ORIGINAL PAPER

Validating the Repetitive Behavior Scale-Revised in YoungChildren with Autism Spectrum Disorder

Pat Mirenda • Isabel M. Smith • Tracy Vaillancourt • Stelios Georgiades •

Eric Duku • Peter Szatmari • Susan Bryson • Eric Fombonne •

Wendy Roberts • Joanne Volden • Charlotte Waddell • Lonnie Zwaigenbaum •

The Pathways in ASD Study Team

Published online: 20 April 2010

� Springer Science+Business Media, LLC 2010

Abstract This study examined the factor structure of the

Repetitive Behavior Scale-Revised (RBS-R) in a sample of

287 preschool-aged children with autism spectrum disorder

(ASD). A confirmatory factor analysis was used to examine

six competing structural models. Spearman’s rank order

correlations were calculated to examine the associations

between factor scores and variables of interest. The 3- and

5-factor models were selected as preferable on the basis of

fit statistics and parsimony. For both models, the strongest

correlations were with problem behavior scores on the

Child Behavior Checklist and repetitive behavior scores on

the ADI-R. Developmental index standard scores were not

correlated with factors in either model. The results confirm

the utility of the RBS-R as a measure of repetitive

behaviors in young children with ASD.

Keywords Repetitive behavior �Autism spectrum disorder � Factor analysis �Internal validity � External validity � Preschool

Introduction

Autism spectrum disorders (ASD; also termed Pervasive

Developmental Disorders in the DSM-IV-TR) are defined

by significant difficulties with social communication and

unusual patterns of behavior (e.g. restricted interests,

repetitive activities, stereotyped movements, and/or unu-

sual responses to sensory stimuli) (American Psychiatric

Association 2000). The area of social communication has

received considerable research attention over the years;

however, systematic study of the restricted and repetitive

behaviors (RRB) domain has increased only recently. RRB

includes both behavior that has been characterized as

‘‘lower level’’ repetitive sensory motor behavior (RSMB),

such as hand flapping, rocking, and humming, motoric

compulsions, and some self-injurious behaviors; and

‘‘higher level’’ insistence on sameness behavior (ISB), such

as pursuit of narrow circumscribed interests, insistence that

routines always occur in the same way, and repetitive use

of language (Bodfish et al. 2000; Lewis and Bodfish 1998).

The distinction between RSMB and ISB has been rein-

forced in reviews (e.g., Turner 1999) and subsequently in

several factor analytic studies with both adult and child

samples (e.g., Bodfish et al. 2000; Cuccaro et al. 2003;

Lam and Aman 2007; Szatmari et al. 2006). For example,

P. Mirenda (&)

University of British Columbia, 2125 Main Mall,

V6T 1Z4 Vancouver, BC, Canada

e-mail: pat.mirenda@ubc.ca

I. M. Smith � S. Bryson

IWK Health Sciences Centre, Dalhousie University,

Halifax, NS, Canada

T. Vaillancourt

University of Ottawa, Ottawa, ON, Canada

S. Georgiades � E. Duku � P. Szatmari

McMaster University, Hamilton, ON, Canada

E. Fombonne

McGill University, Montreal, QC, Canada

W. Roberts

The Hospital for Sick Children, University of Toronto, Toronto,

ON, Canada

J. Volden � L. Zwaigenbaum

University of Alberta, Edmonton, AB, Canada

C. Waddell

Simon Fraser University, Burnaby, BC, Canada

123

J Autism Dev Disord (2010) 40:1521–1530

DOI 10.1007/s10803-010-1012-0

Mooney et al. (2009) conducted an exploratory factor

analysis on RRB items in the Autism Diagnostic Interview-

Revised (ADI-R; Rutter et al. 2003), using data from 137

preschool children with ASD and 77 developmentally

delayed children without ASD. Their two-factor solution

was characterized as consisting of ‘‘lower-order’’ behaviors

(e.g., repetitive use of objects, hand and finger manner-

isms—that is, RSMB) and ‘‘higher-order’’ behaviors (e.g.,

compulsions and rituals—that is, ISB). The two factors

accounted for 43% of variance in the entire sample, and

31% of variance for the ASD group alone. Similarly, Lam

et al. (2008) conducted a factor analysis using ADI-R items

from a more heterogeneous sample of individuals with

autism aged 20 months to 29 years (mean age = 9 years)

and identified three factors—RSMB, ISB, and Circum-

scribed Interests—each of which was composed of three

items from the ADI-R. Overall, the three factors accounted

for 52% of the variance in the sample. The discrepancy

between the Mooney et al. and Lam et al. analyses might

be explained by differences in the chronological ages of the

two samples, among other possible variables; however, the

relationship between age and RRBs has not been system-

atically addressed in factor analytic studies to date.

The measurement of RSMB and ISB has been accom-

plished in a number of ways. The most common has been

to use relevant items from the ADI-R (e.g., Bishop et al.

2006; Cuccaro et al. 2003; Honey et al. 2008; Mooney

et al. 2009; Richler et al. 2007; Szatmari et al. 2006). Other

researchers have used RRB-related items from various

parent interviews and scales (e.g., Bopp et al. 2009; Mili-

terni et al. 2002), or a variety of formal questionnaires

(e.g., Bodfish et al. 2000; Cuccaro et al. 2003; Gabriels

et al. 2005). Finally, a few studies have employed direct

observations of RRB (Epstein et al. 1985; Goldman et al.

2009; Morgan et al. 2008; Watt et al. 2008) The use of such

diverse measurement approaches makes it difficult to

compare results across studies.

Papageorgiou et al. (2008) noted that, since RRBs

encompass a variety of behaviors that are not addressed in

the ADI-R, future research examining the structure of these

behaviors should make use of more comprehensive

instruments. One such measure is the Repetitive Behavior

Questionnaire-2 (Leekam et al. 2007) and its predecessor,

the Repetitive Behavior Interview (Turner 1996), both

which have been used in a few published studies to date

(Ozonoff et al. 2000; Zandt et al. 2007). Other studies (e.g.,

Bodfish et al. 2000; Esbensen et al. 2008; Gabriels et al.

2005) have examined RRB using either the original or

revised version of the Repetitive Behavior Scale (RBS;

Bodfish et al. 1999) which samples a broader array of

behaviors than either the RBQ-2 or the ADI. The RBS

comprises six subscales, each a set of discrete, observable

topographies of restricted, repetitive behaviors that were

based on a theoretical framework involving latent pro-

cesses. The subscales include: (a) Stereotyped Behavior

(i.e., movements with no obvious purpose that are repeated

in a similar manner); (b) Self-injurious Behavior (i.e.,

actions that cause or have the potential to cause redness,

bruising, or other injury to the body); (c) Compulsive

Behavior (i.e., behavior that is repeated and performed

according to a rule or involves things being done ‘‘just

so’’); (d) Ritualistic Behavior (i.e., performing activities of

daily living in a similar manner); (e) Sameness Behavior

(i.e., resistance to change, insisting that things stay the

same); and (f) Restricted Behavior (i.e., limited range of

focus, interest, or activity). Items are rated on a four-point

Likert scale ranging from (0) ‘‘behavior does not occur’’ to

(3) ‘‘behavior occurs and is a severe problem,’’ and raters

are asked to refer to the previous month when completing

the scale.

The current version, the Repetitive Behavior Scale-

Revised (RBS-R; Bodfish et al. 2000) contains 43 items

that were drawn from other instruments and grouped the-

oretically based on the authors’ clinical experience. In a

recent study, Lam and Aman (2007) conducted an

exploratory factor analysis of the RBS-R that utilized data

from 307 participants who ranged in age from 3 to 48 years

(mean = 15 years). The authors identified five factors that

accounted for 47.5% of the variance among items,

including: (a) Rituals/Sameness, (b) Self-injurious Behav-

ior, (c) Stereotypic Behavior, (d) Compulsive Behavior,

and (e) Restricted Interests. Items from two separate con-

ceptually-based subscales on the RBS-R (Ritualistic

Behavior and Sameness Behavior) were combined in a

single factor (Rituals/Sameness), while 22 of the remaining

26 items loaded onto the other four factors in a manner

consistent with the structure of the original RBS-R.

The design employed by Lam and Aman (2007) invites

consideration at several levels. First, they recruited par-

ticipants through a mailed invitation to members of a

statewide autism society, with a response rate of 32%.

Thus, the individuals with ASD who were the focus of the

study may have represented primarily those with active,

informed caregivers and/or those with high rates of RRBs

whose caregivers were highly motivated to complete the

assessment. Further, no standard approach was used for

diagnosis of participants in this study, and it was not pos-

sible to verify that their diagnoses were accurate. In addi-

tion, the broad age range of the individuals with ASD in

Lam and Aman’s study (i.e., 3–48 years) limits interpret-

ability of the findings. Perhaps the items load on factors

differently in different age groups; in particular, RRB

profiles of very young children may differ from those of

older children and adults. This is an important issue for

clarification, since the five-subscale grouping of Lam and

Aman was subsequently used in a recent study of 712

1522 J Autism Dev Disord (2010) 40:1521–1530

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individuals aged 2–62, 74 (10.4%) of whom were 2–

4 years old (Esbensen et al. 2008). The results of this study

indicated age-related patterns of differences in RRB among

the five subscales of the RBS-R, with the most significant

age-related decreases seen for restricted interests and ste-

reotyped movements. However, if the RBS-R factor

structure differs for young children, the results of this

analysis may not apply to that age group. Indeed, RRB

patterns seen in children may differ from those in older

individuals (see Richler et al. 2007).

Because of the increasing use of the RBS-R in research,

it is important to clarify the extent to which it is applicable

to young children with ASD. Previous research suggests

that the relationship between chronological age and RRB is

not straightforward. In general, RSMBs have been found to

be less frequent among older individuals (Esbensen et al.

2008; Lam and Aman 2007; Militerni et al. 2002). In

contrast, the findings on IS have been mixed. Bishop et al.

(2006) reported that IS behaviors become more frequent

with age, while Esbensen et al. (2008) reported a decrease.

Given these mixed findings, we were interested in exam-

ining the entire range of both RSMB and IS behaviors in

young children with ASD. Thus, the primary purpose of

this study was to examine the factor structure (i.e., internal

validity; see Cantwell 1996) of the RBS-R in a large

inception cohort of young children (aged 2–5 years), using

a confirmatory factor analytic approach. A secondary goal

was to examine external validity (Cantwell 1996) of the

RBS-R by examining correlations between RBS-R factors

and several variables that have been related to repetitive

behavior in previous research. These include children’s

developmental level (Bishop et al. 2006; Honey et al.

2008), adaptive behavior (Cuccaro et al. 2003; Hus et al.

2007; Szatmari et al. 2006), autism symptoms (Watt et al.

2008), and other unusual behaviors (Zandt et al. 2007).

Other studies that have examined the measurement model

of the RBS were not able to examine correlates from as

wide an array of clinical characteristics.

Method

Participants and Procedure

The participants were children participating in a Canadian

multi-site longitudinal study (Pathways in ASD) examining

the developmental trajectories of children with ASD. All

participants met the following inclusion criteria: (a) recent

(i.e., within 4 months) clinical diagnosis of ASD, con-

firmed by both the Autism Diagnostic Observation Sche-

dule (ADOS; Lord et al. 2002) and the Autism Diagnostic

Interview-Revised (ADI-R; Rutter et al. 2003), and by a

diagnosis assigned by a clinician using DSM-IV criteria

(American Psychiatric Association 2000); and (b) chrono-

logical age between 24 and 60 months at the time of

diagnosis. Children were excluded from the study if they

had: (a) cerebral palsy or other neuromotor disorders

interfering with study assessments; (b) a known genetic or

chromosomal abnormality; or (c) a severe vision or hearing

impairment. Invitation to participate in the study was

extended to all eligible families from five regional ASD

referral centres across Canada. To ensure independence of

observations, only one child per family was recruited to the

study. The study was approved by the local Research

Ethics Boards at participating sites.

The present sample consisted of 287 preschool children

with ASD (242 boys and 45 girls) with a mean age of

40.72 months at completion of the RBS-R (range =

24.1–64.0 months; SD = 9.27). Additional descriptive

statistics for the children at entry into the study are pre-

sented in Table 1.

Measures

Five measures were used in the present study as indices of

repetitive behavior, autism symptoms, adaptive behavior,

problem behavior, and cognitive development.

Repetitive Behavior Scale-Revised (Bodfish et al. 2000)

This is an empirically derived clinical rating scale for

measuring the presence and severity of a variety of forms

of restricted, repetitive behavior that are characteristic of

persons with ASD. The Repetitive Behavior Scale-Revised

(RBS-R) is designed to provide a quantitative, continuous

Table 1 Descriptive statistics for sample

Characteristic N % Mean SD Min–Max

Sex 287

Male 242 84.3

Female 45 15.7

Child’s age at assessment

(months)

287 40.7 9.3 24.1–64.0

Child’s age group

2-year-olds 105 36.6

3-year-olds 125 43.6

4-year-olds 57 19.9

Merrill-Palmer-Revised

Developmental Index age

equivalent (months)

272 23.1 11.4 5–68

Ethnicity

Caucasian 206 71.8

Other 81 28.2

J Autism Dev Disord (2010) 40:1521–1530 1523

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measure of the spectrum of repetitive behaviors. The RBS-

R consists of 43 items distributed across six conceptually

derived subscales: Stereotyped Behavior, Self-injurious

Behavior, Compulsive Behavior, Routine Behavior,

Sameness Behavior, and Restricted Behavior. This scale

was completed by parent informants.

Autism Diagnostic Interview: Revised (Rutter et al. 2003)

The Autism Diagnostic Interview: Revised (ADI-R) is a

standardized semi-structured interview used in the differ-

ential diagnosis of the ASDs. It is designed to be used with

a parent or caregiver who is familiar with the develop-

mental history and current behavior of individuals over the

age of 2. It consists of three major domains: (1) language

and communication, (2) reciprocal social interaction, and

(3) restricted, repetitive, and stereotyped behaviors and

interests. The ADI-R uses a cut-off point for each of the

three domains, which provides a reliable diagnostic algo-

rithm that is accurate in differentiating autism from other

developmental disorders.

Autism Diagnostic Observation Schedule (Lord et al. 2002)

The Autism Diagnostic Observation Schedule (ADOS) is a

semi-structured assessment that consists of activities that

provide interesting, standard contexts in which interactions

can occur. The activities allow the assessor to observe

social and communication behaviors related to the diag-

nosis of ASD. Ratings for the behaviors are entered into a

reliable diagnostic algorithm that differentiates both autism

and the broader category of ASD from other developmental

disorders.

Vineland Adaptive Behavior Scales Second Edition

(Sparrow et al. 2005)

The Vineland Adaptive Behavior Scales Second Edition

(VABS II) assesses child adaptive behavior in the com-

munication, socialization, daily living skills, and motor

domains, and expresses overall functioning in the Adap-

tive Behavior Composite (ABC) score. The VABS II is

administered to a parent or caregiver using a semi-

structured interview format. Open-ended questions gather

in-depth information and promote rapport between the

interviewer and respondent.

Child Behavior Checklist (Achenbach and Rescorla 2000)

The Child Behavior Checklist (CBCL 1.5-5) obtains par-

ents’ ratings of 99 problem items, and is a well-standardized

measure of externalizing and internalizing behavior prob-

lems in preschool children.

Merrill-Palmer-Revised Scales of Development

(Roid and Sampers 2004)

This is an individually administered measure of develop-

ment that is appropriate for children ages 2–78 months of

age. The Developmental Index is comprised of the cogni-

tive, receptive language, and fine motor scales.

Data Analysis

Baseline data were used in a confirmatory factor analysis

(CFA; Muthen and Muthen 2007) to describe the structure

of the RBS-R. CFA was applied to test the six competing

structural models proposed in the literature (APA 2000;

Bodfish et al. 2000; Lam and Aman; 2007; Lam et al.

2008; Szatmari et al. 2006). The specific models ranged

from 1 to 6 factors (Fig. 1). Five statistical indices,

including the model chi-square/degrees of freedom

(v2/df), the Comparative Fit Index (CFI), the Tucker-

Lewis Index (TLI), the Root Mean Square Error of

Approximation (RMSEA), and the Standardized Root

Mean Square Residual (SRMR), were used to evaluate the

goodness-of-fit of each model to the data. In general, a

v2/df value less than 3.0, CFI and TLI values greater than

0.90, SRMR values below 0.08, and RMSEA values of

0.06 or less indicate a good model fit (Briggs and Cheek

1986; Hu and Bentler 1999). The Akaike Information

Criterion (AIC) was also used as a criterion of model fit,

with lower values indicating a better fit (Akaike 1987).

Figure 1 depicts the six competing models tested in the

analyses. The internal consistency for each unique factor

from the six models was assessed using Cronbach’s alpha

(a). A Cronbach’s a value of 0.70 or higher indicates a

scale with acceptable internal consistency (Heppner et al.

1999).

In addition, non-parametric Spearman’s rank correla-

tions were calculated to examine the association between

factor scores and other variables of interest. Spearman’s

rank correlation coefficients were used because the dis-

tribution of the three factors was not normal. The vari-

ables of interest included the child’s age at diagnosis,

developmental level (as indexed by the Merrill-Palmer-

Revised Scales of Development (M-P-R) Developmental

Index), adaptive behavior ability (as indexed by the

VABS II Adaptive Behavior Composite), autistic symp-

toms (as indexed by the ADI-R and the ADOS), and other

problem behaviors (as indexed by raw scores on the

CBCL 1.5-5).

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Results

The CFA results for all six factor models are presented in

Table 2.

The CFA results indicate that the 3-, 4-, 5-, and 6-factor

models were all reasonably good fits for the data, based on

the fit statistics guidelines described previously. Models III

and V were selected as the preferable models on the basis

of both fit statistics and parsimony. Model III is comprised

of Factor I: Compulsive Ritualistic Sameness Behaviors

(CRSB), including RBS-R items 15–39 (a = 0.909); Fac-

tor II: Self Injurious Behaviors (SIB), items 7–14

(a = 0.811); and Factor III: Restricted Stereotyped

Behaviors (RSB), items 1–6 and 40–43 (a = 0.816). Model

V comprises Factor I: Stereotyped Behaviors (STEREO),

items 1–6 (a = 0.733); Factor II: Self Injurious Behaviors

(SIB), items 7–14 (a = 0.811); Factor III: Compulsive

Behaviors (COMP), items 15–22 (a = 0.720); Factor IV:

Ritualistic Sameness Behaviors (RITUAL/SAME), items

22–39 (a = 0.898); and Factor V: Restricted Behaviors

(RESTR), items 40–43 (a = 0.787). The primary differ-

ence between the two models was that Compulsive and

Ritualistic behaviors comprised one factor in Model V but

were separate in Model III; this was also the case for

Stereotypic and Restricted behaviors.

Table 3 presents correlations between the three factors

from Model III and the variables of interest, and Table 4

presents the same set of correlations for the five factors in

Model V.

For both models, the strongest correlations were with the

CBCL total and subscale raw scores. In addition, all factors

in Model III and all factors except SIB in Model V were

Model I(1-factor)

Stereotypy

Self-Injurious

Compulsive

RitualisticSameness

Restricted

Model II(2-factors)

Stereotypy

Restricted

Self-Injurious

Compulsive

Ritualistic

Sameness

Model III(3-factors)

Stereotypy

Restricted

Self-Injurious

Compulsive

Ritualistic

Sameness

Model IV(4-factors)

Stereotypy

Restricted

Self-Injurious

Compulsive

Ritualistic

Sameness

Model V(5-factors)

Stereotypy

Self-Injurious

Compulsive

Ritualistic

Sameness

Restricted

Model VI(6-factors)

Stereotypy

Self-Injurious

Compulsive

Ritualistic

Sameness

Restricted

Fig. 1 Six competing hypothesized models of the RBS-R factor structure

Table 2 Goodness-of-fit indices of the hypothesized latent-factor models of the RBS-R (N = 287)

Model v2 df v2/df CFI TLI RMSEA SRMR AIC

Model I (one factor) 2536.92* 860 2.94 0.629 0.610 0.082 0.082 25423.755

Model II (two factors) 2183.74* 859 2.54 0.707 0.692 0.073 0.075 25072.572

Model III (three factors) 1998.62* 857 2.33 0.747 0.734 0.068 0.071 24891.456

Model IV (four factors) 1923.89* 854 2.26 0.763 0.750 0.066 0.068 24822.718

Model V (five factors) 1840.61* 850 2.17 0.781 0.767 0.064 0.066 24747.436

Model VI (six factors) 1830.61* 845 2.17 0.782 0.767 0.064 0.066 24747.439

Note: RBS-R repetitive behavior scale-revised, CFI comparative fit index, TLI Tucker Lewis index, RMSEA Root-mean square error of

approximation, SRMR standardized root mean square residual, AIC Akaike information criterion; numbers in bold meet the criterion set for good

model fit

* p \ .001

J Autism Dev Disord (2010) 40:1521–1530 1525

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strongly correlated with the ADI-R repetitive behaviors

total score. However, only the RSB factor in Model III and

the Restricted and Stereotypy factors in Model V were

correlated with scores on the ADOS. All factors in both

models were negatively correlated with the VABS II total

score, most strongly for RSB (Model III) and Stereotypy

(Model V). The only factors that were strongly correlated

with chronological age were those related to compulsive

ritualistic sameness behaviors (CRSB). Developmental

index standard scores as measured by the M-P-R were not

correlated with any factors in either model.

Discussion

The primary purpose of this study was to examine the

RBS-R factor structure proposed by Bodfish et al. (1999),

Bodfish and Lewis (2002) in a carefully selected sample of

Table 3 Spearman’s rank order correlation coefficients for Model III and variables of interest

Variable RBS-R Factor

CRSB SIB RSB

RBS-R age at assessment (in months) .210** -.029 .084

VABS II adaptive behavior composite -.153* -.222** -.247**

ADI-R social domain total .194** .194** .232**

ADI-R communication domain nonverbal/verbal total .137* .143* .156**

ADI-R repetitive behaviors domain total .377** .129* .374**

ADOS repetitive behaviors total (modules 1, 2, 3) .064 .075 .236**

M-P-R developmental index standard score .054 -.096 -.035

CBCL internalizing problems total raw score .629** .384** .550**

CBCL externalizing problems total raw score .531** .437** .517**

CBCL total problems raw score .648** .497** .608**

CBCL pervasive developmental problems raw score .521** .300** .497**

Note: RBS-R repetitive behavior scale-revised, VABS II Vineland adaptive behavior scale second edition, ADI-R Autism diagnostic interview-

revised, M-P-R Merrill-Palmer-revised, CRSB compulsive ritualistic sameness behaviors, SIB self injurious behaviors, RSB restricted stereotyped

behaviors

* p \ .05 level (2-tailed) ** p \ .01 level (2-tailed)

Table 4 Spearman’s rank order correlation coefficients for Model V and variables of interest

Variable RBS-R Factor

STEREO SIB COMP RITUAL/SAME RESTR

RBS-R age at assessment (in months) .037 -.029 .139* .207** .104

VABS II adaptive behavior composite -.306** -.222** -.167** -.141* -.127*

ADI-R social domain total .222** .194** .159** .196** .169**

ADI-R Communication domain nonverbal/verbal total .120* .143* .040 .169** .152*

ADI-R repetitive behaviors domain total .297** .129* .330** .362** .351**

ADOS repetitive behaviors total (modules 1, 2, 3) .270** .075 .092 .031 .148*

MP-R developmental index standard score -.099 -.096 -.032 .083 .044

CBCL internalizing problems total .455** .384** .476** .619** .494**

CBCL externalizing problems total .452** .437** .394** .531** .444**

CBCL total problems .528** .497** .491** .639** .519**

CBCL pervasive developmental problems raw score .448** .300** .415** .509** .415**

Note: RBS-R repetitive behavior scale-revised, VABS II Vineland adaptive behavior scale second edition, ADI-R Autism diagnostic interview-

revised, M-P-R Merrill-Palmer-revised, STEREO stereotyped behaviors, SIB self injurious behaviors, COMP compulsive behaviors, RITUAL/SAME ritualistic/sameness behaviors, RESTR restricted behaviors

* p \ .05 level (2-tailed) ** p \ .01 level (2-tailed)

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young children with ASD, using data gathered soon after

diagnosis. Because the RBS-R was based on a theoretical

framework involving latent processes, a confirmatory fac-

tor analysis was deemed appropriate to assess internal

validity for the present analysis (Brown 2006; Tabachnick

and Fidell 2007). The results confirm the utility of the

RBS-R as a measure of a wide range of RRBs seen in

young children with ASD.

The issue of how many factors to select following CFA

is complex and is typically addressed from multiple per-

spectives. In this study, Cronbach’s a was at least 0.72 for

the factors in all six models, indicating adequate or better

internal consistency (see Appendix). Factors with a values

between .70 and .80 are not less internally consistent than

those with a[ .80. Rather, the finding that even these

factors, which contain only a few item indicators, have

acceptable a values confirms the robustness of the

instrument overall. The fit indices for Model I, the single

factor solution, were the least acceptable, with a RMSEA

value of .082 that was well over the recommended value

of .06. This confirms previous reports indicating that the

structure of RRBs in children with ASD is not uni-

dimensional (e.g., Lam et al. 2008; Mooney et al. 2009).

The fit index results (Table 2) indicate that the 5- and

6-factor solutions were almost identical, but the 5-factor

model is more parsimonious. We propose adopting this

model when a detailed measure of RRB is required. For

example, the 5-factor model might be especially appro-

priate when assessing the extent to which specific types of

RRBs are affected by treatments that are specifically

designed for this purpose, as well as interventions that

target other areas but might have collateral effects on

RRBs. Advantages of using a measure such as the RBS-R

in such circumstances include the brief completion time;

the ability to quantify subtle differences in the degree of

impairment; and weak or no correlation with IQ, as evi-

denced by the results of our correlational analyses (see

Tables 3, 4). With regard to the latter issue, it should be

noted that we found slight variations in the size and

direction of correlations between IQ and RRB factors for

the 2-, 3-, and 4-year-olds in our sample, but none of

these differences were significant.

Both the 5- and 6-factor models show evidence of a

better statistical fit than the 3- and 4-factor models; how-

ever, the latter two models also have acceptable (and

similar) fit statistics and might be useful in some circum-

stances. In particular, the 3-factor model, consisting of

Self-injurious Behavior, Compulsive/Ritualistic/Sameness

behaviors, and Restricted Stereotypic behaviors, might be

appropriate in studies requiring broad descriptors of RRB

symptomatology, such as those that are used in genetic

quantitative trait locus (QTL) analyses. QTL is especially

useful for complex, neurobehavioral phenotypes such as

autism, the characteristics of which can be measured and

quantified along continua. It links two types of informa-

tion—phenotypic data (i.e., trait measurements) and

genotypic data (usually molecular markers)—in order to

explain the genetic basis of variation in complex traits

(Miles and Wayne 2008). This requires a tool that can

measure the trait of interest along a continuum, in a manner

that is parsimonious yet sufficiently precise to capture the

entire spectrum of variability, including subclinical mani-

festations. Recent QTL analyses in ASD have examined

nonverbal communication (Chen et al. 2006), social

responsiveness (Duvall et al. 2007), and both language

delay and RRB (Alarcon et al. 2002) as potential compo-

nent phenotypes (Szatmari et al. 2006). Interestingly, the

Alarcon et al. study found no QTL for RRB, although four

chromosomal regions were identified that may harbor

QTLs for age-at-first-word and age-at-first-phrase. The

authors noted that use of the ADI-R for measurement of

RRB might have affected the results, and suggested that

more comprehensive examination of the RRB construct is

warranted. The 3-factor RBS-R model might be useful in

this regard, since all three factors were correlated with the

ADI-R (suggesting that they measure the same construct)

but provide additional specificity.

Correlational analyses indicated moderately strong

relationships between all RBS-R factors and both inter-

nalizing and externalizing problem behaviors on the

CBCL. There were also significant correlations between

the Pervasive Developmental Disorders-oriented subscale

of the CBCL and all RBS-R factors, providing evidence

of concurrent validity. Associations were also evident

between VABS II adaptive behavior scores and all factors

in both Models III and V. The strongest of these corre-

lations was with the Stereotypic behavior factor, which

echoes the results of previous studies that have also found

correlations between RSMBs and adaptive behavior (e.g.,

Cuccaro et al. 2003; Hus et al. 2007; Szatmari et al.

2006). The relationships between RBS-R factors and

chronological age were less straightforward, with low,

positive correlations across all factors except SIB, for

which a negative correlation was evident. With the

exception of the SIB result, this supports previous work

suggesting that RRBs occur more frequently in older than

in younger children (e.g., Bishop et al. 2006; Cox et al.

1999), although our age range was admittedly narrow

(2–5 years).

The limitations of this study include our inability to

examine measurement equivalence across genders, given

the low number of girls in the study. The strengths of our

J Autism Dev Disord (2010) 40:1521–1530 1527

123

approach include the fact that we recruited an inception

cohort within a specific age range, ascertained our sample

in a systematic manner using gold standard instruments to

confirm diagnoses, and examined several correlates drawn

from previous work. We were also able to examine cor-

relates with one observational measure (the ADOS) in

addition to those that rely on parent reports (i.e., the ADI-

R, VABS II, and CBCL). In the future, we plan to use

longitudinal data to examine RRB differences of individ-

uals in various age groups as well as the progression of

RRBs as children with ASD develop over time.

In summary, the CFA results provide support for use of

the RBS-R to measure a range of RRBs in young children

with ASD. When parsimony is the primary consideration,

Model III would be an appropriate choice; and when a

more detailed examination of RRB is required, Model V

could also be used. Both are theoretically sound, with

evidence of good fit indices and high internal consistency.

From a clinical perspective, this instrument appears to be

psychometrically sound and has several advantages over

measures such as the ADI-R that were developed for other

purposes. That is, the RBS-R covers a broader range of

repetitive behaviors and was designed as a quantitative

index of RRB, whereas the ADI-R was developed to aid in

categorical diagnostic decision-making. As such, the use of

three or five RBS-R factors to measure response to treat-

ment or quantify RRBs for other purposes should provide

sufficient coverage of the domain. Moreover, more wide-

spread adoption of a broad-based instrument such as the

RBS-R to measure RRBs would enable efficient compari-

son of research results across samples of different ages,

genders, levels of development, and autism symptom

severity.

Acknowledgments This study was supported by the Canadian

Institutes of Health Research, Autism Speaks, the Government of

British Columbia, the Alberta Heritage Foundation for Medical

Research, and the Sinneave Family Foundation. The authors thank

all the families who participated in the Pathways in ASD study. The

authors also acknowledge the members of the Pathways in ASDStudy Team. These members had equal contribution to the study and

are listed here alphabetically: Liliana Abruzzese, Susan Bauld, Terry

Bennett, Ainsley Boudreau, Colin Andrew Campell, Mike Chalupka,

Lorna Colli, Melanie Couture, Bev DaSilva, Natalie Dinsdale,

Vikram Dua, Lindsay Fleming, Kristin Fossum, Nancy Garon,

Shareen Holly, Karen Kalynchuk, Kathryne MacLeod, Wendy

Mitchell, Julianne Noseworthy, Irene O’Connor, Sarah Peacock,

Teri Phillips, Sara Quirke, Jennifer Saracino, Cody Shepherd,

Rebecca Simon, Mandy Steiman, Benjamin Taylor. Ann Thompson,

Lee Tidmarsh, Larry Tuff, Stephen Wellington, Isabelle Yun, and Li

Hong Zhong.

Appendix

See Table 5.

Ta

ble

5In

tern

alco

nsi

sten

cy(C

ron

bac

h’s

a)fo

rfa

cto

rsfr

om

the

six

com

pet

ing

mo

del

s

Fac

tors

RB

S-R

Item

sC

ron

bac

h’s

aM

od

elI

Mo

del

IIM

od

elII

IM

od

elIV

Mo

del

VM

od

elV

I

Ste

reo

typ

y1

–6

.73

3X

X

Sel

f-in

juri

ou

s7

–1

4.8

11

XX

XX

Co

mp

uls

ive

15

–2

2.7

20

XX

X

Rit

ual

isti

c2

3–

29

.70

8X

Sam

enes

s3

0–

39

.87

9X

Res

tric

ted

40

–4

3.7

87

XX

Rit

ual

isti

c/sa

men

ess

23

–3

9.8

98

XX

Ste

reo

typ

y/r

estr

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d1

–6

,4

0–

43

.81

6X

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5–

39

.90

9X

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estr

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d/s

elf

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1–

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1–

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.93

0X

1528 J Autism Dev Disord (2010) 40:1521–1530

123

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