VA 22904,
United States
A R T I C L E I N F O

Keywords:
Autism spectrum disorder
Education
Handwriting
Language
School-age
Spelling
Systematic review
Writing
A B S T R A C T
Background: Descriptive and intervention research studies have
identified writing as a challenge
for many students with autism spectrum disorder (ASD).
However, relatively little remains
known about how these studies have examined specific writing
skills, particularly from a writing
research perspective. This study systematically reviewed
descriptive and intervention studies
to examine how studies have
examined transcription (handwriting and spelling) and
translation/text generation (written ex-
pression) skills and associations between writing skills with
language domain and mediational
systems skills. Study quality indicators including reference to
writing research and theory were
also examined.
Method: From an initial screening of 1,958 records, 46 studies
(29 descriptive and 17 inter-
vention) were retained for inclusion. Studies were coded for
study characteristics, quality in-
dicators, and reported writing and writing-associated skills.
Results: Studies included 1,166 participants who were
predominantly male with a verified ASD

diagnosis but varied on other characteristics. Study quality was
low for certain indicators (i.e.,
power analysis and generalization), and fewer studies
referenced writing theory compared to
writing research. Studies reported on different writing skills
(transcription: 52%; translation/text
generation: 70%) but infrequently reported on associations with
language domains (0–7%) and
mediational systems (24–43%).
Conclusions: Studies have focused predominantly on assessing
transcription or translation/text
generation skills with little systematic attention to relationships
between writing and language
domain or mediational systems skills. Reviewed studies offer
preliminary findings, areas of
needed future research, and implications for continued research
into understanding and sup-
porting the writing skill development of children with ASD.
1. Introduction
Autism spectrum disorder (ASD) is a neurodevelopmental
difference that affects social communication and social
cognition with
the presence of repetitive and restrictive behaviors and interests
(American Psychiatric Association, 2013). Within the
burgeoning
area of academic-based research involving children with ASD,
school-age children with ASD demonstrate heterogeneous
academic
development profiles (Bauminger-Zviely, 2013, 2014; Estes,
Rivera, Bryan, Cali, & Dawson, 2011; Fleury et al., 2014; Keen,
Webster,
Received 24 February 2019; Received in revised form 9 October

2019; Accepted 28 October 2019
⁎ Corresponding author.
E-mail addresses: [email protected] (M.C. Zajic),
[email protected] (S.E. Wilson).
Research in Autism Spectrum Disorders 70 (2020) 101471
Available online 11 December 2019
1750-9467/ © 2019 Elsevier Ltd. All rights reserved.
T
http://www.sciencedirect.com/science/journal/17509467
https://www.elsevier.com/locate/rasd
http://crossmark.crossref.org/dialog/?doi=10.1016/j.rasd.2019.1
01471&domain=pdf
& Ridley, 2016; Kim, Paul, Tager-Flusberg, & Lord, 2014;
Mundy & Mastergeorge, 2012; Simpson & Myles, 2016).
Research focused
on content-area development has remained limited and has
typically examined reading and mathematics development (e.g.,
Jones
et al., 2009), leaving questions about writing development often
overlooked and unanswered (Bauminger-Zviely, 2014; Corbett
&
Iqbal, 2018; Keen et al., 2016; Zajic & Asaro-Saddler, 2019)
despite the importance of writing in other academic areas and
across the

lifespan (e.g., Graham, 2006, 2018).
A limited number of studies have examined the writing
development of children with ASD (e.g., Asaro-Saddler, 2015;
Finnegan &
Accardo, 2018; Kushki, Chau, & Anagnostou, 2011; Pennington
& Delano, 2012). Children with ASD show challenges with
hand-
writing and graphomotor skills (Kushki et al., 2011) as well as
with text generation (Brown, Johnson, Smyth, & Oram Cardy,
2014;
Mayes & Calhoun, 2003b, 2008; Myles et al., 2003; Zajic et al.,
2018). Intervention research has emphasized multicomponent
approaches that draw from evidence-based practices (EBPs)
based on recommendations across ASD and writing research
(Accardo,
Finnegan, Kuder, & Bomgardner, 2019; Asaro-Saddler, 2015,
2016; Pennington & Delano, 2012).
Inquiry into how research has assessed the writing skills of
children with ASD has received less systematic attention. The
complex,
multifaceted nature of writing has been poorly addressed in the
available literature with studies having noted infrequent
guidance
from writing research and theory (i.e., Dockrell, Ricketts,
Charman, & Lindsay, 2014; Zajic et al., 2018; Zajic, Dunn, &
Berninger,
2019). This study reviews the available empirical literature
(Berninger, 2015) to examine 1) how studies have assessed
writing and closely related skills of school-age children with
ASD, and 2)
if studies have included references to relevant writing research
and theory.

1.1. Reviews of academic and writing abilities of childr en with
ASD
Several reviews have synthesized findings related to the writing
skills of individuals with ASD. In a review of six studies in
children with ASD without a co-occurring intellectual
disability, Whitby and Mancil (2009) reported children appeare d
more at risk
for higher-order rather than lower-order academic challenges
and that cognitive abilities (i.e., FIQ) predicted academic
achievement.
Though writing showed similar relationships to FIQ, writing
differentiated from other skills with children showing lower-
order
(handwriting) and higher-order (written expression) challenges.
In their review of 19 studies, Keen et al. (2016) reported that
children showed variable strengths and weaknesses, and
academic performance was positively associated with cognitive
abilities and
negatively associated with ASD symptom severity. Keen et al.
(2016) further acknowledged the positive relationship between
FIQ and
writing (for written expression but not for spelling) and
highlighted writing when discussing uneven patterns of
achievement (i.e.,
children performed better at spelling than writing). However,
overall takeaways often did not generalize to writing skills
(e.g., no
studies examined the relationship between ASD symptom
severity and writing skills).
Additional reviews have provided further insights into writing
development and writing instruction. In a review of seven
handwriting studies, Kushki et al. (2011) reported positive
associations between fine motor and handwriting development,
noting

consistent handwriting challenges in legibility and formation. In
a review of 15 intervention studies, Pennington and Delano
(2012)
emphasized explicit writing instruction in the absence of ASD-
specific EBPs. In a review of 11 studies, Asaro-Saddler (2016)
found
that self-regulated strategy development (SRSD) improved the
writing quality of elementary-age students with ASD who
struggled
with writing. In a meta-analysis of 13 studies, Finnegan and
Accardo (2018) reported lower performance in spelling,
handwriting
(legibility, size, and speed), and composition length and overall
structure for individuals with ASD compared to typically
developing
(TD) peers. In a research synthesis of 24 single-case design
studies, Accardo et al. (2019) identified several effective
features of
instructional writing packages that have been used with children
with ASD (i.e., combinations of visual, motivational, choice,
technology, behavioral, peer, auditory, and tactile supports).
One issue not captured by prior reviews is the extent to which
past research has accounted for the multifaceted nature of
writing.
Recent studies have highlighted this concern by suggesting
researchers have included few references to writing research
and theory
(i.e., Dockrell et al., 2014; Zajic et al., 2018, 2019). Addressing
concerns about how writing skills are studied in children with
ASD
can benefit future designs across descriptive and intervention
studies. However, available reviews have often focused on
either
descriptive (Finnegan & Accardo, 2018; Keen et al., 2016;
Kushki et al., 2011; Whitby & Mancil, 2009) or intervention

studies
(Accardo et al., 2019; Asaro-Saddler, 2016; Pennington &
Delano, 2012). A more comprehensive approach is needed.
1.2. Writing development and associated language domains and
mediational systems
Writing is a challenging, complex skill that has existed for over
6000 years (Graham, 2006). Writing contains two core com-
ponents: transcription and translation/text generation (Fayol,
Alamargot, & Berninger, 2012). Transcription involves
transcribing
what an individual wants to communicate via written symbols
and includes skills like handwriting, keyboarding, and spelling
(Graham, 2006). Translation/text generation functions within
the mind to transform ideas and cognitive representations into
written
language (Fayol et al., 2012; Hayes & Berninger, 2014) .
Often overlooked linguistic, cognitive, and social processes that
dynamically change throughout the lifespan (Berninger, 2015;
Graham, 2018; Hayes & Berninger, 2014) support these writing
skills (e.g., Myhill & Fisher, 2010). Linguistic research
highlights the
challenge of translating ideas into written form to meet different
writing tasks by focusing on the influences from additional
language
skills (Berninger, 2009; Neef, 2012). Sociocultural research
posits writing as a learned social technology produced for social
cir-
cumstances to communicate among people across contexts
(Bazerman, 2015). Psychological research focuses on the
cognitive pro-
cesses required for confronting writing as problem-solving tasks
across different contexts (Hayes & Berninger, 2014; Hayes &
Flower,

1980; MacArthur & Graham, 2015). Given the processes
involved, writing challenges are common for many students. A
large body of
M.C. Zajic and S.E. Wilson Research in Autism Spectrum
Disorders 70 (2020) 101471
2
literature has focused on the identification of writing challenges
in children with specific learning disabilities (SLDs; Swanson,
Harris,
& Graham, 2014). Frameworks helpful to understanding the
challenges faced by children with SLDs (e.g., Connelly &
Dockrell, 2016)
may offer approaches to conceptualizing the writing
development of children with ASD who often show similar
difficulties for
different reasons (e.g., Mayes, Breaux, Calhoun, & Frye, 2017;
Price, Lacey, Weaver, & Ogletree, 2017; Zajic et al., 2019).
One comprehensive framework for understanding written
language development in children with SLDs is a language
domain and
mediational systems framework (Berninger, 2000, 2009, 2015;
Silliman & Berninger, 2011). The language domains include
language
by hand (writing), language by eye (reading), language by
mouth (oral language), and language by ear (listening
comprehension).
Relationships between language domains begin during early
development and adapt as well as influence each other across
the school-
age years (Berninger, 2000, 2015; Berninger & Abbott, 2010;

Berninger & Richards, 2002; Fitzgerald & Shanahan, 2000;
Shanahan,
2006, 2015). Four underlying mediational systems support
language domain development: sensorimotor; social, emotional,
and
motivational (SEM); cognition, and attention/executive
functions (Berninger, 2015; James, Jao, & Berninger, 2016;
Silliman &
Berninger, 2011). The sensorimotor system receives information
from the surrounding environments via the ears and eyes and
acts
upon them via the mouth and hands. The SEM system manages
how individuals develop, manage, and experience emotions and
motivations along with how they navigate relationships with
others across environments. The cognition system focuses on
the
facilitation of learning via broader mental processes (i.e.,
intellectual abilities and memory). The attention/executive
functions
system helps individuals attend to their environments and
regulate their actions across contexts via lower- (i.e.,
supervisory atten-
tion) and higher-order (e.g., self-regulation) processes.
1.3. Current study
This study adopts the language domains and mediational
systems framework to review available empirical research about
the
writing development of individuals with ASD. This study
examines how descriptive and intervention research studies
have assessed
writing skills (i.e., transcription and translation/text generation)
along with associated language domain (i.e., reading, listening,
and
oral language) and mediational system skills (i.e., sensorimotor,

SEM, cognition, and attention/executive functions). In addition
to
assessing for general study quality, this study created quality
indicators for quantifying how studies have referred to writing
research
and theory.
2. Method
2.1. Selection of studies and inclusion criteria
Best practices for surveying the literature were followed to
allow for a comprehensive search (Cooper, 2016). Record
identifi-
cation and screening were completed in line with the systematic
process outlined by the Preferred Reporting Items for
Systematic
Reviews and Meta-Analyses (PRISMA; Moher, Liberati,
Tetzlaff, Altman, & The PRISMA Group, 2009; see Fig. 1).
Authors conducted
an electronic search of four large journal databases (Academic
Search Complete, PsycINFO, Education Resources Information
Center,
and PubMed) using Boolean search terms—(autism* OR
asperg*) AND (handwrit* OR spell* OR writ*)—within the
title, abstract,
and keyword search fields. Manual searches were conducted via
reference lists of previously mentioned reviews and meta-
analyses.
Comprehensive searching was completed in February 2019.
Titles, abstracts, and full texts were assessed for inclusion
based on criteria found in Fig. 1. Criteria c (included at least
one
dependent writing skill measure assessed in English) and g
(included at least one participant without a co-occurring

intellectual
disability or severe communication difficulty [ID/SCD]) were
included to narrow assessment and participant heterogeneity. As
different language systems rely on different orthographies (e.g.,
Berninger, 1994), this review included studies that used
English-
based writing assessments (as accounting for different language
systems fell beyond the scope of this review). As the cognitive
and
communicative profiles of children with ASD are heterogeneous
(e.g., Kim et al., 2014; Klinger, Mussey, & O’Kelley, 2018) and
can
impact decisions made about academic programming and
instruction (e.g., Pennington & Carpenter, 2019; Simpson &
Myles, 2016),
this review focused on participants who did not demonstrate a
co-occurring ID/SCD. However, excluding studies based on
these
criteria can bias reported findings, and considerations are
offered in the limitations section.
2.2. Study quality and characteristics
All studies were assessed for study quality and inclusion of
writing research or theory (Table 1). Quality indicators for
descriptive
studies were adapted from Finnegan and Accardo (2018) and for
intervention studies were adapted from Accardo et al. (2019).
One
intervention study was coded using descriptive rather than
intervention quality indicators due to not using a single-case
study
research design. Categories for inclusion of writing research
and inclusion of writing theory were created to examine if
studies
referenced either broader writing research findings (i.e., not

involving children with ASD) or theoretical writing
frameworks. Studies
were coded for inclusion of either (i.e., at least one reference)
in the introduction and discussion sections. Authors
independently
coded studies for all quality indicators and discussed
disagreements until consensus.
Authors extracted additional data about study characteristics
and skill assessment, including research design, journal ti tles
and
research areas, ASD group demographics, included comparison
group(s), and ASD symptom severity and IQ assessment
information.
See Tables 2 and 3 for study characteristic information and
study quality summaries.
Disorders 70 (2020) 101471
3
2.3. Language domains and mediational systems framework
Studies were categorized based on inclusion of written language
outcomes for transcription skills (i.e., spelling and/or hand-
writing) and translation/text generation skills (i.e., written
expression). If transcription was assessed during a
translation/text
generation task (e.g., spelling performance assessed during a
timed, genre-based writing task), then the study was coded as
Fig. 1. Preferred Reporting Items for Systematic Reviews and
Meta-Analyses (PRISMA; Moher et al., 2009) flowchart for

record identification,
screening, and eligibility assessment.
Disorders 70 (2020) 101471
4
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ci
ta
ti
on
in
th
e
in
tr
od
uc
ti
on
to
a
th
eo
re
ti
ca
l
w
ri
ti
ng
fr
am

ew
or
k
(1
).
A
ge
-
Th
e
m
ea
n
ag
e
of
th
e
pa
rt
ic
ip
an
ts
in
th
e
co
m
pa

ri
so
n
gr
ou
ps
w
as
no
t
si
gn
ifi
ca
nt
ly
di
ff
er
en
t
(1
).
Fi
de
lit
y
of
tr
ea
tm

en
t
-
D
at
a
w
er
e
pr
ov
id
ed
ev
id
en
ci
ng
th
e
tr
ea
tm
en
t
w
as
ad
m
in

is
te
re
d
as
in
te
nd
ed
w
it
h
in
te
gr
it
y
(h
ig
he
r
th
an
0.
80
fi
de
lit
y
re
po

rt
in
g)
(1
).
W
ri
ti
ng
re
se
ar
ch
(I
nt
ro
du
ct
io
n)
-
In
cl
ud
ed
at
le
as
t
on

e
ci
ta
ti
on
in
th
e
in
tr
od
uc
ti
on
to
re
se
ar
ch
on
w
ri
ti
ng
de
ve
lo
pm
en
t

in
ch
ild
re
n
w
it
ho
ut
A
SD
(1
).
El
ig
ib
ili
ty
-
D
ia
gn
os
ti
c
in
fo
rm
at

io
n
w
as
us
ed
to
de
te
rm
in
e
if
pa
rt
ic
ip
an
ts
m
et
cr
it
er
ia
fo
r
st
ud
y
in

cl
us
io
n
(1
).
Fu
nc
ti
on
al
re
la
ti
on
-
D
es
ig
n
ha
d
po
te
nt
ia
l
to
de
m
on
st

ra
te
ex
pe
ri
m
en
ta
l
co
nt
ro
l
(1
).
W
ri
ti
ng
th
eo
ry
(D
is
cu
ss
io
n)
-
In
cl

ud
ed
at
le
as
t
on
e
ci
ta
ti
on
in
th
e
di
sc
us
si
on
to
a
th
eo
re
ti
ca
l
w
ri
ti
ng

fr
am
ew
or
k
(1
).
Tr
ea
tm
en
t
fi
de
lit
y
-
Pa
rt
ic
ip
an
ts
ac
ro
ss
gr
ou
ps
w

er
e
tr
ea
te
d
co
ns
is
te
nt
ly
an
d
te
st
ed
in
si
m
ila
r
se
tt
in
gs
(1
).
B
as
el

in
e
st
ab
ili
ty
-
Pr
ov
id
ed
ev
id
en
ce
of
a
st
ab
le
pa
tt
er
n
w
it
h
th
re
e
da
ta

po
in
ts
(0
.5
).
-
Pr
ov
id
ed
ev
id
en
ce
of
a
st
ab
le
pa
tt
er
n
w
it
h
fi
ve
or

m
or
e
da
ta
po
in
ts
(1
).
W
ri
ti
ng
re
se
ar
ch
(D
is
cu
ss
io
n)
-
In
cl
ud
ed
at

le
as
t
on
e
ci
ta
ti
on
in
th
e
di
sc
us
si
on
to
re
se
ar
ch
on
w
ri
ti
ng
de
ve
lo
pm

en
t
in
ch
ild
re
n
w
it
ho
ut
A
SD
(1
).
R
an
do
m
se
le
ct
io
n
-
Pa
rt
ic

ip
an
ts
w
er
e
ra
nd
om
ly
se
le
ct
ed
fr
om
a
la
rg
er
re
pr
es
en
ta
ti
ve
gr
ou
p
(1

).
C
ei
lin
g
eff
ec
t
-
N
o
ce
ili
ng
eff
ec
ts
pr
es
en
t
du
ri
ng
ba
se
lin
e
or

in
te
rv
en
ti
on
(1
).
Te
st
er
s
-
St
ud
y
in
di
ca
te
s
w
he
th
er
te
st
er
s
w
er
e

qu
al
ifi
ed
an
d/
or
tr
ai
ne
d
(1
).
M
ai
nt
en
an
ce
-
St
ud
y
in
cl
ud
ed
as
se
ss
m

en
t
of
m
ai
nt
en
an
ce
of
th
e
in
te
rv
en
ti
on
(1
).
N
um
be
rs
-
Pa
rt
ic
ip
an

ts
in
gr
ou
ps
re
ce
iv
ed
a
co
m
pa
ra
bl
e
nu
m
be
r
of
te
st
s
(1
).
Tr
ai
ni
ng

of
in
te
rv
en
ti
on
is
t
-
In
te
rv
en
ti
on
is
t
re
ce
iv
ed
pr
of
es
si
on
al
de
ve
lo
pm

en
t
or
w
as
a
kn
ow
n
ex
pe
rt
(1
).
M
ea
su
re
s
-
St
ud
y
ca
n
be
re
pl
ic

at
ed
w
it
h
sa
m
e
ou
tc
om
e
m
ea
su
re
s
(1
).
Fl
oo
r
eff
ec
t
-
N
o
fl
oo
r
eff

ec
ts
pr
es
en
t
du
ri
ng
ba
se
lin
e
or
in
te
rv
en
ti
on
(1
).
R
el
ia
bi
lit
y
-
St

an
da
rd
iz
ed
te
st
s
w
er
e
us
ed
or
in
te
rr
at
er
re
lia
bi
lit
y
w
as
re
po
rt
ed
(1

).
So
ci
al
va
lid
it
y
-
St
ud
y
in
cl
ud
ed
as
se
ss
m
en
t
of
so
ci
al
va
lid
it
y

of
th
e
in
te
rv
en
ti
on
(1
).
V
al
id
it
y
-
Ev
id
en
ce
pr
ov
id
ed
th
at
m
ea

su
re
s
w
er
e
va
lid
(1
).
G
en
er
al
iz
at
io
n
-
St
ud
y
in
cl
ud
ed
as
se
ss
m
en
t

of
ge
ne
ra
liz
at
io
n
of
th
e
in
te
rv
en
ti
on
(1
).
Te
ch
ni
qu
es
-
A
na
ly
si
s
lin

ke
d
to
re
se
ar
ch
qu
es
ti
on
s
(1
).
R
el
at
io
ns
hi
p
be
tw
ee
n
va
ri
ab
le
s

-
In
te
ra
ct
io
n
eff
ec
ts
an
d/
or
co
rr
el
at
io
n
sc
or
es
w
er
e
re
po
rt
ed
to
in
di

ca
te
w
he
th
er
or
no
t
th
er
e
w
er
e
re
la
ti
on
sh
ip
s
be
tw
ee
n
th
e
va
ri
ab
le
s
(1

).
D
at
a
re
po
rt
in
g
-
A
de
qu
at
e
da
ta
re
po
rt
ed
,i
nc
lu
di
ng
ge
nd
er
an
d

ag
e
of
pa
rt
ic
ip
an
ts
(1
).
D
ir
ec
ti
on
al
it
y
-
D
ir
ec
ti
on
of
ou
tc
om
e

m
ea
su
re
s
gi
ve
n,
if
ap
pl
ic
ab
le
(1
).
Po
w
er
an
al
ys
is
-
Po
w
er
an
al
ys
is
co

nd
uc
te
d
to
de
te
rm
in
e
sa
m
pl
e
si
ze
(o
r
m
en
ti
on
of
sa
m
pl
e
si
ze
as
lim

it
at
io
n)
(1
).
1
A
da
pt
ed
fr
om
Fi
nn
eg
an
an
d
A
cc
ar
do
(2
01
8)
.
2

A
da
pt
ed
fr
om
A
cc
ar
do
et
al
.
(2
01
9)
.
3
O
ne
in
te
rv
en
ti
on
st
ud
y
(A

sa
ro
-S
ad
dl
er
et
al
.,
20
15
)
di
d
no
tu
se
a
si
ng
le
-c
as
e
de
si
gn
ap
pr
oa
ch
.Q
ua
lit

y
w
as
as
se
ss
ed
us
in
g
de
sc
ri
pt
iv
e
st
ud
y
qu
al
it
y
in
di
ca
to
rs
in
st
ea
d
of
in
te

rv
en
ti
on
qu
al
it
y
in
di
ca
to
rs
,
as
th
os
e
in
di
ca
to
rs
w
er
e
or
ig
in
al
ly
de
ve

lo
pe
d
ba
se
d
on
ex
pe
ri
m
en
ta
l
an
d
qu
as
i-
ex
pe
ri
m
en
ta
l
re
se
ar
ch
de
si
gn
s

(s
ee
Fi
nn
eg
an
&
A
cc
ar
do
,
20
18
).
Disorders 70 (2020) 101471
5
T
ab
le
2
D
es
cr
ip
ti

ve
st
ud
y
ch
ar
ac
te
ri
st
ic
s
an
d
qu
al
it
y
in
di
ca
to
rs
.
St
ud
y
Jo
ur
na
l
A
re

a
A
SD
G
ro
up
C
om
pa
ri
so
n
G
ro
up
(s
)
D
ir
ec
tl
y
A
ss
es
se
d
A
SD

?
A
ss
es
se
d/
R
ep
or
te
d
IQ
/A
bi
lit
y?
St
ud
y
Q
ua
lit
y9
W
ri
ti
ng
Th
eo
ry

(I
,
D
)
W
ri
ti
ng
R
es
ea
rc
h
(I
,
D
)
A
la
ni
z
et
al
.
(2
01
5)
O
cc

up
at
io
na
l
Th
er
ap
y
n
=
27
(2
1M
/6
F)
,
ag
e
ra
ng
e
=
4-
10
TD
–
–

14
(9
3%
)
0,
0
1,
1
A
sh
bu
rn
er
et
al
.
(2
01
2)
Sp
ec
ia
l
Ed
uc
at
io
n

n
=
22
(2
1M
/1
F)
,
ag
e
ra
ng
e
=
8-
13
–
–
–
81
0
(7
3%
)
0,
0
1,

0
A
ss
ou
lin
e
et
al
.
(2
01
2)
A
ut
is
m
n
=
35
(5
0M
/9
F1
),
ag
e
ra

ng
e
=
5-
17
–
A
D
I-
R
,
A
D
O
S
W
A
IS
-I
II
,
W
IS
C
-I
V
10
1
0
(9
1%

)
0,
0
0,
1
B
ro
w
n
et
al
.
(2
01
4)
A
ut
is
m
n
=
25
(2
2M
/3
F)
,
ag

e
ra
ng
e
=
8-
17
TD
SR
S
W
A
SI
13
(8
7%
)
1,
1
1,
1
C
ar
tm
ill
et

al
.
(2
00
9)
O
cc
up
at
io
na
l
Th
er
ap
y
n
=
28
(2
4M
/4
F)
,
ag
e
ra
ng
e

=
8-
8
TD
G
A
D
S,
G
A
R
S
K
B
IT
14
(9
3%
)
1,
1
1,
1
D
ill
on
an

d
U
nd
er
w
oo
d
(2
01
2)
H
um
an
-C
om
pu
te
r
St
ud
ie
s
n
=
10
(9
M
/1
F)
,

ag
e
ra
ng
e
=
6-
12
TD
–
TR
O
G
8
12
(8
0%
)
1,
0
0,
0
D
ir
lik
ov

et
al
.
(2
01
7)
O
cc
up
at
io
na
l
Th
er
ap
y
n
=
57
(4
9M
/8
F)
,
ag
e
ra
ng
e

=
7-
12
A
D
H
D
,
TD
A
D
I-
R
,
A
D
O
S-
G
,
SR
S
W
IS
C
-I
V
,
W
IS
C

-V
13
(8
7%
)
0,
0
1,
0
D
oc
kr
el
l
et
al
.
(2
01
4)
Ps
yc
ho
lo
gy
n
=

64
(5
7M
/7
F)
,
ag
e
ra
ng
e
=
6-
12
SL
I
SR
S
B
A
S-
II
14
(9
3%
)
1,

1
1,
1
Es
te
s
et
al
.
(2
01
1)
A
ut
is
m
n
=
30
(2
5M
/5
F)
,
ag
e
ra
ng

e
=
9-
9
–
A
D
I-
R
,
A
D
O
S-
G
D
A
S
91
0
(8
2%
)
0,
0
0,
0
Fo

le
y-
N
ic
po
n
et
al
.
(2
01
2)
Sp
ec
ia
l
Ed
uc
at
io
n
n
=
52
(4
5M
/7
F)
,
ag
e

ra
ng
e
=
6-
16
–
A
D
I-
R
,
A
D
O
S-
G
W
IS
C
-I
V
10
1
0
(9
1%
)
0,

0
0,
0
Fu
en
te
s
et
al
.
(2
00
9)
N
eu
ro
lo
gy
n
=
14
(1
1M
/3
F)
,
ag
e

ra
ng
e
=
8-
13
TD
A
D
O
S-
G
,
A
D
I-
R
W
IS
C
-I
V
12
(8
0%
)
0,
0
0,

0
Fu
en
te
s
et
al
.
(2
01
0)
N
eu
ro
lo
gy
n
=
12
(1
1M
/1
F)
,
ag
e
ra
ng
e

=
12
-1
6
TD
A
D
O
S-
G
,
A
D
I-
R
W
A
SI
,
W
IS
C
-I
V
12
(8
0%
)
0,
0

0,
0
G
ra
ce
,
En
ti
co
tt
et
al
.
(2
01
7)
A
ut
is
m
n
=
23
(2
3M
/0
F)
,
ag

e
ra
ng
e
=
8-
12
TD
SR
S-
2
W
A
SI
-I
I,
W
IS
C
-I
V
15
(1
00
%
)
0,
0

1,
1
G
ra
ce
et
al
.
(2
01
8)
Ps
yc
ho
lo
gy
n
=
23
(2
3M
/0
F)
,
ag
e
ra
ng
e

=
8-
12
TD
SR
S-
2
W
A
SI
-I
I,
W
IS
C
-I
V
14
(9
3%
)
0,
0
1,
1
G
ra

ce
,
R
in
eh
ar
t
et
al
.
(2
01
7)
A
ut
is
m
n
=
23
(2
3M
/0
F)
,
ag
e
ra
ng
e

=
8-
12
TD
SR
S-
2
W
A
SI
-I
I,
W
IS
C
-I
V
15
(1
00
%
)
0,
0
0,
1
G
ri

sw
ol
d
et
al
.
(2
00
2)
A
ut
is
m
n
=
20
(1
8M
/2
F)
,
ag
e
ra
ng
e
=
6-

16
–
–
W
IS
C
-R
91
0
(8
2%
)
0,
0
0,
0
H
en
de
rs
on
an
d
G
re
en
(2

00
1)
La
ng
ua
ge
n
=
2
(1
M
/1
F)
,
ag
e
ra
ng
e
=
9-
13
–
–
B
A
S,
W
IS

C
-I
II
-R
41
0
(3
6%
)
0,
0
0,
0
Jo
hn
so
n,
Pa
pa
do
po
ul
os
et
al
.
(2
01

3)
A
ut
is
m
n
=
14
(1
4M
/0
F)
,
ag
e
ra
ng
e
=
7-
14
A
D
H
D
,
TD
SR

S,
D
B
C
-P
W
A
SI
,
W
IS
C
-I
V
14
(9
3%
)
0,
0
0,
0
Jo
hn
so
n
et
al
.

(2
01
5)
A
ut
is
m
n
=
26
(2
6M
/0
F)
,
ag
e
ra
ng
e
=
8-
13
TD
SR
S,
D
B

C
-P
W
A
SI
,
W
IS
C
-I
V
13
(8
7%
)
0,
1
0,
1
Jo
hn
so
n,
Ph
ill
ip
s
et

al
.
(2
01
3)
D
ev
el
op
m
en
ta
l
D
is
ab
ili
ti
es
n
=
26
(2
6M
/0
F)
,
ag
e
ra

ng
e
=
8-
13
TD
SR
S,
D
B
C
-P
W
A
SI
,
W
IS
C
-I
V
12
(8
0%
)
0,
0
0,

0
K
im
et
al
.
(2
01
8)
Ps
yc
ho
lo
gy
n
=
47
(M
/F
2
),
ag
e
ra
ng
e
=
9-

18
–
A
D
O
S
D
A
S,
W
A
SI
,
W
IS
C
-
II
I
10
1
0
(9
1%
)
0,
0
0,
0

M
ay
es
an
d
C
al
ho
un
(2
00
3a
)
A
ut
is
m
n
=
78
(M
/F
3
),
ag
e
ra
ng

e
=
6-
15
–
–
W
IS
C
-I
II
91
0
(8
2%
)
0,
0
0,
1
M
ay
es
an
d
C
al

ho
un
(2
00
3b
)
A
ut
is
m
n
=
78
(M
/F
4
),
ag
e
ra
ng
e
=
6-
15
–
–

W
IS
C
-I
II
81
0
(7
3%
)
0,
0
0,
1
(c
on
tin
ue
d
on
ne
xt
pa
ge
)
Disorders 70 (2020) 101471

6
T
ab
le
2
(c
on
tin
ue
d)
St
ud
y
Jo
ur
na
l
A
re
a
A
SD
G
ro
up

C
om
pa
ri
so
n
G
ro
up
(s
)
D
ir
ec
tl
y
A
ss
es
se
d
A
SD
?
A
ss
es
se
d/
R
ep
or

te
d
IQ
/A
bi
lit
y?
St
ud
y
Q
ua
lit
y9
W
ri
ti
ng
Th
eo
ry
(I
,
D
)
W
ri
ti

ng
R
es
ea
rc
h
(I
,
D
)
M
ay
es
an
d
C
al
ho
un
(2
00
6)
Ps
yc
ho
lo
gy
n
=

87
(M
/F
5
),
ag
e
ra
ng
e
=
6-
16
A
D
H
D
-C
,
A
D
H
D
+
,
A
D
D
-I
,

A
D
D
+
,
B
D
,
B
I,
A
/D
,
SB
,
B
eD
–
W
IS
C
-I
II
10
(6
7%
)
0,
0
1,

1
M
ay
es
an
d
C
al
ho
un
(2
00
7)
N
eu
ro
ps
yc
ho
lo
gy
n
=
11
8
(M
/F
6
),

ag
e
ra
ng
e
=
8-
16
TD
,
A
D
H
D
,
A
/D
,
O
D
D
–
W
IS
C
-I
II
,
W
IS
C

-I
V
11
(7
3%
)
0,
0
0,
1
M
ay
es
an
d
C
al
ho
un
(2
00
8)
A
ut
is
m
n

=
54
(4
8M
/6
F)
,
ag
e
ra
ng
e
=
6-
14
–
C
A
Y
C
W
IS
C
-I
V
91
0
(8
2%

)
0,
0
0,
1
M
yl
es
et
al
.
(2
00
3)
D
ev
el
op
m
en
ta
l
D
is
ab
ili
ti
es

n
=
16
(1
5M
/1
F)
,
ag
e
ra
ng
e
=
9-
11
TD
–
W
IS
C
-I
II
13
(8
7%
)
0,

0
0,
0
Tr
oy
b
et
al
.
(2
01
4)
A
ut
is
m
n
=
30
(3
7M
/4
F7
),
ag
e
ra

ng
e
=
8-
21
TD
,
O
O
A
D
O
S,
SC
Q
W
A
SI
13
(8
7%
)
0,
0
0,
0
Za

jic
et
al
.
(2
01
8)
A
ut
is
m
n
=
77
(6
3M
/1
4F
),
ag
e
ra
ng
e
=
8-
16
A

D
H
D
,
TD
A
D
O
S,
A
SS
Q
,
SC
Q
,
SR
S
W
A
SI
-I
I
14
(9
3%
)
1,
1
1,

1
N
ot
e.
A
/D
=
an
xi
et
y/
de
pr
es
si
on
;
A
D
D
+
=
at
te
nt
io
n-
de
fi
ci
t
di

so
rd
er
(A
D
H
D
in
at
te
nt
iv
e
pl
us
a
co
m
or
bi
d
di
ag
no
si
s)
;
A
D
D
-I
=
at

te
nt
io
n-
de
fi
ci
t
di
so
rd
er
(A
D
H
D
in
at
te
nt
iv
e)
;
A
D
H
D
=
at
te
nt
io
n-
de

fi
ci
t/
hy
pe
ra
ct
iv
it
y
di
so
rd
er
;
A
D
H
D
+
=
at
te
nt
io
n-
de
fi
ci
t/
hy
pe
ra

ct
iv
it
y
di
so
rd
er
(p
lu
s
a
co
m
or
bi
d
di
ag
no
si
s)
;
A
D
H
D
-C
=
at
te

nt
io
n-
de
fi
ci
t/
hy
pe
ra
ct
iv
it
y
di
so
rd
er
(c
om
bi
ne
d)
;
B
D
=
bi
po
la
r
di

so
rd
er
;
B
eD
=
be
ha
vi
or
di
so
rd
er
;
B
I=
br
ai
n
in
ju
ry
;
O
D
D
=
op
po

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