The structure of the Autism Diagnostic Interview-Revised: diagnostic and phenotypic implications

The structure of the Autism DiagnosticInterview-Revised: diagnostic and phenotypic

implications

Anne V. Snow,1 Luc Lecavalier,1 and Carrie Houts21Nisonger Center and Department of Psychology, Ohio State University, USA; 2Department of Psychology,

Ohio State University USA

Background: Multivariate statistics can assist in refining the nosology and diagnosis of pervasivedevelopmental disorders (PDD) and also contribute important information for genetic studies. TheAutism Diagnostic Interview-Revised (ADI-R) is one of the most widely used assessment instrumentsin the field of PDD. The current study investigated its factor structure and convergence with measures ofadaptive, language, and intellectual functioning. Methods: Analyses were conducted on 1,861individuals with PDD between the ages of 4 and 18 years (mean = 8.3, SD = 3.2). ADI-R scores weresubmitted to confirmatory factor analysis (CFA) and exploratory factor analysis (EFA). Analyses wereconducted according to verbal status (n = 1,329 verbal, n = 532 nonverbal) and separately for algorithmitems only and for all items. ADI-R scores were correlated with scores on measures of adaptive, lan-guage, and intellectual functioning. Results: Several factor solutions were examined and compared.CFAs suggested that two- and three-factor solutions were similar, and slightly superior to a one-factorsolution. EFAs and measures of internal consistency provided some support for a two-factor solutionconsisting of social and communication behaviors and restricted and repetitive behaviors. Measuresof functioning were not associated with ADI-R domain scores in nonverbal children, but negativelycorrelated in verbal children. Conclusions: Overall, data suggested that autism symptomatology canbe explained statistically with a two-domain model. It also pointed to different symptoms susceptible tobe helpful in linkage analyses. Implications of a two-factor model are discussed. Keywords: Autisticdisorder, pervasive developmental disorder, assessment, factor analysis, classification.

Autism and other pervasive developmental disorders(PDDs) are characterized by impairments in thethree domains of social interaction, communication,and repetitive and restricted behaviors. A three-domain conceptualization of autism is reflected inthe current diagnostic criteria (American PsychiatricAssociation, 2000; World Health Organization,1992) and in assessment instruments used to detectit (Lord, Rutter, & Le Couteur, 1994; Lord et al.,2000). Although it has been well accepted by thefield, this three-domain model is based primarily onclinical judgment rather than empirical evidence(Szatmari et al., 2006). Empirical studies of thestructure of autism symptoms have been inconclu-sive (Constantino et al., 2004; Tadevosyan-Leyfer etal., 2003; Lecavalier et al., 2006). While someauthors have proposed a single factor to explainautism symptoms (Constantino et al., 2004), othershave proposed two to six (e.g., Lecavalier et al.,2006; Georgiades et al., 2007; Szatmari et al., 2002;Tadevosyan-Leyfer et al., 2003).

Understanding the structure of autism symptomscan move the field forward in two important ways: itcan improve our diagnostic and classification sys-tems and provide valuable information for geneticstudies. According to current diagnostic criteria,autism is diagnosed when an individual exhibits

qualitative impairments in each of the three symp-tom domains. Although the current diagnostic sys-tems accurately identify ‘classic’ autism, it has beensuggested that these criteria are not sufficient forcapturing the variability in the clinical expression ofautism (Tanguay, Robertson, & Derrick, 1998).As such, classification systems have broadened toinclude PDD subtypes, to be used when an individ-ual does not meet criteria in all symptom domains.The current diagnostic criteria for PDDs are largelyregarded as vague and difficult to use reliably(Mahoney, Szatmari, MacLean, Bryson, Bartolucci etal., 1998; Miller & Ozonoff, 2000). It is possible thatthe three-domain conceptualization of autism doesnot precisely describe the disorder, thus contribut-ing to unreliable diagnoses. Studies of the structureof autism symptoms using large heterogeneoussamples of individuals with PDDs can help clarifyautism symptom domains and contribute to amore valid nosology. By empirically examining thestructure of autism symptoms, we can also refinediagnostic practices to more adequately representthe phenotype.

The accurate delineation of clinical phenotypes isone of the most pressing methodological issues inunderstanding pathogenic processes (e.g., Szatmariet al., 2007). A dimensional classification systemmay have advantages over a categorical system inthis regard. A dimensional model could allowConflict of interest statement: No conflicts declared.

Journal of Child Psychology and Psychiatry 50:6 (2009), pp 734–742 doi:10.1111/j.1469-7610.2008.02018.x

� 2008 The AuthorsJournal compilation � 2008 Association for Child and Adolescent Mental Health.Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA

researchers to study individuals quantitatively(above and below arbitrary thresholds) on coredimensions and lead to a better understanding ofthe broader phenotype. In order to clarify therelationship between susceptibility genes and thephenotype, studies have attempted to identify chro-mosomal loci that are associated with specificbehaviors related to autism (Hus, Pickles, Cook, Risi,& Lord, 2007). Some chromosomal regions havebeen shown to be associated with empirically derivedfactors of autism symptoms (Shao et al., 2003).Factor analytic studies can also aid in identifyingwhich behaviors are most associated with thedomains of autism and thereby identify relevantbehaviors for consideration in genetic studies.

To date, many factor analytic studies of autismhave focused on data obtained from the Autism

Diagnostic Interview-Revised (ADI-R; Lord et al.,1994). The ADI-R is a semi-structured interviewdesigned to differentiate individuals with autismfrom those with language impairments and intellec-tual disability (ID). Items on the ADI-R are struc-tured according to the diagnostic criteria defined bythe DSM-IV-TR (APA, 2000) and ICD-10 (WHO,1992). It is an ideal measure for studies of the autismphenotype because it is generally accepted by thefield as the most comprehensive assessment instru-ment available. Additionally, it is administered in aninterview format by a trained rater, which increasesits validity and reliability.

Several researchers have used the ADI-R to ex-plore the autism phenotype. Studies used differentprocedural variations and had different objectives.Only two studies have included the majority of theADI-R items in their analyses. Tadevosyan-Leyfer etal. (2003) performed a principal components analy-sis (PCA) of 98 items in a sample of 292 individuals.They identified six components; Spoken language,Social intent, Compulsions, Developmental mile-stones, Savant skills, and Sensory aversions. TheSocial intent component consisted of social-affectiveitems and nonverbal communication items andcontained the highest number of items.

Constantino et al. (2004) used cluster analysis andPCA on 63 items in a sample of 226 youngsters withand without PDDs. Cluster analyses revealed twolarge clusters: social interaction and communicationitems, and repetitive/stereotypic behaviors andatypical communication items. The PCA resulted in14 components, with one component consisting ofsocial and communication items that explained 40%of the variance.

Three studies have examined the structure ofautism symptoms using the 12 subscale summaryscores from the ADI-R. Van Lang et al. (2006) per-formed a confirmatory factor analysis (CFA) in asample of children and adolescents with a PDD(n = 130) or social/communication problems(n = 125). Three factors were identified: Impairedsocial communication, Impaired make-believe and

play, and Stereotyped language and behavior.Georgiades et al. (2007) performed a PCA in a sampleof 209 individuals with PDDs. Three componentswere extracted: Social-communication, Inflexiblelanguage and behavior, and Repetitive sensory andmotor behavior. CFA supported this model whencompared to the DSM three-factor model. Recently,Frazier, Youngstrom, Kubu, Sinclair, and Rezai(2008) performed a PCA and CFA in a sample of1,170 verbal children and adults from the AutismGenetic Resource Exchange (AGRE) database. Twolarge components were extracted: Social interactionand communication items, and Stereotyped speechand restricted/repetitive behaviors. The CFA sup-ported a two-factor model.

Only one study has examined the factor structureof the ADI-R algorithm items (Lecavalier et al., 2006).The sample included 226 children with PDD andbehavior problems. Exploratory factor analysis (EFA)produced a three-factor solution similar to the ori-ginal algorithm. Unlike the algorithm, however, allnonverbal communication items loaded on the Socialfactor.

Other studies of the ADI-R have focused on oneaspect of autism symptomatology. For instance,Tanguay et al. (1998) aimed to clarify the dimensionsof behavior associated with the social and commu-nication items. Twenty-eight items were submitted toEFA in a sample of 63 children and adolescents.Three factors were identified: Affective reciprocity,Joint attention, and Theory of mind. Analyses werereplicated in a subsample (n = 46) of verbal children.Two studies have used factor analysis to examine thestructure of the restricted and repetitive behaviordomain of the ADI-R. Cuccaro et al. (2003) used PCAto analyze 12 items in a sample of 207 individualswith autism. Two components were extracted:Repetitive Sensory Motor Actions and Resistance toChange. Szatmari et al. (2006) performed a PCA on11 ADI-R items in a sample of 339 individuals withPDDs. Similar to Cuccaro et al. (2003), two compo-nents were extracted: Repetitive Sensory and MotorBehaviors, and Insistence on Sameness. It has beensuggested that these two dimensions may be ofdiagnostic value, as the former appears to be asso-ciated with global developmental delays and thelatter may be specific to autism (Carcani-Rathwell,Rabe-Hasketh, & Santosh, 2006).

In sum, previous factor analytic studies of theADI-R have resulted in solutions that are not entirelyconsistent with the behavioral domains of autism asdefined by the DSM. Studies suggest substantialoverlap of symptoms in the social and communicationdomains and a decomposition of the restricted andrepetitive behavioral domain into two dimensions.

The purpose of the current study was to furtherinvestigate the structure of the ADI-R. It built on thestrengths of existing research that has employed themost detailed measure of autism symptoms, whilealso accounting for the limitations of previous

Structure of the ADI-R 735

� 2008 The AuthorsJournal compilation � 2008 Association for Child and Adolescent Mental Health.

studies by using a large sample, optimal statisticaltechniques, and all of the ADI-R items. CFA andEFAs were conducted on two sets of items: algorithmitems only and all of the items. Algorithm itemsrepresent a set of truncated items and are scoreddifferently (behavior between age 4–5) than the set ofall items (current behavior). These analyses arecomplementary; examining the structure of thealgorithm items is informative for psychometric/diagnostic purposes, and looking at all items speaksmore to the broader phenotype. Analyses were alsoconducted based on verbal ability. Several itemsassess verbal communication and excluding non-verbal subjects would unnecessarily truncate thePDD population. CFA was used to compare differentmodels of autism symptomatology (in CFA, pathspecification are specified for items). EFA was per-formed on randomly selected subsamples to ensurethat meaningful solutions were not overlooked (inEFA, path specifications are not specified and itemsare free to load anywhere, whereas in CFA pathspecification are specified). Such analyses wereimpossible in the past due to small sample sizes.

Important procedural variations exist between thecurrent study and previous studies using the ADI-R.First, most other studies have used PCA, whereas weperformed factor analysis (FA). While both EFA andPCA can be used as exploratory analytic approaches,there are important differences with respect to theinterpretation of results (Widaman, 2008). The factorstructure resulting from EFA can be interpreted asthe association between the observed variables andthe underlying latent variables, whereas the resultsof PCA indicate the optimal weightings of the ob-served variables so as to reduce the dataset into assmall a number of dimensions as possible. Becausethe goal of this study was to identify latent con-structs rather than reduce data, EFA was used.Second, the current study is the only one to use alarge sample of verbal and nonverbal individuals andexamine these subgroups independently. Separatingthe sample in this fashion creates more homoge-neous groups, which results in more precise esti-mates. Finally, we performed two sets of analyses atthe item level. By examining the subscales, implicitassumptions are made about how the individualitems load onto the subscales and each item is as-sumed to be weighted equally. Additionally, thismethod imposes structure onto the ADI-R itemsprior to the analysis of the entire measure. The re-sults of such studies can be interpreted as thestructure of a priori identified subdomains ratherthan the structure of ADI-R items.

Method

Participants

Data included in this study were obtained from apublicly available database provided by the AGRE

program (Geschwind et al., 2001). The selectioncriteria for AGRE require that at least two familymembers have a diagnosis of autism or another PDD.The diagnosis is confirmed using the ADI-R, ADOS,and through a diagnostic assessment performed by apediatric neurologist or developmental pediatrician. AllADI-R assessments are audio taped and raters areperiodically videotaped during in-home visits. Whenthe interview is completed, the audio and videotapesare sent to the AGRE office for data entry and storage.Audio and videotapes are subsequently reviewed on aregular basis by an experienced ADI-R trainer forreliability checks. Information regarding vocabulary,adaptive functioning, and cognitive functioning arealso collected.

Analyses were conducted on 1,861 individuals withPDDs between the ages of 4 and 18 years (mean = 8.32,SD = 3.16). The final sample consisted of 1455 males(78%) and 406 females (22%). Race was not identifiedfor 34.1% of the sample, and of the remaining individ-uals, 94.7% were Caucasian, 2.4% African-American,2.4% Asian-American, and .5% other (e.g., NativeHawaiian/Pacific Islander, more than one race). Sev-enty-one percent of the participants (n = 1329) wereverbal and 29% (n = 532) were nonverbal.

Scores on the PPVT-III were available for 949 indi-viduals (mean = 84.0, SD = 26.9), scores on the VABSwere available for 1039 individuals (mean = 55.0,SD = 21.2), and nonverbal IQ (NVIQ) scores on Raven’sProgressive Matrices were available for 830(mean = 99.4, SD = 19.3). There was no difference inADI-R item, subscale, or total scores between theseindividuals and the rest of the sample.

Eighty-two percent of the informants were mothers,3% were fathers, 13.4% were mothers and fatherscombined, and .3% were other caregivers.

Measures

Autism Diagnostic Interview-Revised. The ADI-Rcontains 93 questions, 34 of which are used in thealgorithm. Most questions are scored as (0) No definitebehavior of the type identified, (1) Behavior of the typespecified probably present but defining criteria not fullymet, or (2) Definite abnormal behavior of the typedescribed in the definition and coding. A score of (3) isused to indicate extreme severity, and scores of (7)Abnormality not of the type associated with autism, and(8) Not applicable can also be used.

Algorithm scores are obtained directly from the items.To ensure that undue weight is not placed on individualitems, severity codes of 3 are treated as 2s on thescoring algorithm. Some pairs of items found undersimilar DSM-IV headings are grouped together, and thehigher score from within that pair is used. Differentcutoff scores in the Communication domain are useddepending on verbal status.

Peabody Picture Vocabulary Test, Third Edition(PPVT-III). The PPVT-III is an individually administeredmeasure of receptive vocabulary for individuals be-tween the ages of 30 months and adulthood. The PPVT-III can also serve as a screening test of verbal ability.Raw scores can be converted to age-referenced stan-dard scores.

736 Anne V. Snow, Luc Lecavalier, and Carrie Houts


Raven’s Progressive Matrices. Raven’s ProgressiveMatrices is a nonverbal measure of general intelligencefor individuals between 6 years and adulthood. Rawscores are converted into standard scores and percen-tile ranks using age-based norms.

Vineland Adaptive Behavior Scales (VABS). TheVABS is a standardized rating instrument that mea-sures four adaptive skill domains: Socialization, Com-munication, Daily Living, and Motor skills. The Motorskills domain is used for children below the age of 6.Raw scores are transformed to obtain domain standardscores and a composite standard score.

Data analysis

Four sets of data were analyzed (algorithm items on theverbal sample, algorithm items on the nonverbal sam-ple, all items on the verbal sample, and all items on thenonverbal sample). With all four data sets, the sameprocedure was followed: CFA was performed on theentire sample. EFA was performed on half of the sam-ple, which was selected randomly. This allowed for amore stable solution, as we avoided the risk of capital-izing on chance when using the same sample for bothanalyses. There were no significant differences betweenthe subsample and the entire sample in terms of theproportion of nonverbal children, the proportionof children with ID, adaptive behavior scores, or thesubscale and total scores of the ADI-R.

Analyses of the algorithm items were performed on 34items for the verbal sample and 28 items for the non-verbal sample. Analyses of all items were performed onitems 31–79 of the ADI-R (n = 48 for verbal sample;n = 38 for nonverbal sample). Items measuring earlydevelopment were not included. Item 65 (Friendships)was not used because it was only scored for partici-pants above the age of 5 years. For algorithm analyses,scores of 3 were transformed to 2, and scores of 7transformed to 0. For the analyses including all of theitems, scores of three were unchanged, so as to retaininformation about the severity of symptoms. Scores formost abnormal 4–5-year age period were used, exceptwhen otherwise noted in the ADI-R scoring directions.When required by the algorithm, the chronological ageof the subject was used to determine which item toscore.

Factor analysis

Because ADI-R items are scored on an ordinal scale,factor analyses were conducted with polychoric corre-lation matrices generated with R version 1.8.1 (2003).EFA was conducted with Comprehensive ExploratoryFactor Analysis (Browne, Cudeck, Tateneni, & Mels,2002). Ordinary Least Squares (OLS) was used as thediscrepancy function and quartimin rotations wereperformed. OLS was selected because it requires fewerassumptions and is mathematically more robust thanmaximum likelihood estimation or generalized leastsquares estimation (MacCallum, Tucker, & Briggs,2001).

CFA was conducted using LISREL (Joreskog & Sor-bom, 1996) and Mplus (Muthen & Muthen, 1998).Diagonally weighted least squares (DWLS) with

polychoric correlations were used (computing Pearson’scorrelations for categorical data can underestimate themagnitude of associations). The Maximum Likelihoodassumptions of continuity and normal distribution arefrequently violated by categorical data. The violation ofthese assumptions can result in factor structures thatare misleading, making any conclusions drawn fromsuch analyses invalid (Kishton & Widaman, 1994).DWLS was used because it provides accurate estimatesof the model parameters for categorical data and hasreduced sample size requirements compared to otherestimation methods (Wirth & Edwards, 2007).

Path specifications were according to the original apriori three-factor solution (i.e., current structure of theADI-R based on the DSM/ICD), a two-factor solutionconsisting of social/communication items andrestricted/repetitive behaviors, and a one-factor solu-tion. These models are called independent clusteringstructures (where each item is associated with only onefactor). We also examined bi-factor structures (wherethere is a ‘general’ factor such as autism and ‘specific’factors such as social or communicative impairments).The independent clustering structures produced thebest fits with the data. Goodness of fit was assessed byexamining the root mean squared error of approxima-tion (RMSEA), the comparative fit index (CFI), thegoodness of fit index (GFI), and the standardized rootmean squared residual (SRMR). Browne and Cudeck(1993) proposed the following guidelines for interpre-tation of the RMSEA: below .05, good fit; between .05and .08, acceptable fit; and above .1, unacceptable fit.The SRMR is the average standardized deviation/residual between the empirical and model-impliedcorrelation matrix. Values less than .10 are generallyconsidered acceptable (Kline, 2005).

Convergence with measures of functioning

Spearman ranked correlation coefficients were calcu-lated between ADI-R scores and total and domainscores of other instruments. Correlations were com-puted using the original social, communication, andrestricted/repetitive behavior domain scores from theADI-R, as well as the empirically-derived social/com-munication domain score. The original and empirically-derived domain scores were calculated by summing thescores of all items that loaded onto each factor. Totalstandardized scores on the PPVT-III, domain and com-posite scores of the VABS, and the Raven’s NVIQ scoreswere transformed into categorical scores for theseanalyses (1 = <24, 2 = 25–39, 3 = 40–54, 4 = 55–70,5 = 71–85, 6 = 86–100, 7 = 101–115, 8 = >115). Such atransformation to ordinal variables increases reliability.

Results

Confirmatory factor analysis

Based on fit indices, the two-factor model of social/communication items and restricted/repetitivebehaviors was quite similar to the three-factor solu-tion and better than the one-factor solution. Modelsbased on a bi-factor structure did not yield better fitindices. Better fits were obtained for the nonverbal



sample. Indices of fit for all models tested are pre-sented in Table 1.

The two-factor solutions for all four data sets arepresented in Table 2. Factor loadings shouldbe interpreted as regression coefficients between thespecific symptom and latent construct. Overall,factor loadings were quite high for the nonverbalsample, with the exception of the items ‘unusualpreoccupations’ and ‘circumscribed interests’. Theseitems did not load strongly onto the restricted/repetitive behavior factor when analyzed with thealgorithm items or all of the items. The items ‘imag-

inative play’, ‘social smiling’, ‘appropriate social

response’ and ‘interest in other children’ had thehighest loadings on the social/communicationfactor. For the restricted/repetitive behavior factor,the items ‘repetitive use of objects’ and ‘unusualsensory interests’ had the highest factor loadings.The same items had high and low loadings on a two-and three-factor solution.

Factor loadings for the verbal sample ranged from.23 to .77, with the exception of the items ‘inappro-priate questions’ and ‘neologisms’ which ranged from.12 to .17. The items ‘nodding’, ‘conventional ges-

tures’, ‘imitative social play’, ‘shared enjoyment’, and‘appropriate social response’ had the highest factorloadings on the social/communication factor. Simi-lar to the nonverbal sample, the items ‘repetitive use

of objects’ and ‘unusual sensory interests’ had thehighest factor loadings on the restricted/repetitivebehavior factor. As with the nonverbal sample, theitems with the highest or lowest factor loadings werethe same between the two and three factor solutions.

Exploratory factor analysis

Factor solutions with one to five factors wereconsidered. The two-factor solution was the mostinterpretable for all four data sets. Specifically, itshowed a pattern of factor loadings that indicated adistinction between social/communication itemsand restricted/repetitive behavior items. Examina-tion of the scree plots supported this choice. Thethree-factor solution was discarded because so few

items loaded on the third factor. The number of itemswith primary loadings on the third factor rangedfrom 1–3 in three datasets. In the fourth dataset (allitems in the nonverbal children), seven items hadprimary loadings on the third factor. These itemswere a combination of social, communication, andrestricted/repetitive behavior domain and were dif-ficult to interpret. The four- and five-factor solutionswere also discarded due to too few items on theadditional factors. All EFA solutions are availablefrom the authors upon request.

Internal consistencies of the domain and totalADI-R scores were calculated for all four data sets.Table 3 summarizes these results. In all analyses,the internal consistency for the social/communica-tion domain was higher or equal to that for the all ofthe items. The internal consistency for the RepetitiveBehavior domain was the lowest.

Convergence with measures of functioning

Correlation coefficients between ADI-R domain andtotal scores and the scores from the VABS, PPVT-III,and Raven’s Progressive Matrices are presented inTable 4. For the verbal sample, the communicationand the social/communication ADI-R domain scoreshad the highest associations with VABS total anddomain scores, and the restricted/repetitive behav-ior domain score had the lowest. In the nonverbalsample, there were fewer significant associationsbetween the ADI-R scores and the VABS total anddomain scores.

The PPVT-III and Raven’s NVIQ scores wereexamined only for the verbal sample. The totalPPVT-III and NVIQ scores were most strongly nega-tively correlated with the communication and social/communication domains of the ADI-R.

Discussion

Taken together, results from the analyses indicatedthat the autism phenotype can be explainedstatistically with a two-factor model consisting of

Table 1 Fit indices for CFA models

One-factoralgorithm All

Two-factoralgorithm All

Three-factor(DSM) algorithm All

NonverbalRMSEA .028 .059 .026 .053 .026 .05695% CI (.022 – .034) (.056 – .062) (.020 – .032) (.050 – .057) (.020 – .032) (.051 – .061)

SRMR .090 .088 .089 .084 .088 .097CFI .993 .943 .994 .954 .994 .955GFI .968 .927 .969 .936 .970 .924

VerbalRMSEA .070 .075 .068 .065 .069 .06295% CI (.068 – .070) (.074 – .077) (.066 – .071) (.064 – .067) (.066 – .072) (.060 – .064)

SRMR .081 .084 .079 .077 .085 .079CFI .966 .940 .968 .955 .968 .959GFI .962 .948 .963 .954 .962 .952



social/communication symptoms and restricted/repetitive behaviors. Results of the CFAs indicatedthat the fit indices for the two- and three-factorsolutions were similar and better than the one-factormodel. Because the models are not nested, there isno statistical test to determine if one model is ‘better’than the other. We tend to privilege the two-factorsolution for several reasons. First, within the currentdataset, when path loadings were not constrained in

the EFA, items tended to cluster together naturally ina two-factor solution. Although the pattern of corre-lations with other measures did not supportunequivocally one solution over another, measuresof internal consistency suggested a separate factorfor repetitive behaviors. The two-factor model is moreparsimonious and consistent with previousresearch. The overlap between social and commu-nication items has been reported in the literature,

Table 2 CFA Factor loadings

ADI-R itema

Nonverbalb Verbalc

Algorithm All Items Algorithm All Items

Factor I Factor II Factor I Factor II Factor I Factor II Factor I Factor II

31. Use of other’s body .32 .28 .42 .4232. Articulation/pronunciation .2333. Stereotyped utterances .51 .4834. Social chat .57 .6135. Reciprocal conversation .61 .6036. Inappropriate questions .11 .1237. Pronomial reversal .28 .2738. Neologisms .11 .1739. Verbal rituals .29 .3140. Speech abnormalities .4141. Communicative speech .4842. Pointing to express interest .49 .53 .63 .7343. Nodding .42 .48 .78 .7444. Head shaking .41 .43 .75 .7245. Conventional gestures .73 .62 .80 .7446. Attention to voice .55 .6247. Imitation of actions .59 .61 .64 .6048. Imaginative play .76 .73 .76 .7049. Imitative social play .72 .63 .80 .7350. Direct gaze .62 .55 .66 .6351. Social smiling .72 .68 .69 .6852. Directing attention .67 .46 .79 .7253. Offering to share .52 .51 .71 .6754. Shared enjoyment .60 .60 .77 .7755. Offering comfort .68 .65 .69 .6856. Quality of social overtures .67 .65 .75 .7257. Range of facial expressions .69 .64 .73 .7158. Facial expressions .40 .35 .50 .5059. Appropriate social response .69 .67 .79 .7560. Initiation of activities .55 .5961. Imitative social play .61 .6762. Interest in children .68 .67 .73 .7163. Response to approaches .70 .63 .75 .7264. Group play with peers .49 .51 .63 .7766. Social disinhibition .54 .5867. Unusual preoccupations ).15 ).05 .26 .3568. Circumscribed interests ).04 .03 .26 .3269. Repetitive use of objects .78 .7670. Compulsions/rituals .15 .25 .27 .3871. Unusual sensory interests .75 .6272. Sensitivity to noise .27 .4373. Response to sensory stimuli .39 .4174. Adherence to routine .17 .4775. Resistance to change .20 .4576. Attachment to objects .31 .4277. Hand and finger mannerisms .47 .5978. Stereotypies .25 .5279. Midline hand movements ).01 .3669/71 Highest score .85 .7777/78 Highest score .51 .65

aSummary of item phrasing only; bn = 532; cn = 1329.



regardless of the number of other factors identified(Frazier et al., 2008; Georgiades et al., 2007;Gotham, Risi, Pickles, & Lord, 2007; Lecavalier etal., 2006; Tanguay et al., 1998; Tadevosyan-Leyfer etal., 2003; van Lang et al., 2006).

Unlike previous studies (Cuccaro et al., 2003;Szatmari et al., 2006), the repetitive behavior factordid not emerge as two dimensions (repetitive sensorymotor and insistence on sameness) when the currentdata were submitted to EFA. Of importance is thefact that current analyses used all items, not asubgroup. Our objective was to examine items fromall domains while others used specific items orsubscales. Of course, the item pool will impact thefinal solution and it is not advisable statistically tolook at every possible model. One might guess that ifthis pattern were evident in the current sample,items assessing sensory motor behaviors and insis-tence on sameness would have shown differentpatterns of factor loadings across the two samples.As evident in Table 2, this was not the case.

Although the current data does not provide anunequivocal case for a two-factor model of autismsymptoms, the similarity between the two-factormodel and the widely accepted three-factor modelwarrants discussion. Refining the structure of theautistic phenotype has implications for the nosol-ogy/diagnostic practices and can provide valuableinformation for genetic research.

Nosological and diagnostic implications includepossible revisions to our diagnostic systems basedon the combination of social and communicationsymptoms into one domain of impairment. Combin-ing the social and communication symptoms intoone domain may better capture the core impairmentof PDDs and result in a more accurate classificationsystem. An additional diagnostic implicationincludes the need for a better understanding andmeasurement of the restricted/repetitive behaviorsdimension of autism. In the current study, analysesof the algorithm items showed that only five of the 34items load on the restricted/repetitive behavior fac-tor. This suggests that these symptoms are less wellrepresented in the ADI-R. Including additional itemsthat assess repetitive behaviors may help to bettercapture this domain of autism. Additionally, theitems ‘repetitive use of objects’ and ‘unusual sensoryinterests’ had the highest factor loadings on therestricted/repetitive behavior factor. In the ADI-Ralgorithm, only the highest score of these two itemsis used. It may be advisable to use both scores, asthey show a strong association with the restricted/repetitive behavior dimension. Factor loadings arevariance shared between the item and the fac-tor. Because they are particular to each item, theycan be interpreted as independent contributionsof variance to the factor (Comrey & Lee, 1992).Recent research suggests that symptom dimensionsof social/communication and restricted/repetitivebehaviors have different developmental trajectories(Lord et al., 2006) and may share largely indepen-dent causes (Mandy & Skuse, 2008).

The current results also have implications forgenetic studies. Studies of the autism phenotypemay benefit by adopting a dimensional approach inwhich the core symptoms are measured quantita-tively. This would allow for researchers to studyindividuals who are above and below thresholds ofimpairment on core dimensions, rather than study-ing people based on categories.

If the social communication and restricted/repeti-tive behaviors are the core impairments involved inautism, it would follow that the symptoms moststrongly associated with these domains are coresymptoms that might be useful in genetic linkagestudies. Core symptoms can be identified by exam-ining the pattern of factor loadings, which areregression coefficients describing the associationbetween items and latent constructs. The items thatwere most strongly associated with the social/com-munication domain in the nonverbal sample are

Table 3 Internal consistency of domain and total ADI-R scores

Algorithm items All items

NonverbalSocial .78 .84Communication .57 .66Social /Communication .82 .88RRB .26 .61Total .77 .84

VerbalSocial .90 .91Communication .78 .83Social/Communication .92 .93RRB .54 .74Total .91 .93

RRB = restricted and repetitive behavior.

Table 4 Spearman ranked correlation coefficients betweenADI-R domain and total scores

Social CommSocial/Comm RRB Total

NonverbalVinelandSociala ).12* ).20** ).15* .04 ).10Communicationa ).08 ).17** ).11 ).04 ).10Daily Livinga ).00 ).03 ).02 ).08 ).04Motor Skillsb ).17** ).25** ).22** ).11 ).21**Compositea ).04 ).08 ).05 ).02 ).04

VerbalVinelandSocialc ).46** ).43** ).47** ).34** ).48**Communicationc ).39** ).41** ).42** ).26** ).41**Daily Livingc ).31** ).35** ).35** ).28** ).35**Motor Skillsd ).14** ).27** ).20** ).24** ).22**Compositec ).40** ).41** ).42** ).29** ).42**

PPVT-IIIe ).27** ).37** ).33** ).12** ).30**Raven’s NVIQf ).14** ).15** ).15** ).07* ).15**

an = 280; bn = 261; cn = 759; dn = 688; en = 863; fn = 745;*p < .01; **p < .001.RRB = restricted and repetitive behavior.



among those that have been recognized as the earliestsymptoms of autism (e.g., Chawarska & Volkmar,2005). These behaviors may be useful in geneticlinkage studies of younger individuals. In the verbalsample, the items with the highest factor loadingswere more sophisticated measures of social commu-nication. These results highlight the difference inautism symptoms with respect to functioning leveland the importance of considering verbal ability instudies of autism. In both samples, the items with thehighest factor loadings were social and communica-tion items. This emphasizes the conceptualization ofthe social communication domain as a core impair-ment in autism and distinct from the restricted/repetitive behavior domain. The finding that the sameitems had the highest factor loadings in the two- andthree-factor solutions provides additional evidencefor the existence of core symptoms that are stronglyassociated with the latent factors that make up thesymptomalogical dimensions of autism.

The use of the AGRE database constitutes astrength and a limitation of the current study. Thelarge sample size made it possible to run comple-mentary EFA and CFA analyses of both the algorithmitems and all of the ADI-R items. These analyseshelped to clarify the structure of one of the mostwidely used assessment instruments in the field aswell as refine our understanding of the autism phe-notype. An important limitation of the AGRE data-base is that it includes only families in which morethan one child has been diagnosed with a PDD. Thismay reduce the generalizability of results; however,two studies examining autistic symptoms in indi-viduals from multiplex and singleton families foundno differences between these two groups on the do-main scores of the ADI-R (Cuccaro et al., 2003;Georgiades et al., 2007). Future studies of childrenwho do not have an affected sibling are necessary toconfirm the current results.

Recent advances in multivariate statistics andaccess to large clinical samples represent a uniqueopportunity to contribute meaningfully to researchon the nosology and genetics of PDDs. Progress willbe made with programmatic research and multipleresearch strategies. None of these techniques will befully satisfactory when used alone. The currentstudy adopted one such strategy (multivariate sta-tistics) and represents but a small step in what willbe a very protracted process. Other statistical tech-niques such as cluster analysis, where the individual

is the unit of analysis, might also provide importantinformation and allow for the identification ofhomogeneous subgroups.

Acknowledgements

The authors wish to thank AGRE for the generoususe of their database, and the families who partici-pated in the AGRE program.

Correspondence to

Luc Lecavalier, Ohio State University, NisongerCenter, 305 McCampbell Hall, 1581 Dodd Drive,Columbus, Ohio, USA, 43210-1257; Tel: 614.292.2378; Fax: 614.688.5522; Email: [email protected]

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Manuscript accepted 13 August 2008



Documents

The structure of the Autism Diagnostic Interview-Revised: diagnostic and phenotypic implications