A Cross-Cultural Comparison of Autistic Traits in the UK, India and Malaysia

ORIGINAL PAPER

A Cross-Cultural Comparison of Autistic Traits in the UK, Indiaand Malaysia

Megan Freeth • Elizabeth Sheppard •

Rajani Ramachandran • Elizabeth Milne

� Springer Science+Business Media New York 2013

Abstract The disorder of autism is widely recognised

throughout the world. However, the diagnostic criteria and

theories of autism are based on research predominantly

conducted in Western cultures. Here we compare the

expression of autistic traits in a sample of neurotypical

individuals from one Western culture (UK) and two East-

ern cultures (India and Malaysia), using the Autism-spec-

trum Quotient (AQ) in order to identify possible cultural

differences in the expression of autistic traits. Behaviours

associated with autistic traits were reported to a greater

extent in the Eastern cultures than the Western culture.

Males scored higher than females and science students

scored higher than non-science students in each culture.

Indian students scored higher than both other groups on the

Imagination sub-scale, Malaysian students scored higher

than both other groups on the Attention Switching sub-

scale. The underlying factor structures of the AQ for each

population were derived and discussed.

Keywords Culture � Autistic traits � Western � Eastern �India � Malaysia � UK

Introduction

Research into Autism Spectrum Disorders (ASD) is pri-

marily carried out in Western nations—typically Western

Europe, North America and Australasia—though general

conclusions are often assumed to extend to other popula-

tions. The impact of culture on behaviour, cognition or

diagnosis is not widely considered (Daley 2002; Dyches

et al. 2004; Matson and Kozlowski 2011). ASD diagnosis is

currently based on whether behaviour deviates from what is

regarded as typical. However, what is typical may differ

between cultures. Hence the point at which normal vari-

ability differentiates from an actual disorder, such as an

ASD, is likely to be influenced by cultural values and norms

(Daley 2002; Daley 2004; Mandell and Novak 2005; Nor-

bury and Sparks 2013). As a consequence, diagnostic tools

and procedures which have been developed and standard-

ised in the West (e.g. ADOS, Lord et al. 2000; ADI-R, Rutter

et al. 2003) may not prove to be reliable or valid if applied in

other non-Western cultures without adaptation. Recent

systematic cross-cultural comparative studies on autism

symptomatology and behavioural problems associated with

autism were conducted by Matson et al. (2011) and Chung

et al. (2012), comparing populations from Israel, South

Korea, UK and the USA. However, cross-cultural compar-

ative studies relating to autism are rather sparse in general

(Zaroff and Uhm 2012) and more are required to fully

understand the impact of culture on autism diagnosis and the

behaviours associated with the autistic spectrum.

An example of how cultural differences may come into

play in diagnosis is described by Norbury and Sparks

(2013), in relation to the use of two aspects of socio-

communicative behaviour. A lack of eye-contact and fail-

ure to use socio-communicative gestures such as pointing

are regarded as distinctive features of ASD in Western

M. Freeth (&) � E. Milne

Psychology Department, University of Sheffield,

Western Bank, Sheffield S10 2TP, UK

e-mail: [email protected]

E. Sheppard

School of Psychology, University of Nottingham Malaysia

Campus, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan,

Malaysia

R. Ramachandran

Department of Psychology, University of Calicut,

Malappuram 673 635, Kerala, India

123

J Autism Dev Disord

DOI 10.1007/s10803-013-1808-9

cultures and contribute to the Autism Diagnostic Obser-

vation Schedule (ADOS, Lord et al. 2000) algorithm for

diagnosis. However in some Eastern societies, such as

Chinese communities, direct eye-contact and using the

index finger to point are regarded as impolite, so it would

be quite normal for a child or adult not to engage in these

behaviours. Hence, lack of eye-contact and pointing would

not necessarily be indicators of ASD in these particular

societies.

Differences on a range of constructs have been previ-

ously observed between Western and Eastern populations.

Western cultures tend to have independent social orienta-

tion, emphasising self-direction, autonomy and self-

expression whereas Eastern cultures have interdependent

social orientation, emphasising harmony, relatedness and

connection (Markus and Kitayama 1991; Triandis 1989;

Varnum et al. 2010). Individuals from Eastern cultures

place more emphasis on the situation than those from

Western cultures (Choi et al. 1999). In addition, Varnum

et al. (2010) highlight significant differences between

individuals from these cultures in their ‘‘cognitive habits’’.

Cultural difference is seen in visual attention, with people

from Western cultures showing narrow field independent

attention, viewing the world analytically and focussing on

salient items; whereas people from Eastern cultures show a

broader field dependent pattern of attention, in which a

more holistic approach is taken where attention is allocated

to relations among objects (Masuda and Nisbett 2006;

McKone et al. 2010). Many of these constructs have been

shown to vary in individuals with ASD, with ASD being

associated with a more analytic, individualistic, field-

independent style of processing than individuals without

ASD (e.g. Jarrold et al. 2005; Shah and Frith 1983). Thus,

it is likely that Western and Eastern cultures may differ in

the expression of, and also perhaps the perception of,

autistic symptomatology.

To our knowledge, only one study has directly compared

the cognitive style of individuals with and without ASD in

a Western (UK) and Eastern (Singapore) culture (Koh and

Milne 2012). As observed in other studies, ASD partici-

pants in the UK sample showed a more field-independent/

local perceptual style than those without ASD. However,

no differences in cognitive style were found for the Sin-

gapore participants with and without ASD. This evidence

suggests that a localised field-independent perceptual style

may not be universal across individuals with ASD in dif-

ferent cultures. It is also worth noting that in experiments

that compare task performance between a group of indi-

viduals with ASD and a control group, whether or not

participants with ASD show atypical performance is

directly contingent on the performance of the matched-

control group. Thus, if task performance is significantly

influenced by culture, this will affect the conclusions drawn

regarding performance of the ASD group. The wider

implications of this are that items within diagnostic tools

relating to behaviours that are influenced by cultural norms

may not effectively discriminate between those with and

without ASD in certain cultures.

As standards of diagnosis vary drastically across cul-

tures due to differing standards of training and experience

of clinicians, differing political climates and differing

parental motivations (Daley and Sigman 2002; Dyches

et al. 2004; Ravindran and Myers 2012; Samadi et al.

2012), it is difficult to get a handle on whether the

behaviour and cognition of individuals with autism truly

differ across cultures or whether a slightly different cross-

section of society tends to be diagnosed with autism in each

culture. Regarding this issue, a recent review by Zaroff and

Uhm (2012) highlights that studies assessing the potential

influence of culturally specific social cognitive processing

styles on ASD prevalence are lacking. They propose that

methodological factors such as service availability,

screening procedures, diagnostic criteria used and the

availability to diagnostic tools are as much, if not more,

responsible than factors such as country of residence and

ethnicity in influencing ASD prevalence. They argue that

such methodological factors may affect disorder reporting,

detection, and analysis. To date, as far as we are aware,

there are no systematic comparisons of ASD between dif-

ferent countries using standardised tools and procedures.

The only systematic comparisons of the influence of eth-

nicity on ASD diagnosis have been conducted within the

USA, reporting that White-American children are more

likely to be diagnosed than other races, especially

Hispanic-American children (Kogan et al. 2008; Palmer

et al. 2010; Rosenberg et al. 2009). However, it is currently

thought that socio-economic factors, rather than any gen-

uine difference in prevalence between ethnicities, accounts

for observed differences (Mandell et al. 2009; Palmer et al.

2010; Rosenberg et al. 2009). One way to overcome the

problem of differing standards and methods of diagnosis

across cultures when attempting to assess the influence of

culture on behaviour relevant to ASD is to look at whether

there are any cross-cultural differences on scores on a

standardised measure of autism that is administered to a

cross-section of the undiagnosed population in exactly the

same way in each culture. This is the principle underlying

the method adopted in the current study.

Recently, it has been proposed that ASDs [autism,

Asperger Syndrome and pervasive developmental disorder

not otherwise specified (PDD-NOS)] not only lie on a

continuum with one another but also with the normal

population (Happe and Ronald 2008; Wainer et al. 2011),

with typical individuals varying in the degree to which they

possess autistic traits. Arising from this variation is the

suggestion that there are heritable autistic traits which fall

J Autism Dev Disord

123

below the threshold for diagnosis of an ASD but give a

genetic loading for autism, resulting in an increased like-

lihood of an ASD occurring in first degree relatives (Micali

et al. 2004; Virkud et al. 2009). In support of the existence

of a broad autism phenotype (BAP), twin and family

studies have shown that genetic factors have a large role in

risk for developing an ASD (e.g. Lauritsen and Elwald

2001: Skuse et al. 2005). Moreover, studies have reported a

number of cognitive and behavioural features of autism

existing in first degree relatives of those with the condition,

including impairments in social cognition (Losh and Piven

2007) executive dysfunction (Bolte and Poutska 2006),

weak central coherence (Happe et al. 2001) and language

abnormalities (Landa et al. 1992; Folstein et al. 1999).

Further, variations in the neurotypical population in aspects

of cognition relevant to autism have been shown to be

related to amount of self-reported autistic traits providing

support for the broad autism phenotype as a construct. For

example, Grinter et al. (2009) found that individuals who

had more autistic traits were better at the Embedded Fig-

ures Task (EFT) and showed poorer global visual inte-

gration than individuals who had less autistic traits, a

profile which is typically associated with autism. Sasson

et al. (in press) found that impairments in social cognition

and social skill that characterise ASD extend in milder

forms to those who possess a high but sub-clinical amount

of autistic traits.

The Autism-spectrum Quotient (AQ, Baron-Cohen

et al. 2001) is a 50-item self-report questionnaire which

probes respondents about a range of autism-related

behaviours to measure quantifiable autistic traits in the

normal population. The higher an individual scores on the

AQ, the greater the number of autistic traits he or she

possesses. Research conducted in various Western popu-

lations has shown that individuals who have a diagnosis

of an ASD score higher on the AQ than individuals who

do not (Baron-Cohen et al. 2001; Hoekstra et al. 2008).

Typically developing males tend to score slightly higher

than typically developing females (Baron-Cohen et al.

2001; Hoekstra et al. 2008; Stewart and Austin 2009) and

individuals studying or working in science professions

score higher than individuals who are non-scientists

(Baron-Cohen et al. 2001; Stewart and Austin 2009).

Studies using translated versions in non-English speaking

Western cultures such as Italy (Ruta et al. 2012); French-

speaking Canada (Lepage et al. 2009); and the Nether-

lands (Hoekstra et al. 2008) have returned broadly similar

findings, suggesting the AQ robustly measures autistic

phenotypic behaviour within Western European/North

American cultures.

The AQ was designed around a five factor structure,

with ten questions pertaining to each of the five domains:

social skill, attention switching, attention to detail,

communication and imagination (Baron-Cohen et al.

2001). However, subsequent studies attempting to verify

this structure by using factor analysis have proposed that

other structures may be more appropriate. These studies

conducted in the UK, US and the Netherlands have

reported between two and five factors, but with many

commonalities (Hoekstra et al. 2008; Austin 2005; Hurst

et al. 2007; Stewart and Austin 2009; Kloosterman et al.

2011). They appear to show a strong and reliable factor

comprising questions that probe facets of ‘social skill’,

with attention to details, communication/mindreading and

imagination factors also frequently returned.

While the AQ has been used extensively with Western

populations there is little previous research that has used it

to investigate autistic traits in non-Western populations.

However, one such example is a study conducted with

participants from Japan (Kurita et al. 2005; Wakabayashi

et al. 2006). While the pattern of responding was similar

for the Japanese sample as for Western respondents, their

mean AQ scores were significantly higher. This might

imply that there are differing cultural norms for some

autism-related behaviours, as probed by the AQ, resulting

in elevated scores in the Japanese sample. In addition, Lau

et al. (2013) recently published data from a Taiwanese

sample using a Chinese-translated version of the AQ. The

psychometric properties of the AQ-Chinese did not change

between clinic-based and community-based data suggest-

ing a good fit for a continuum of autistic expression. A

factor analysis indicated consistency in the manifestation

of the autistic profile across different cultures and age

groups in the AQ-Chinese. We therefore considered the

AQ an appropriate tool for research use within other non-

Western samples, such a study would be one of the first to

systematically compare responses across cultures and

indeed the first to look at responses from India and

Malaysia. Given the evidence reviewed above that Eastern

cultures show interdependent social orientation and a more

field-dependent, holistic cognitive style, we predicted that

participants from India and Malaysia may self-report fewer

autistic traits than those from the UK.

The research reported in this paper aimed to compare

autistic traits, as measured by the AQ, in two English-

speaking student populations from Eastern cultures (India

and Malaysia) with autistic traits in a student population

from a Western culture (UK). Two Eastern cultures, rather

than one, were used in the current study to give some

indication of whether any cultural differences observed

generalise between Eastern cultures, or whether differences

are more specific to one particular culture. Malaysia and

India both form part of the British commonwealth and

English is the language of instruction within the higher

education systems in both countries, enabling use of the

English language version of the AQ. ASD is increasingly

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diagnosed in both Malaysia and India, within the Western

diagnostic system (DSM or ICD) and sometimes using

Western diagnostic or screening tools (Barua and Daley

2008; Toran 2011); however the clinical rigour may not be

as high as in the West (Daley 2004). Understanding the

expression of behaviours relating to autism in these cul-

tures is therefore a pressing issue.

We aimed (a) to determine whether the self-reported

amount of behaviours representing autistic traits were

consistent across populations; (b) to investigate whether

typical features of performance observed in Western pop-

ulations were be replicated in India and Malaysia i.e.

whether males scored higher than females; whether indi-

viduals studying for science degrees scored higher than

those studying non-science subjects; (c) to explore whether

any categories of autistic behaviours (social skill; attention

switching; attention to detail; communication; imagination)

were more or less prevalent across cultures; and (d) to

explore whether behaviours fall into similar constructs in

each culture by analysing the factor structure in each of the

three populations.

Method

Participants

Undergraduates and postgraduates from student popula-

tions in the UK, Malaysia and India participated in this

study. There were 723 participants from the UK. In order to

ensure cultural homogeneity, only participants who iden-

tified their nationality as being ‘‘British or English’’ and

their ethnicity as ‘‘White or Caucasian’’ were included in

the UK sample. There were 271 participants from the state

of Kerala in Southern India, all of Indian nationality and

ethnicity. There were 245 participants from Malaysia, all

who identified their nationality as being Malaysian.

Regarding ethnicity, there were 172 Chinese–Malaysian

participants; 38 Malay participants; 20 Indian–Malaysian

participants. In addition there were 15 participants who

wrote other responses to the ethnicity question, e.g.

‘‘Buddhist’’ or ‘‘Asian’’.

Materials

The Autism-spectrum Quotient (AQ) questionnaire was

administered. Scores on this 50 item self-report question-

naire provide an indicator of the degree to which an indi-

vidual possesses traits associated with the autistic

spectrum. In the original article (Baron-Cohen et al. 2001),

male students scored an average of 18.6 out of 50; female

students scored an average of 16.4 out of 50.

Procedure

All participants completed the AQ questionnaire and were

told that the questionnaire assessed personality traits and

preferences. Participants were also asked to state their age,

sex and course studied. Recruitment to the study was based

on a convenience sampling method in each country. In the

UK, all first year undergraduates and first year postgraduates

at the University of Sheffield were invited to complete the

AQ online. In Malaysia an invitation was sent to all students

at the University of Nottingham Malaysia campus to com-

plete the same online questionnaire. In India, at the Uni-

versity of Calicut no such large e-mail database existed

hence some students completed the questionnaire online and

others completed a paper version. Students were targeted

whose course was taught in English. Each student who

completed the questionnaire in each country received

exactly the same questionnaire, containing exactly the same

wording.

Results

Scores on the AQ were calculated using the original Baron-

Cohen et al. (2001) collapsed scoring method: responses in

the ‘‘autistic’’ direction were given a score of 1, responses

in the ‘‘non-autistic’’ direction were given a score of 0.

Each participant therefore received a score between 0 and

50, higher scores indicating the presence of more autistic

traits. This scoring method was used as it was the method

proposed in the original article (Baron-Cohen et al. 2001),

was also used to validate use of the AQ in Japan (Waka-

bayashi et al. 2006) and is the method most frequently used

in research, so therefore provides the most scope for

comparison with other published work.

Mean AQ scores, reliability, as indicated by cronbach’s avalues, and other descriptive statistics for each group are

shown in Table 1. As described below, a series of ANOVAs

and post hoc t tests assessed whether there were overall dif-

ferences in scores between groups and also whether gender

and/or course studied had a differential effect in each culture.

In addition, the items were further grouped into Baron-Cohen

et al. (2001)’s five conceptually derived sub-scales. Scores on

these sub-scales were then compared between groups in order

to assess whether patterns of responses differed between

groups in relation to these sub-scales.

The Influence of Culture on AQ Scores Overall

A one-way ANOVA demonstrated that mean AQ scores

differed between the three groups, F(2,1236) = 71.63,

p \ .001, gp2 = 0.10. Post-hoc t tests indicated that the AQ

scores for the UK students was significantly lower than for

J Autism Dev Disord

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both the Indian students, t(992) = 9.00, p \ .001, d = 0.6,

and the Malaysian students, t(966) = 9.41, p \ .001,

d = 0.6. There was no significant difference between the

AQ scores of the Indian and Malaysian students,

t(514) = -0.93, p = .35, d = 0.1. The distributions of AQ

scores for each group are shown in Fig. 1.

The Effect of Gender on AQ Scores

A second analysis included only participants who disclosed

their gender. A 2 9 3 (gender 9 nationality) between-

subjects ANOVA found a main effect of gender,

F(1,1194) = 5.88, p = .015, gp2 = 0.12, as males scored

higher than females overall (male mean = 20.6; female

mean = 19.6). There was no gender 9 nationality inter-

action, F(2,1194) = 0.86, p = .43, gp2 = 0.001 (see Fig. 2)

suggesting that a similar trend in the data, for males to

score higher than females, was present across cultures.

In addition, two one-way ANOVAs were conducted,

demonstrating that there was a main effect of culture

for both males, F(2,527) = 24.02, p \ .001 and females

F(2,667) = 36.95, p \ .001, with UK males scoring lower

than both Indian males, t(421) = 5.41, p \ .001, d = 0.5

and Malaysian males, t(345) = 5.54, p \ .001, d = 0.6.

Table 1 Descriptive statisticsAQ

UK Malaysia India

N 723 245 271

Male:female 240:460 (23 missing) 107:136 (2 missing) 183:74 (14 missing)

Science:non-science 223:415 (81 missing) 164:78 (3 missing) 184:44 (43 missing)

Mean age (sd) 22.3 (7.0) 20.9 (2.5) 21.0 (2.6)

Mean AQ score (sd) 17.23 (6.6) 21.65 (5.5) 21.22 (5.0)

Cronbach’s a

AQ 0.79 0.66 0.58

Social skill 0.63 0.57 0.48

Attention to detail 0.56 0.59 0.53

Attention switching 0.56 0.45 0.35

Communication 0.59 0.51 0.51

Imagination 0.53 0.45 0.12

Fig. 1 Distribution of AQ

scores across cultures

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No difference between the mean scores of Indian and

Malaysian males was observed, t(288) = 1.50, p = .14,

d = 0.2. UK females scored lower than both Indian

females, t(532) = 5.44, p \ .001, d = 0.5 and Malaysian

females, t(594) = 7.32, p \ .001, d = 0.6. No difference

between the mean scores of Indian and Malaysian females

was observed, t(208) = 0.16, p = .87, d = 0.02. These

results indicate that the elevated scores observed in the

Indian and Malaysian samples were observed in both males

and females.

The Effect of Course Studied on AQ Scores

In the original Baron-Cohen et al. (2001) AQ paper, stu-

dents studying sciences (including mathematics) scored

significantly higher than students studying humanities or

social sciences. In the current sample, for the students who

provided the title of their degree course, these were coded

as science1 or non-science2 (social science/humanities/

business). Some students were not classified as they were

studying joint-honors courses of which part was science

and part was non-science. A 2 9 3 (course-type 9

nationality) between-subjects ANOVA found a significant

main effect of course-type, F(1,1075) = 16.49, p \ .001,

gp2 = 0.02, as students studying science courses tended to

score higher than students studying non-science courses

(mean science AQ = 20.81; mean non-science

AQ = 18.91). There was no course-type 9 nationality

interaction, F(2,1075) = 0.39, p = .68, gp2 = 0.001 indi-

cating that a similar trend in the data was present across

cultures (see Fig. 3). This analysis also indicates that the

effect of culture on AQ scores was not different between

students studying science courses and students studying

non-science courses.3 In addition, two one-way ANOVAs

were conducted, demonstrating that there was a main effect

of culture for both science students, F(2,568) = 21.26,

p \ .001, and non-science students, F(2,534) = 12.56,

p \ .001, with UK science students scoring lower than

both Indian science students, t(405) = 4.39, p \ .001,

d = 0.4, and Malaysian science students, t(385) = 5.82,

p \ .001, d = 0.6. Malaysian science students scored

slightly higher than Indian science students, t(346) = 1.96,

p = .05, d = 0.2. UK non-science students scored lower

than both Indian non-science students, t(457) = 3.35,

p = .001, d = 0.3 and Malaysian non-science students,

t(491) = 4.02, p \ .001, d = 0.4. No difference between

the mean scores of Indian and Malaysian non-science

students was observed, t(120) = 0.21, p = .83, d = 0.04.

These results indicate that the elevated scores observed in

the Indian and Malaysian samples were observed in both

science and non-science students.

AQ Sub-scale Analysis

The AQ was designed around five conceptually-derived

sub-scales: social skill, communication, attention to detail,

attention switching and imagination representing the main

areas in which autism is diagnosed. Full details of all items

in each sub-scale can be found in Baron-Cohen et al.

(2001)’s original article but as a general guide, social skill

requires participants to rate their social ability and prefer-

ence for conducting activities alone compared to in a

Fig. 3 Mean AQ scores by subject studied across cultures

Fig. 2 Mean AQ scores by gender across cultures

1 Science included engineering, physics, chemistry, biology, math-

ematics, communications, computer science, biomedical sciences,

pharmacy, genetics, medicine, B-tech, M-tech, biotechnology.2 Social sciences/humanities/business included Master of Business

Administration, psychology, language, literature, librarianship, edu-

cation, economics, management, finance.3 In order to be sure that the trend in the data for Indian and

Malaysian students to score higher than UK students was not driven

by the science students, planned comparisons indicated that UK

non-science students scores significantly lower than both Indian non-

science students [t(457) = 3.35, p = .001] and Malaysian non-

science students [t(491) = 4.02, p \ .001].

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group—a more autistic response would be poor social

ability and preference for aloneness; communication relates

to ease of understanding social cues of communication

such as turn-taking, knowing when a social partner is bored

or judging their social intention—a more autistic response

would be poor understanding of social cues; attention to

detail is the ability to notice small details and patterns—a

more autistic response would be high ability to notice

details and patterns; attention switching is a preference for

focusing on one thing and avoidance of the unfamiliar—a

more autistic response would be preference for focusing

and the familiar; imagination is ability to imagine and

creatively construct alternate scenarios—a more autistic

response would be to report poor imagination. Scores on

these subscales were compared between groups using a

5 9 3 (sub-scale 9 culture) mixed-measures ANOVA. As

reported previously, there was a main effect of culture,

F(2,1236) = 71.63, p \ .001, gp2 = 0.10. However, there

was also a sub-scale 9 culture interaction, F(2,1236) =

29.90, p \ .001, gp2 = 0.05, indicating that differences

between groups varied on each sub-scale (Fig. 4). In order

to investigate these differences further a series of one-way

ANOVAs were conducted for each sub-scale followed by a

series of post hoc t tests with bonferroni corrections.4

Scores on the communication sub-scale were significantly

different between groups, F(2,1236) = 48.86, p \ .001,

post hoc t tests found that both the Indian and Malaysian

scores were significantly higher than the UK scores (UK-

India, t(992) = 8.76, p \ .001; UK–Malaysia, t(966) =

6.74, p \ .001), there was no significant difference

between the Indian and Malaysian scores [t(514) = 1.46,

p = .15]. Scores on the social skill sub-scale were signif-

icantly different between groups, F(2,1236) = 50.31,

p \ .001, post hoc t tests found that both the Indian and

Malaysian scores were significantly higher than the UK

scores [UK–India, t(992) = 6.45, p \ .001; UK–Malaysia,

t(966) = 9.07, p \ .001], there was no significant differ-

ence between the Indian scores and the Malaysian

scores [t(514) = 2.54, p = .012]. Scores on the imagina-

tion sub-scale were significantly different between groups,

F(2,1236) = 48.37, p \ .001, post hoc t tests found that

both the Indian and Malaysian scores were significantly

higher than the UK scores [UK–India, t(992) = 9.74,

p \ .001; UK–Malaysia, t(966) = 4.24, p \ .001], also the

Indian scores were significantly higher than the Malaysian

scores [t(514) = 4.32, p \ .001]. Scores on the attention to

detail sub-scale were not significantly different between

groups, F(2,1236) = 2.57, p = .56. Scores on the attention

switching sub-scale were significantly different between

groups, F(2,1236) = 41.88, p \ .001, post hoc t tests

found that both the Indian and Malaysian scores were

significantly higher than the UK scores [UK–India,

t(992) = 4.12, p \ .001; UK–Malaysia, t(966) = 8.68,

p \ .001], also the Malaysian scores were significantly

higher than the Indian scores [t(514) = 4.55, p \ .001].5

Factor Structure Analysis

A principal component analysis (PCA) was conducted on

each of the three datasets in order to assess whether similar

behaviours clustered together in the different cultures. PCA

using orthogonal (varimax) rotation was chosen for factor

structure analysis to maximize the independent variance

accounted for by the factors and produce easily interpret-

able factors. As in other papers analysing the factor

structure of the AQ (e.g. Austin 2005; Hoekstra et al. 2008;

Kloosterman et al. 2011; Stewart and Austin 2009) the full

1–4 point Likert scale scores were used in this analysis

with the necessary data reversed such that answers in the

autistic direction attained higher scores. A total AQ score

was calculated by summing all of the scores for each of the

items, with a maximum score of 200.

The Kaiser–Meyer–Olkin measure verified the sampling

adequacy for all three datasets as all values were [0.5:

KMO UK = 0.87; KMO Malaysia = 0.74; KMO

India = 0.64. Bartlett’s test of sphericity indicated that

correlations between items in all three samples were suf-

ficiently large for PCA, UK sample v2(1,225) = 10,567,

p \ .001; Malaysia v2(1,225) = 3,321, p \ .001; India

v2(1,225) = 2,807, p \ .001.

Thirteen factors from the UK data, sixteen factors from

the Malaysian data and seventeen factors from the Indian

data had eigenvalues greater than 1, explaining 57.3, 61.9

and 60.6 % of the variance respectively. However, a

number of commonalities values were less than 0.7 for

4 Due to 15 t-tests being conducted, statistical significance required

p \ .003.

5 These analyses were also run on the same data coded using the full

1–4 point Likert scale with the necessary data reversed such that

answers in the autistic direction attained higher scores. The nature of

the results was similar overall though there were some differences:

The difference between Indian and Malaysian AQ scores reached

significance with Indian students scoring lower overall, though still

significantly higher than UK student (Mean Indian AQ = 115.3;

Mean Malaysian AQ = 118.4; Mean UK AQ = 108.6). This

appeared to be driven by Indian males scoring lower using this

scoring method resulting in their scores lying between those of the

Malaysian and UK males (Mean Indian male AQ = 115.2; Mean

Malaysian male AQ = 119.1; Mean UK male AQ = 110.2) whereas

the nature of the results using both scoring methods was very similar

for females with Indian and Malaysian females both scoring higher

than UK females but not scoring significantly differently from each

other (Mean Indian female AQ = 115.4; Mean Malaysian female

AQ = 117.7; Mean UK female = 106.9). The effects of course

studied were very similar to those of the main analysis and no

differences in the nature of results were observed. Also for the sub-

scale analysis, results were very similar in nature to those of the main

analyses and no differences in the nature of results were observed.

J Autism Dev Disord

123

each dataset, therefore the scree plots for each factor

analysis were inspected. For each dataset there was a kink

at around 4 factors, each of these factors also had eigan-

values greater than 2. Therefore the PCAs were re-run

extracting four factors for each dataset. These components

explained 33.2 % of the variance in the UK data, 29 % of

the variance in the Malaysian data and 25 % of the vari-

ance in the Indian data.

The lists of component loadings for all three datasets

showing values greater than 0.4 in the rotated component

matrix are presented in Tables 2, 3, 4.6 Consistent with

previous literature, the first component in all three samples

represents a social factor, specifically we suggest that the

first factor in each sample is ‘‘social situation enjoyment’’.

As can be seen in Tables 2, 3, 4, many of the items in this

factor were the same in each dataset. This was a very clear

factor in each of the three datasets, accounting for 16.1, 9.1

and 13.7 % in the UK, Indian and Malaysian samples

respectively. The second factor in each of the three datasets

can be described as ‘‘social communication’’ however, in

both the Indian and the Malaysian sample this factor was

also populated by items which can be described as

‘‘attention to detail’’ indicating that this factor represents a

construct that would naturally fall into two separate cate-

gories in the UK. This factor also had very poor internal

consistency in the Indian and Malaysian sample. In the UK

sample, factor three was a clear ‘‘attention to detail’’ factor.

‘‘Attention to detail’’ was also a clear theme in factor four

in both the Malaysian and Indian samples. However in the

Malaysian sample this factor contained some social items

and again this factor displayed poor internal consistency in

both of the Eastern samples. The final factor in the UK

sample was a clear ‘‘imagination’’ factor. Factor three in

the Malaysian sample was also a clear ‘‘imagination’’

factor. However, no such factor was present in the Indian

sample rather another factor termed ‘‘social awareness’’

was the final factor in this sample. It is therefore clear that

many commonalities were observed in the factor structures

in each of the three cultures tested. However, some of the

factors in the Indian and Malaysian data did not naturally

fall into discrete categories as we typically recognise them

in Western cultures.

Discussion

In the present study Malaysian and Indian students tended

to self-report more autistic traits than UK students. In

previously reported studies on UK samples, males tend to

score higher than females and science students tend to

score higher than non-science students (Baron-Cohen et al.

2001; Wheelwright et al. 2006). This pattern was replicated

in the current study with these general patterns emerging

across all three populations, indicating that these patterns

are robust and independent of cultural influences, thus

providing some support for the validity of the AQ as a tool

for use in India and Malaysia. The internal consistency of

the AQ overall was moderate in the Eastern samples

compared to being good in the Western sample. Analysis of

the data using Baron-Cohen et al. (2001) conceptually

Fig. 4 Mean AQ sub-scale

scores across cultures.

** p \ .001

6 An additional PCA was run on 35 % of the UK data (n = 253) to

check whether a similar factor structure in the UK sample would

emerge in a sample that was of similar size to the Indian and

Malaysian samples. The same 4 factors emerged. The ‘‘social

situation enjoyment’’, ‘‘attention to detail’’ and ‘‘imagination’’ factors

actually accounted for more variance (18.4, 6.5 and 5.5 %) in this

reduced dataset while the ‘‘poor social communication’’ factor

accounted for somewhat less variance (4.5 %) than in the full dataset.

The vast majority of items that loaded onto the 4 factors in the full

dataset factor analysis also appeared in the reduced dataset factor

analysis.

J Autism Dev Disord

123

derived factor structure indicated that differences between

the groups varied across sub-scales. Both Indian and

Malaysian students scored more highly than UK partici-

pants on four out of the five sub-scales: communication;

social skill; imagination and attention switching. No dif-

ferences in scores on the attention to detail sub-scale were

observed. In addition, Indian students scored more highly

than both other groups on the imagination sub-scale.

Malaysian students scored more highly than both other

groups on the attention switching scale. Potential reasons

for these elevated scores are discussed in later paragraphs.

Despite the differences in the overall amount of autistic

traits in each sample, there were similarities in the factor

structures that emerged across cultures, indicating that the

same types of behaviours tended to group together in the

different cultures. This was particularly true for the first

factor in each of the principal component analyses, with

‘‘social situation enjoyment’’ emerging as a clear factor

Table 2 Factor structure of the AQ—UK

Q Loading Item

Factor 1: social situation enjoyment (% of variance = 16.14) Cronbach’s a = 0.68

38 0.795 I am good at social chit-chat

11 0.794 I find social situations easy

44 0.773 I enjoy social occasions

47 0.730 I enjoy meeting new people

17 0.725 I enjoy social chit-chat

22 0.694 I find it hard to make new friends

26 0.622 I frequently find that I don’t know how to keep a conversation going

13 0.587 I would rather go to a library than a party

15 0.560 I find myself drawn more strongly to people than to things

1 -0.541 I prefer to do things with others rather than on my own

46 0.511 New situations make me anxious

Factor 2: poor social communication (% of variance = 6.74) Cronbach’s a = 0.57

27 0.556 I find it easy to ‘‘read between the lines’’ when someone is talking to me

45 0.549 I find it difficult to work out people’s intentions

36 0.519 I find it easy to work out what someone is thinking or feeling just by looking at their face

10 0.494 In social groups I can easily keep track of several different people’s conversations

42 0.491 I find it difficult to imagine what it would be like to be someone else

32 0.485 I find it easy to do more than one thing at once

30 -0.472 I don’t usually notice small changes in a situation or person’s appearance

20 0.468 When I’m reading a story I find it difficult to work out the characters’ intentions

33 0.460 When I talk on the phone I’m not sure when it’s my turn to speak

48 0.426 I am a good diplomat

7 0.406 Other people frequently tell me that what I’ve said is impolite, even though I think it is polite

Factor 3: attention to detail (% of variance = 5.74) Cronbach’s a = 0.62

23 0.609 I notice patterns in things all the time

9 0.603 I am fascinated by dates

6 0.583 I usually notice car number plates or similar strings of information

12 0.568 I tend to notice details that others do not

41 0.508 I like to collect information about categories of things (e.g. types of car, types of bird, types of train, types of plant etc.)

19 0.506 I am fascinated by numbers

16 0.481 I tend to have very strong interests, which I get upset about if I can’t pursue.

5 0.452 I often notice small sounds when others do not

Factor 4: imagination (% of variance = 4.56) Cronbach’s a = 0.48

14 0.603 I find making up stories easy

3 0.497 If I try to imagine something I find it very easy to create a picture in my mind

50 0.462 I find it easy to play games with children that involve pretending

34 0.439 I enjoy doing things spontaneously

8 0.403 When I’m reading a story, I can easily imagine what the characters might look like

J Autism Dev Disord

123

that accounted for a large proportion of the variance in

each data set. This is in line with findings from previous

factor analytic studies, which have most consistently found

a first social factor that accounts for a large proportion of

the variance (e.g. Austin 2005; Hoekstra et al. 2008).

Social communication and attention to detail also emerged

strongly in each factor analysis. However, these factors

merged into a single factor in both the Malaysian and

Indian samples, which was not the case for the UK sample

where social communication and attention to detail were

separate. This suggests that certain constructs group toge-

ther differently in Eastern cultures, specifically that good

attention to detail and poor social communication are more

closely linked in India and Malaysia than in the UK.

Imagination emerged as a clear factor in the UK and

Malaysian samples. However, this was not present in the

Indian data.

How we behave in social situations and how we per-

ceive and interpret others’ behaviour is very much influ-

enced by our culture. We hypothesised that underlying

cultural differences between Western and Eastern cultures

may impact on the expression of autistic traits across cul-

tures. We predicted that participants from the Eastern

cultures of India and Malaysia may report less autistic traits

than participants from the Western UK culture due to them

displaying more social orientation and having a more

Table 3 Factor structure of the AQ—Malaysia

Q Loading Item




48 0.695 I am a good diplomat.





15 0.512 I find myself drawn more strongly to people than to things

13 0.504 I would rather go to a library than a party

10 0.457 In a social group, I can easily keep track of several different people’s conversations

46 0.470 New situations make me anxious

Factor 2: good attention to detail and poor social communication (% of variance = 5.70) Cronbach’s a = 0.12

6 -0.442 I usually notice car number plates or similar strings of information

9 -0.432 I am fascinated by dates

10 0.411 In a social group, I can easily keep track of several different people’s conversations


29 -0.49 I am not very good at remembering phone numbers

30 -0.485 I don’t usually notice small changes in a situation, or a person’s appearance


37 0.464 If there is an interruption, I can switch back to what I was doing very quickly

49 -0.505 I am not very good at remembering people’s dates of birth

Factor 3: imagination (% of variance = 5.22) Cronbach’s a = 0.47

21 0.551 I don’t’ particularly enjoy reading fiction

8 0.497 When I’m reading a story, I can easily imagine what the characters might look like

20 0.486 When I’m reading a story, I find it difficult to work out the characters’ intentions

50 0.459 I find it very easy to play games with children that involve pretending

40 0.456 When I was young, I used to enjoy playing games involving pretending with other children

Factor 4: social awareness and attention to detail (% of variance = 4.38) Cronbach’s a = 0.45

7 0.490 Other people frequently tell me that what I’ve said is impolite even though I think it is polite

18 0.545 When I talk, it isn’t always easy for others to get a word in edgeways

39 0.490 People often tell me that I keep going on and on about the same thing

20 0.444 When I’m reading a story, I find it difficult to work out the characters’ intentions

23 0.418 I notice patterns in things all the time

41 0.405 I like to collect information about categories of things (e.g. types of car, types of bird, types of train, types of plant etc.)

J Autism Dev Disord

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holistic, field-dependent information processing style

(Masuda and Nisbett 2006; McKone et al. 2010). However,

conversely we found more self-reported autistic traits in the

Eastern cultures (India and Malaysia) compared to the UK.

Below we discuss some alternate explanations regarding

specific cultural differences that may have led to this

finding.

As previously discussed, people belonging to Western

cultures (such as the UK) are more likely to describe

themselves and others, in terms of attributes within the

person and mental dispositions, as well as to draw causal

attribution for behaviour (person-centred attributions). On

the contrary, people belonging to Eastern cultures (such as

India and Malaysia) are more likely to use more situational

descriptions and explanations (situation-centred attribu-

tions) (Markus and Kitayama 1991). Making judgements

based on mental dispositions or traits is a social skill.

Therefore, there may be greater scope for practicing these

types of social skills in Western cultures. Thus, qualitative

differences in certain social skills may arise between

cultures. In support of this suggestion Masuda et al. (2008)

found a cultural difference in focus between the individual

and the situation when participants were asked to interpret

facial expressions. While American participants focused

exclusively on faces while inferring emotions, Japanese

participants attended to the whole social context. In addi-

tion, Eastern societies often discourage open expression of

emotions (Matsumoto 2009). Thus, items assessing social

skill, such as ‘understanding what someone is thinking and

feeling from their face’ (item 36), would not be common in

such cultures. Similarly, many behaviours that are accept-

able in Western societies may not be considered ‘polite’ in

Eastern societies. For example, showing boredom when

someone is talking (item 31) aimed to assess communica-

tion, but this behaviour may not be considered acceptable

and hence not widely exhibited in certain cultures. In

Eastern cultures the social environment is typically highly

structured, with social standing playing a very important

role in social interactions and in determining appropriate

behaviour (Nisbett and Masuda 2003). The social role

Table 4 Factor structure of the AQ—India

Q Loading Item






47 0.597 I enjoy meeting new people

Factor 2: good attention to detail and poor social communication (% of variance = 6.17) Cronbach’s a = 0.20

23 -0.499 I notice patterns in things all the time


32 0.467 I find it easy to do more than one thing at once

37 0.449 If there is an interruption, I can switch back to what I was doing very quickly

14 0.422 I find making up stories easy


19 -0.418 I am fascinated by numbers

12 -0.414 I tend to notice details that others do not

31 0.402 I know how to tell if someone listening to me is getting bored

Factor 3: social awareness (% of variance = 5.36) Cronbach’s a = 0.53

35 0.540 I am often the last to understand the point of a joke

39 0.515 People often tell me that I keep going on and on about the same thing

20 0.485 When I’m reading a story I find it difficult to work out the characters’ intentions

18 0.417 When I talk, it isn’t always easy for others to get a word in edgeways



33 0.491 When I talk on the phone I’m not sure when it’s my turn to speak

Factor 4: attention to detail (% of variance = 4.09) Cronbach’s a = 0.29

6 0.576 I usually notice car number plates or similar strings of information

30 0.502 I don’t usually notice small changes in a situation, or a person’s appearance

9 0.443 I am fascinated by dates

J Autism Dev Disord

123

rather than the individual is seen as the unit of explanation

(Miller 1984). In other words external factors like social

roles and relationships have better predictive power for

behaviour in Eastern society. Under such circumstances

people belonging to such societies would focus less on

‘‘reading between the lines’’ (item 27), an item aimed to

assess communication, and more on who is talking. Some

of the items on the AQ relevant to imagination are focussed

on pretend play. Research shows that culture influences the

frequency and nature of play. Edwards (2000) reported that

in India fewer than half the children they observed showed

fantasy play. Farver and Shin (1997) found Anglo-Ameri-

can children to be involved in pretend play more during

free play than Korean American Children. These differ-

ences would naturally elicit scores in the ‘‘autistic’’ direc-

tion on the imagination sub-scale in the Eastern culture in

the sample tested. All of these examples point towards

social skills being more encouraged, and autistic traits

being more discouraged, in Western cultures. It is therefore

perhaps not so surprising that participants from the UK

self-reported having better social and communication skills

than participants from India and Malaysia.

A potential reason for the observed difference in factor

loading and factor structure between the cultures may be

because certain items are interpreted differently in these

cultures, a suggestion that was also put by Norbury and

Sparks (2013). To provide a specific example, one of the

items under attention to detail is ‘I usually notice car

numbers or similar strings of information’ (item 6), which

aims to assess attention to detail. This may be interpreted

differently in India as car number plates are a matter of

religious belief and social prestige. The demand for these

numbers (for example, numbers like 777, 999 and 9999 is

auctioned at Rs. 50,000 and the number 786, considered

sacred by Muslims—the data in India was collected in the

Malabar region of Kerala which has a strong Muslim

presence—has a minimum price tag of Rs. 25,000 or $470

US) is so high that the Kerala State Motor Vehicles

Department auctions many, so called, ‘fancy’ numbers

(Paul 2011). Therefore a typical behaviour in India would

be to pay attention to car number plates, whereas this

would be deemed as atypical, or having ‘‘heightened

attention to detail’’ in the West, hence being indicative of

an autistic trait by the AQ. Similar preference for specific

numbers also exists in Malaysia. The word for number 4

sounds similar to the word for ‘‘death’’ in Cantonese so

many Chinese Malaysians would be wary of a car number

plate containing the number four. Eight on the other hand is

an auspicious number as the word sounds similar to the

word for ‘‘prosperity’’. Malaysians will pay to have a plate

that contains one or more eight (Pandiyan 2012). Eastern

societies generally prefer doing activities in groups. Often,

a person in authority (family head, teacher or manager)

makes, or at least sanctions, the final decision. This leaves

less free choice regarding participation in certain activities.

So for example, the activities of going to the theatre or a

museum (item 24) would likely both be social activities in

India with the family head deciding whether others are

going. It would therefore not be valid to interpret a par-

ticipants’ preference for going to the museum as a prefer-

ence for a non-social activity. Thus, an activity that is

considered to be a social one or one that pertains to

attention in one culture need not be considered to be so in

another.

We therefore propose that the main reason for individ-

uals in the Indian and Malaysian populations scoring

higher on the AQ is the influence that culture has on many

behavioural and cognitive processes including many that

are considered to be important diagnostic indicators of

ASD. We propose that the AQ is culturally sensitive to

some degree, as indicated by similar patterns of results

across all three cultures in terms of gender and course

studied, but it does not appear to account for cultural norms

as demonstrated by the heightened scores in the Indian and

Malaysian samples overall. It is therefore clear that if

Western based diagnostic criteria are used exclusively in

non-Western cultures a subsequent artificial increase in

frequency of diagnosis may occur. It is possible that certain

AQ items may be interpreted differently in Eastern cultures

leading to differences in how behaviour is categorised. For

example, something considered as pertaining to attention in

the West may in fact have a social dimension in an Eastern

society. It seems that the way core features are perceived

may vary as a function of cultural expectation. Thus while

autism should, of course, be characterised by deficits in

socialization, communication and imagination across all

cultures, the particular behaviours that constitute these

deficits may vary in nature or intensity across cultures. It is

important to consider that, as suggested by Daley (2004)

and Norbury and Sparks (2013), exclusively using Western

criteria for diagnosis without proper adaptation for other

cultures may result in misdiagnosis, delay in identification

of autism in children as well as not identifying the milder

forms of ASD in non-Western cultures.

It is also worth bearing in mind that the populations

tested were groups of students in all three cultures.

Answers from these groups may not be representative of

each society as a whole. As discussed, the Indian and

Malaysian participants showed higher levels of self-

reported autistic traits than the British participants. It is

currently unclear whether the profiles outlined are repre-

sentative of the populations of the three cultures overall or

whether individuals with more autistic traits in Eastern

cultures (India and Malaysia) tend to be more academically

successful. Asia is considered by some to be on the cusp of

becoming the technological hub of the world (Dhingra

J Autism Dev Disord

123

2011). As a result there may be a societal inclination and

more awareness regarding the sciences than the arts. It is

possible that this might be emerging in the academic

structure in the country. However these are issues that need

further investigation. At the very least, it seems that pos-

sessing more traits associated with the broader autistic

profile, as specified in the West, is not detrimental to

academic success within Eastern cultures.

In summary, the findings of the current study demon-

strate that autistic traits were reported more frequently in

the Eastern than the Western cultures tested, with higher

scores on the Autistic-spectrum Quotient (Baron-Cohen

et al. 2001) being observed in Indian and Malaysian stu-

dents compared to UK students. The typically observed

pattern of males scoring higher than females and science

students scoring higher than non-science students (e.g.

Baron-Cohen et al. 2001; Wheelwright et al. 2006) was

observed in all three samples. No differences were

observed between groups on items relating to attention to

detail. However, students in the Eastern samples scored

higher than the UK sample on items relating to social skill,

attention switching, communication and imagination, with

the Indian students scoring higher than both other groups

on imagination and the Malaysian students scoring higher

than both other groups on Attention Switching. Internal

consistency analyses revealed that ‘‘imagination’’ as con-

ceptualised by the AQ may not be a reliable construct in

Indian culture. Similarities in empirically derived factor

structures emerged between groups, with each group dis-

playing a clear ‘‘social situation enjoyment’’ factor. Social

communication and attention to detail also emerged

strongly in each of the cultures, though these factors

appeared to be more closely linked in the Eastern samples

than in the UK sample. Imagination emerged as a factor in

the UK and Malaysian samples but not in the Indian

sample, again questioning the validity of ‘‘imagination’’ as

a construct. We propose that differences in social structure

and cultural interpretation strongly contribute to the

observed differences between groups. As each of the

samples were drawn from academically successful indi-

viduals (students) it is also clear that possessing slightly

elevated levels of autistic traits is not detrimental to aca-

demic success in Eastern cultures and may even be valued

within these societies.

Acknowledgments MF was supported by a fellowship from the

Leverhulme Trust.

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A Cross-Cultural Comparison of Autistic Traits in the UK, India and Malaysia

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