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External Validation of Bifactor Model of ADHD: Explaining Heterogeneity in Psychiatric Comorbidity, Cognitive Control, and Personality Trait Profiles Within DSM-IV ADHD Michelle M. Martel & Bethan Roberts & Monica Gremillion & Alexander von Eye & Joel T. Nigg Published online: 7 July 2011 # Springer Science+Business Media, LLC 2011 Abstract The current paper provides external validation of the bifactor model of ADHD by examining associations between ADHD latent factor/profile scores and external validation indices. 548 children (321 boys; 302 with ADHD), 6 to 18 years old, recruited from the community participated in a comprehensive diagnostic procedure. Mothers completed the Child Behavior Checklist, Early Adolescent Temperament Questionnaire, and California Q- Sort. Children completed the Stop and Trail-Making Task. Specific inattention was associated with depression/with- drawal, slower cognitive task performance, introversion, agreeableness, and high reactive control; specific hyperactivity-impulsivity was associated with rule- breaking/aggressive behavior, social problems, errors dur- ing set-shifting, extraversion, disagreeableness, and low reactive control. It is concluded that the bifactor model provides better explanation of heterogeneity within ADHD than DSM-IV ADHD symptom counts or subtypes. Keywords ADHD . Bifactor . Cognitive control . Personality . Problem behavior Attention-Deficit/Hyperactivity Disorder (ADHD) is a prevalent childhood behavioral disorder characterized by three continuous symptom dimensions of inattention, hyperactivity, and impulsivity (APA 2000). The substantial heterogeneity of symptom presentation in individuals diagnosed with the disorder led to the development of three subtypes in DSM-IV (APA 2000): Predominantly inattentive (ADHD-PI), predominantly hyperactive- impulsive (ADHD-PHI), and combined (ADHD-C). Al- though the validity of these subtypes has been called into question (e.g., Lahey et al. 2005), an alternative approach to parsing clinical heterogeneity has not been established. One promising approach is factorial, called the bifactor model. A bifactor model of ADHD features a general or gfactor of ADHD with two specific sfactors of inattention and hyperactivity-impulsivity that capture unique variance. This structural model has been tested in clinical and community- recruited samples of children with ADHD via comparison with other factor models (Martel et al. 2010b; Toplak et al. 2009). Now that the bifactor structure has been replicated by different investigators and samples, evaluation of its external validity via external correlates of ADHD is essential; this was the goal of the present investigation. In the bifactor model of ADHD, a general, or g,factor of the disorder can exist simultaneously with two (or more) specific, or s,factors, in this case, inattention and hyperactivity-impulsivity (as shown in Fig. 1). The model can capture interindividual heterogeneity in the symptom presentation of ADHD by suggesting that individuals can manifest with general and/or specific liabilities while carrying the same latent disorder. While individuals with a general liability would highly express both inattentive and hyperactive-impulsive symptoms of ADHD, individuals with a specific liability would present with relatively high inattentive or hyperactive-impulsive symptoms, yet would M. M. Martel (*) : B. Roberts : M. Gremillion Psychology Department, University of New Orleans, 2005 Geology & Psychology Building, 2000 Lakeshore Drive, New Orleans, LA 70148, USA e-mail: [email protected] A. von Eye Psychology Department, Michigan State University, East Lansing, USA J. T. Nigg Psychiatry Department, Oregon Health and Science University, Oregon, USA J Abnorm Child Psychol (2011) 39:11111123 DOI 10.1007/s10802-011-9538-y

External Validation of Bifactor Model of ADHD: Explaining Heterogeneity in Psychiatric Comorbidity, Cognitive Control, and Personality Trait Profiles Within DSM-IV ADHD

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Page 1: External Validation of Bifactor Model of ADHD: Explaining Heterogeneity in Psychiatric Comorbidity, Cognitive Control, and Personality Trait Profiles Within DSM-IV ADHD

External Validation of Bifactor Model of ADHD: ExplainingHeterogeneity in Psychiatric Comorbidity, Cognitive Control,and Personality Trait Profiles Within DSM-IV ADHD

Michelle M. Martel & Bethan Roberts &

Monica Gremillion & Alexander von Eye & Joel T. Nigg

Published online: 7 July 2011# Springer Science+Business Media, LLC 2011

Abstract The current paper provides external validation ofthe bifactor model of ADHD by examining associationsbetween ADHD latent factor/profile scores and externalvalidation indices. 548 children (321 boys; 302 withADHD), 6 to 18 years old, recruited from the communityparticipated in a comprehensive diagnostic procedure.Mothers completed the Child Behavior Checklist, EarlyAdolescent Temperament Questionnaire, and California Q-Sort. Children completed the Stop and Trail-Making Task.Specific inattention was associated with depression/with-drawal, slower cognitive task performance, introversion,agreeableness, and high reactive control; specifichyperactivity-impulsivity was associated with rule-breaking/aggressive behavior, social problems, errors dur-ing set-shifting, extraversion, disagreeableness, and lowreactive control. It is concluded that the bifactor modelprovides better explanation of heterogeneity within ADHDthan DSM-IV ADHD symptom counts or subtypes.

Keywords ADHD . Bifactor . Cognitive control .

Personality . Problem behavior

Attention-Deficit/Hyperactivity Disorder (ADHD) is aprevalent childhood behavioral disorder characterized bythree continuous symptom dimensions of inattention,hyperactivity, and impulsivity (APA 2000). The substantialheterogeneity of symptom presentation in individualsdiagnosed with the disorder led to the development ofthree subtypes in DSM-IV (APA 2000): Predominantlyinattentive (ADHD-PI), predominantly hyperactive-impulsive (ADHD-PHI), and combined (ADHD-C). Al-though the validity of these subtypes has been called intoquestion (e.g., Lahey et al. 2005), an alternative approach toparsing clinical heterogeneity has not been established. Onepromising approach is factorial, called the bifactor model.A bifactor model of ADHD features a general or “g” factorof ADHD with two specific “s” factors of inattention andhyperactivity-impulsivity that capture unique variance. Thisstructural model has been tested in clinical and community-recruited samples of children with ADHD via comparisonwith other factor models (Martel et al. 2010b; Toplak et al.2009). Now that the bifactor structure has been replicatedby different investigators and samples, evaluation of itsexternal validity via external correlates of ADHD isessential; this was the goal of the present investigation.

In the bifactor model of ADHD, a general, or “g,” factorof the disorder can exist simultaneously with two (or more)specific, or “s,” factors, in this case, inattention andhyperactivity-impulsivity (as shown in Fig. 1). The modelcan capture interindividual heterogeneity in the symptompresentation of ADHD by suggesting that individuals canmanifest with general and/or specific liabilities whilecarrying the same latent disorder. While individuals with ageneral liability would highly express both inattentive andhyperactive-impulsive symptoms of ADHD, individualswith a specific liability would present with relatively highinattentive or hyperactive-impulsive symptoms, yet would

M. M. Martel (*) :B. Roberts :M. GremillionPsychology Department, University of New Orleans,2005 Geology & Psychology Building, 2000 Lakeshore Drive,New Orleans, LA 70148, USAe-mail: [email protected]

A. von EyePsychology Department, Michigan State University,East Lansing, USA

J. T. NiggPsychiatry Department, Oregon Health and Science University,Oregon, USA

J Abnorm Child Psychol (2011) 39:1111–1123DOI 10.1007/s10802-011-9538-y

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not have a separate disorder. A bifactor model of ADHDcan provide similar information as the current DSM-IVconcept of ADHD through (1) continuous measures ofbehavioral symptom domains, through the “g” ADHD and“s” inattention and hyperactivity-impulsivity latent factorscores, and (2) categorical subtypes, through latent profileanalysis of the latent factor scores. Yet, a bifactor modelmay allow for more accurate quantification of symptomcounts and better classification of individuals into categor-ical subgroups due to the fact that the model is moreflexible and allows for measurement free from error. Thus,a bifactor model of ADHD may allow for a moremeaningful and empirically sound parsing of clinicalheterogeneity than the current DSM-IV conceptualization,allowing for a nuanced examination of external validity.

Children with DSM-IV ADHD demonstrate heterogene-ity in the key external validation indices of psychiatriccomorbidity, cognition or neuropsychology, and personality

traits (Cantwell 1992), and these indices have arguablyreceived the most attention and most extensive validation inempirical work (e.g., Nigg 2006), suggesting their impor-tance in a complete understanding of the disorder. Withregard to psychiatric comorbidity, ADHD is highly comor-bid with Oppositional-Defiant Disorder (ODD), ConductDisorder (CD), and anxiety and mood disorders (Angold etal. 1999; Jensen et al. 1997). Consistent DSM-IV subtypedifferences have been noted in the profile of comorbidconditions. Other disruptive behavior disorders like ODDand CD are more often seen in individuals with ADHD-Cthan in individuals with ADHD-PI (Milich et al. 2001). Incontrast, comorbid anxiety and mood disorders are moreoften seen in individuals with ADHD-PI, as compared toindividuals with ADHD-C and ADHD-PHI (Barkley 2006).Thus, anxiety and mood problems in ADHD may be morerelated to specific inattention, while comorbid disruptivebehavior problems may be more related to specific

ADHD

Close Attention

Sustained Attention

Listens

Follow Through

Organization

Sustained Mental Effort

Loses Things

Easily Distracted

Forgetful

Fidgets

Leaves Seat

Runs, Climbs

Plays Quietly

“Driven by Motor”

Talks A Lot

Blurts

Waiting Turn

Interrupts, Intrudes

Inatt

Hyper

.13

-.48

-.15

-.47

-.08

-.61

.12

-.62

.07

1

.87

-.09

1.01

-.15

.89

.23

.91

1.59-.21

.76

1.6

1.18

1.5

1.56

.78

.88

1.63

.43

1.02

.46

.83

.59

.99

1

.86

.59

-.48

-.86

Fig. 1 ADHD Bifactor Model. Note. All path coefficients are standardized. Nonsignificant factors loadings are italicized and underlined. SeeMartel et al. (2010b)

1112 J Abnorm Child Psychol (2011) 39:1111–1123

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hyperactivity-impulsivity and general ADHD (Lahey andWillcutt 2010).

Weakness in neuropsychological cognitive control alsoplagues many individuals with ADHD and has beenexplored as an endophenotype of the disorder (Doyle etal. 2005). Deficits in response inhibition, working memory,set-shifting, planning, and vigilance have been mostconsistently associated with ADHD (Nigg 2006; Willcuttet al. 2005). However, problems with motivation and timingare also found in some children with ADHD. Althoughmany children with ADHD exhibit problems with cognitivecontrol, some children with ADHD have intact cognitivecontrol and some children without ADHD exhibit problemsin this domain (Willcutt et al. 2005). Pathway models ofADHD suggest that most aspects of cognitive control aremore specifically related to the expression of the inattentivesymptoms of the disorder, while motivation, speed, and timingare more related to the hyperactive-impulsive symptoms ofthe disorder (Sonuga-Barke 2005). Further, a hypothesizedgroup of children with only inattention and few or nohyperactive-impulsive symptoms may be characterized byslower performance; this group is commonly referred to assluggish cognitive tempo and/or ADHD-Restrictive Inatten-tive presentation as proposed in draft DSM-5 criteria (dsm5.org; Carr et al. 2010; Goth-Owens et al. 2010; McBurnett etal. 2001). Thus, cognitive control and slow cognitiveperformance may exhibit associations with specific inatten-tion, while an impulsive response pattern may show strongerassociations with specific hyperactivity-impulsivity.

ADHD has been associated with a characteristic person-ality trait profile. This trait profile is typically characterizedby low effortful control and conscientiousness, highnegative emotionality and neuroticism, low agreeableness,and—in some cases—high extraversion (Nigg et al. 2002).When more specific relations between traits and ADHDsymptoms have been examined, effortful control andconscientiousness appear to be somewhat differentiallyrelated to inattentive symptoms of ADHD, while reactiveforms of control and negative emotionality or neuroticismare differentially related to hyperactivity-impulsivity andoppositional-defiance (Martel and Nigg 2006). Other workalso suggests a relationship between high negative emo-tionality, low agreeableness, and hyperactivity-impulsivityand ODD in school-aged children (Frick et al. 2003; Parkeret al. 2004). Thus, effortful control and conscientiousnessmay be differentially related to specific inattention, whereasmore affective forms of control like reactive control,negative emotionality/neuroticism, and low agreeablenessmay be more related to specific hyperactivity-impulsivity orgeneral ADHD.

The current paper takes a critical approach to externallyvalidating the bifactor structure of ADHD by utilizing acontinuous symptom and categorical subtype approach,

similar to DSM-IV. In the first approach, we examined theassociations of specific inattention, specific hyperactivity,and general ADHD latent factor scores from the ADHDbifactor model with associated child problem behaviors,cognitive control, and personality traits. Hypotheses underthis approach were that 1) specific inattention would berelated to anxiety/mood symptoms, cognitive control,slower cognitive performance, and effortful control/consci-entiousness traits; 2) specific hyperactivity-impulsivitywould be related to ODD/CD problems, an impulsiveresponse pattern on cognitive tasks, and reactive control,negative emotionality/neuroticism traits, and low agree-ableness; and 3) general ADHD symptoms would showmore uniform relations with child problem behaviors,cognitive control, and personality traits with the 4) specificlatent factor scores exhibiting a more differentiated profileof association with the external validators, providing abetter description of ADHD heterogeneity than the currentDSM-IV symptom counts.

In the second approach, we adopted a complementaryperson-centered statistical approach using the bifactor latentfactor scores (Bergman and Magnusson 1997; von Eye andBergman 2003). The hypotheses in this approach were that 1)at least three subgroups, or “subtypes,” within ADHD wouldbe formed: a general ADHD group, a specific hyperactivity-impulsivity group, and a specific inattention group and 2) thespecific hyperactivity-impulsivity and specific inattentiongroups would exhibit distinct profiles in relation to childproblem behaviors, cognitive control, and personality traitswith more differentiated associations than DSM-IV subtypes.

Method

Participants

Overview Participants were 548 children (321 boys), 6 to18 years old, recruited from the community and thenevaluated for study eligibility and diagnostic status.Children were initially included in one of two groups:ADHD (n=302) and non-ADHD comparison youth (“con-trols,” n=199). Forty-seven additional children who wereclassified as having situational or sub-threshold ADHDwere included to provide more complete coverage of thedimensional trait space of ADHD (Sherman et al. 1997). TheADHD group included 110 children with ADHD-PI (i.e.,met criteria for six or more inattentive symptoms, plusimpairment, onset, and duration, and never in the past metcriteria for combined type) and 192 children with ADHD-C(i.e., met criteria for six or more inattentive symptoms andsix or more hyperactive-impulsive symptoms, plus impair-ment, onset, and duration). The current sample included nochildren with the hyperactive-impulsive ADHD subtype,

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fairly typical in this age range (e.g., Lahey et al. 2005). Asshown in Table 1, 161 children met DSM-IV criteria forODD, and 19 were diagnosed with CD. Forty one metcriteria for Major Depressive Disorder (lifetime), 13 forDysthymia (lifetime), and 45 for Generalized AnxietyDisorder (lifetime). Children came from 468 families; 80families had two children in the study. All familiescompleted informed consent.

Recruitment and Identification A broad community-basedrecruitment strategy was used, with mass mailings toparents in local school districts, public advertisements, aswell as flyers at local clinics. Families who volunteeredthen passed through a standard multi-gate screening processto identify cases and non-cases eligible for the study. AtStage 1, all families were screened by phone to rule outyouth prescribed long-acting psychotropic medication (e.g.antidepressants), neurological impairments, seizure history,head injury with loss of consciousness, other major medicalconditions, or a prior diagnosis of mental retardation orautistic disorder, as reported by parent.

At Stage 2, parents and teachers of remaining eligibleyouth completed the following standardized rating scales:Child Behavior Checklist/Teacher Report Form (CBCL/TRF; Achenbach 1991) and the ADHD Rating Scale(ADHD-RS; DuPaul et al. 1998). In addition, parentscompleted a structured clinical interview to ascertainsymptom presence, duration, and impairment. Parents andteachers were instructed to rate children’s behavior whennot taking psychostimulant medication.

The diagnostic interview used was dependent on the yearof data collection. For participants who participatedbetween 1997 and 2001 (N=218), the Diagnostic Interview

Schedule for Children (DISC-IV; Shaffer et al. 2000) wascompleted with the parent by telephone or during on-campus visits. A trained graduate student or advancedundergraduate with at least 10 h of training administeredthe DISC-IV. Fidelity to interview procedure was checkedby having the interview recorded with 5% reviewed by acertified trainer. For children who were administered theDISC-IV and met duration, onset (age 7), and impairmentcriteria for DSM-IV ADHD, a modified “or” algorithmwas used to establish the diagnostic group and to createthe symptom count. Teacher-reported symptoms on theADHD-RS (i.e., items rated as a “2” or “3” on the 0 to 3scale) could be added to the parent-endorsed symptomtotal, up to a maximum of two additional symptoms, toget the total number of symptoms. This approach wasadopted to maximize similarity to the methods usedwhen establishing the DSM-IV cut points as valid (Laheyet al. 1994) while avoiding the over-identification thatmight occur from an unrestricted “or” count. Childrenfailing to meet cut-offs for all parent and teacher ADHDrating scales at the 80th percentile and having four orfewer symptoms of ADHD with the “or” algorithm wereconsidered controls. However, those in the undefinedrange were retained in the sample for analyses of the fullrange of symptoms.

For children who participated from 2002 to 2008, youthand their primary caregiver completed the Kiddie Schedulefor Affective Disorders and Schizophrenia (KSADS-E;Puig-Antich and Ryan 1986). The data from the interviewsand parent and teacher rating scales were then presented toa clinical diagnostic team consisting of a board certifiedchild psychiatrist and licensed clinical child psychologist toimplement a best-estimate diagnostic procedure. They were

ADHD Control Totaln=302 n=199 N=5481

Boys n(%) 204(67.5) 96(48.2) 321(58.6)**

Ethnic Minority n(%) 78(25.8) 54(27.1) 144(26.3)

Age 11.32(2.93) 12.5(3.24) 11.67(3.06)**

IQ 110.33(14.92) 103.78(13.90) 106.2(14.7)**

Family Income 62643(67080) 75244(51109) 66694(59532)*

ADHD-C n(%) 192(63.6) – 192 (35)**

ADHD-PI n(%) 110(36.4) – 110(20.1)**

ODD n(%) 118(39.1) 26(13.1) 161(29.4)**

CD n(%) 18(6) 1(.5) 19(3.5)**

Inattentive Sx (P + T) 6.02(2.34) 0.41(1.04) 3.77(3.32)**

Hyperactive Sx (P + T) 6.35(2.38) 0.53(1.22) 4.03(3.44)**

Inattentive Sx (P) 18.03(5.03) 3.23(3.4) 11.93(8.41)**

Hyperactive Sx (P) 11.97(7.23) 2.04(2.21) 7.9(7.48)**

Inattentive Sx (T) 14.89(6.89) 2.36(3.18) 9.68(8.27)**

Hyperactive Sx (T) 9.85(7.94) 1.35(2.36) 6.37(7.49)**

Table 1 Descriptive statistics ofsample

*p<0.05. **p<0.01, via t-testsor chi-squares. 1 Forty-sevenchildren were identified as hav-ing situational ADHD or werescreened out of the study at alater point in time, but wereincluded in study analyses be-cause they had data on traits andcomorbid psychopathology.ADHD-C=ADHD, combinedsubtype. ADHD-PI = ADHD,predominantly inattentive sub-type. ODD = Oppositional-Defiant Disorder. CD = ConductDisorder. (P + T) = Parent +teacher rated symptoms using 0–1 rating system. (P) = Parent-rated symptoms using 0–3 ratingscale on ADHD-RS. (T) =Teacher-rated symptoms using0–3 rating scale on ADHD-RS

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allowed to use the same “or” algorithm described above intheir diagnostic decision making. Their agreement rateswere acceptable for ADHD diagnosis, subtypes, and currentODD and CD (all kappas ≥0.89).

Pooling the data across families that received theKSADS and the DISC was justified based on our analysisof agreement between the two methods in 430 youth forwhom a parent completed both a KSADS and a DISC-IV.The two interviews had adequate agreement for totalnumber of symptoms (inattention, ICC=0.88; hyperactivity,ICC =0.86), presence of six or more symptoms of ADHD(kappa =0.79), presence of impairment (kappa =0.64), andpresence of ADHD (defined as six or more symptoms +cross situational impairment in each interview for purposesof computing agreement; kappa =0.79).

Measures

Symptom Counts Maternal report on ADHD symptoms wasavailable via report on diagnostic interview, and maternal andteacher report on ADHD symptoms was available via reporton the ADHD-RS. Confirmatory factor analyses of thebifactor ADHD model utilized parent and teacher reportsymptom counts, using parent report on diagnostic interviewand teacher report on the ADHD-RS, integrated using the “or”algorithm (as discussed above and in Lahey et al. 1994).

Child Problem Behaviors Child problems behaviors, in-cluding anxiety/depression, withdrawal/depression, so-matic complaints, social problems, rule-breakingbehavior, and aggressive behaviors, were assessed usingmaternal report on the Child Behavior Checklist (CBCL;Achenbach 1991). The CBCL is a well-validated instru-ment that provides valid and reliable information aboutdimensional and empirically-based symptoms of psycho-pathology (Achenbach et al. 2003).

Cognitive Control Children completed all cognitive controltesting after a 24- to 48-hour medication washout, depend-ing on nature of the preparation, to equal at least 5 halflives. In order to measure response inhibition and responsevariability, the tracking version of the Stop Task wasadministered. The tracking version of the Stop Task, awell-validated computer task with alpha reliability higherthan 0.8 (Band et al. 2003; Logan 1994), measures theability to effortfully inhibit an automatic key press followinga tone. A measure of inhibitory control, stop signal reactiontime, was computed by subtracting mean stop signal latencyfrom mean go response time. A response variability measurewas obtained, defined as the within-child variability of thereaction time on the “go” trials (Russell et al. 2006). In orderto measure speed and set-shifting, the Trailmaking Task, asubtest of the Halstead-Reitan Neuropsychological Battery,

was administered during which the child is required torapidly draw a line between alternating numbers andletters in ascending order. Widely used in neuropsycho-logical research, the outcome measure of the task was set-shifting, measured as time to complete and number oferrors made during the more difficult Trails B Task.(Reitan and Wolfson 1985; Spreen and Strauss 1991); timeto complete Trails A served as a measure of speed.

Temperament and Personality Traits Parents completed thecommon language version of the California Q-Sort (Caspiet al. 1992) and a short form of the Early AdolescentTemperament Questionnaire (EATQ; Capaldi and Rothbart1992). The CCQ is a typical Q-Sort consisting of 100 cardswhich must be placed in a forced-choice, nine-category,rectangular distribution. The rater (in this case, the mother)describes the child by placing descriptive cards in one ofthe categories, ranging from one (least descriptive) to nine(most descriptive). To measure the Big Five Factors, scalesdeveloped by John et al. (1994) were used. To measurereactive control, resiliency, and negative emotionality,scales developed by Eisenberg et al. (2003; personalcommunication, 2006) were used (e.g., reactive control,“is shy and reserved;” resiliency, “is resourceful in initiatingactivities;” negative emotionality, “cries easily”). A com-posite score was generated by reverse-scoring selected itemsand computing the average. Scale reliabilities were all above0.70 with the exception of the scale for openness (α=0.56). Toprovide a measure of effortful control, mothers completed ashort form of the Early Adolescent Temperament Question-naire (EATQ; Capaldi and Rothbart 1992). The EATQ ParentReport-Revised consists of 62 items describing childtemperament characteristics. The questionnaire instructionsconsist of asking the parent to rate how well the statementsdescribe her child on a scale from 1 (almost always untrue ofthe child) to 5 (almost always true of the child), yielding 10scales. In the current study, the effortful control scale from theEATQ was utilized (Eisenberg et al. 1996). Alpha reliabilitywas acceptable (0.84).

Data Analysis

Confirmatory factor analysis for the bifactor ADHD modelwas conducted on mother- and teacher-rated ADHDsymptoms (described above) using the Mplus softwarepackage (Muthén & Muthén 1998-2008), as fully describedin Martel et al. (2010b). The bifactor model of ADHDshown in Fig. 1 exhibited good fit to the data based on thecomparative fit index of 0.99, root mean square error ofapproximation of 0.05, and CMIN/df of 2.43, although thechi-square statistic was significant (Χ2[59]=143.18, p<0.01), and the bifactor model exhibited better fit to the data

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compared to one-factor, two-factor, three-factor, andsecond-order factor models (e.g., all other models’ RMSEAwere 0.11 or above; see Martel et al. 2010b). It should benoted that a bifactor model with orthogonal factors was alsotested, but exhibited poor fit to the data (see Martel et al.2010b). However, based on theoretical grounds, the non-orthogonal model was retained as representing a conceptu-alization of ADHD consistent with partially distinct path-ways. Based on the pattern of factor loadings in the bifactormodel, which change slightly based on the entry order ofsymptoms in the factor analysis, these latent factors wereconceptualized as representing “s” inattention (or morespecifically poor sustained effort), “s” hyperactivity-impulsivity (or overactivity), and “g” ADHD (or thoughtfulattention + overactivity). The factor scores from this modelwere subsequently saved.

Bivariate correlations were then calculated between theADHD latent factors scores (i.e., specific inattention,specific hyperactivity-impulsivity, and general ADHD)and child problem behaviors, cognitive control, andpersonality traits. The same correlations were then repeatedwith inattentive, hyperactive-impulsive, and total ADHDsymptom counts, as defined in DSM-IV, as a comparison. Inorder to decrease the likelihood of Type I error due to thenumber of conducted correlations, statistical significance isonly interpreted at the 0.01 level (0.05/3 due to three sets ofcorrelations conducted with specific inattention, specifichyperactivity-impulsivity, and general ADHD).

Person-centered analysis of the latent factor scores wasconducted via latent profile analysis. Model fit wascompared using log-likelihood, Akaike information criteria(AIC), Bayes information criteria (BIC), and entropy.Smaller values of log-likelihood, AIC, and BIC indicatebetter fit to the data or increased probability of replication,and higher values of entropy reflect better distinctionsbetween groups (Kline 2005). Because some evidencesuggests that the BIC performs best of the informationcriterion indices (Nylund et al. 2007), BIC was prioritized.

External validation of the best-fitting profile solutionwas conducted via comparison of mean group differencesin external validation measures, evaluated using multivar-iate analysis of variance, followed by corrected Tukeyposthoc analyses.

Results

External Validation of Group-Based ADHD Latent FactorScores Bivariate correlations were calculated betweenADHD latent factor scores, DSM-IV ADHD symptomcounts, and child problem behaviors, cognitive control,personality traits, and genetic risk. As shown in Table 2, theADHD latent factors exhibited a more nuanced pattern of

associations compared to ADHD symptom counts. Thespecific hyperactivity-impulsivity and the general ADHDfactors were relatively more common in boys compared togirls and associated with younger age. The specificinattention factor was associated with older age.

As shown in Table 2, the ADHD latent factor scoresshowed differential associations with child problem behav-iors, but the DSM-IV ADHD symptom counts did not.Specific hyperactivity-impulsivity and general ADHD weresignificantly associated with increased anxiety/depression(r=0.28, 0.20, p<0.01 respectively), social problems (r=0.43,0.33, p<0.01), rule-breaking behavior (r=0.53, 0.43, p<0.01), and aggression (r=0.56, 0.47, p<0.01 respectively). Incontrast, specific inattention was only significantly associatedwith increased withdrawal/depression (r=0.15, p<0.05).Inattentive, hyperactive-impulsive, and total DSM-IVADHD symptoms exhibited significant associations with allchild problem behaviors except withdrawal/depression.

The ADHD latent factor scores showed differentialassociations with child cognitive control, but again the ADHDsymptom counts did not. The specific hyperactivity-impulsivity factor and general ADHD factor exhibitedsignificant associations with cognitive control. Specifichyperactivity-impulsivity and general ADHD, but not specificinattention, were significantly associated with worse responseinhibition (r=0.29, 0.26; p<0.01), increased response vari-ability (r=0.34, 0.29; p<0.01), and worse set-shifting(r=0.14, 0.11 time; r=0.18, 0.15 errors; all p<0.05). Incontrast, inattentive, hyperactive-impulsive, and total DSM-IVADHD symptoms showed significant associations with allindices of cognitive control.

Personality traits exhibited somewhat differential rela-tions with the ADHD factor scores, but the DSM-IVADHDsymptom counts did not. Specific inattention, specifichyperactivity-impulsivity, and the general ADHD factorexhibited uniform associations with low resiliency (r=−0.16, −0.37, −0.23, p<0.01 respectively), low conscien-tiousness (r=−0.19, −0.64, −0.44, p<0.01), and highneuroticism (r=0.14, 0.24, 0.13, p<0.01). Specifichyperactivity-impulsivity and general ADHD were addi-tionally associated with low effortful control (r=−0.44,−0.31, p<0.01 respectively) and high negative emotionality(r=0.39, 0.31, p<0.01). However, some dissociation inrelations was also noted. Specific inattention was associatedwith high reactive control (r=0.18, p<0.01), high agree-ableness (r=0.14, p<0.01), and low extraversion (r=−0.24,p<0.01), while specific hyperactivity-impulsivity and generalADHD were associated with the reverse profile: lowreactive control (r=−0.54, −0.53, p<0.01 respectively),low agreeableness (r=−0.46, −0.45, p<0.01), and highextraversion (r=0.29, 0.36, p<0.01). In contrast, DSM-IVADHD symptom counts exhibited significant associationswith all personality traits, and the direction of the correla-

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tions was the same for inattentive, hyperactive-impulsive,and total ADHD symptoms.

Person-Centered Analysis of Latent Factor Scores Latentprofile analysis evaluating two through six profile solutionswas conducted on the three latent factor scores. As shownin Table 3, model fit improved as the number of profilesincreased, up to the five profile solution. Beginning at sixprofiles, model fit declined, particularly as measured by theBIC which was the prioritized fit index. The five profilesolution scores were retained for further analysis. Based ondescriptive statistics, the five profiles were labeled asfollows: “low symptoms,” “specific inattention,” “norma-tive symptoms,” “general ADHD,” and “general ADHD +specific hyperactivity-impulsivity.” There appeared to bethree main subgroups within the ADHD diagnostic catego-ry: “specific inattention,” “general ADHD,” and “generalADHD + specific hyperactivity-impulsivity;” thus, these

three profiles were considered as potential ADHD subtypesduring the external validation analyses. Most of the DSM-IV-defined ADHD-C subtype was split between the “gen-eral ADHD” and “general ADHD + specific hyperactivity-impulsivity” profile groups, whereas most of the DSM-IV-defined ADHD-PI subtype fell into the “specific inatten-tion” profile group. There were more boys in the “generalADHD + specific hyperactivity-impulsivity” profile groupcompared to the other groups. Further, the “general ADHD+ specific hyperactivity-impulsivity” and “general ADHD”groups were relatively younger than the other groups andthe “specific inattention” profile group was relatively olderthan the other ADHD profile groups.

External Validation of Person-Centered Latent Profiles Mul-tivariate analyses of variance (MANOVA) were conductedto examine latent profile and DSM-IV subtype differencesin child problem behaviors, cognitive control, and person-

Table 2 Bivariate correlations among psychopathology, cognitive control, traits, and parent- and teacher-rated specific and general ADHD latentfactor scores

“s” Inattention (Symptoms) “s” Hyperactive-Impulsive (Symptoms) “g” ADHD (Symptoms)

Males (Mean) −0.08(4.49**) 0.14**(3.45**) 0.22**(9.22**)

Females (Mean) −0.08(2.76) −0.13(3.18) −0.11(5.80)Age 0.15**(−0.27**) −0.28**(−0.25**) −0.31**(−0.27**)Psychopathology

Anxiety/depression 0.03(0.21**) 0.28**(0.30**) 0.20**(0.27**)

Withdrawal/depression 0.15*(0.10) 0.11(0.08) 0.01(0.09)

Somatic complaints 0.09(0.18**) 0.19**(0.22**) 0.10(0.21**)

Social problems 0.03(0.38**) 0.43**(0.44**) 0.35**(0.42**)

Rule-breaking behavior −0.02(0.47**) 0.53**(0.52**) 0.43**(0.51**)

Aggressive behavior −0.06(0.47**) 0.56**(0.57**) 0.47**(0.54**)

Cognitive Control

Response inhibition −0.03(0.28**) 0.29**(0.27**) 0.26**(0.28**)

Response variability −0.03(0.33**) 0.33**(0.32**) 0.29**(0.33**)

Trail B Time 0.03(0.14**) 0.14**(0.15**) 0.11*(0.15**)

Trails B Errors 0.003(0.17**) 0.18**(0.18**) 0.15**(0.18**)

Trails A Time 0.06(0.10*) 0.11*(0.12**) 0.06(0.11**)

Traits

Neuroticism 0.14**(0.26**) 0.24**(0.27**) 0.13**(0.27**)

Extraversion −0.24**(0.23**) 0.29**(0.31**) 0.36**(0.28**)

Agreeableness 0.14**(−0.40**) −0.46**(−0.44**) −0.45**(−0.43**)Conscientiousness −0.19**(−0.65**) −0.64**(−0.66**) −0.44**(−0.67**)Effortful Control −0.08(−0.45**) −0.44**(−0.44**) −0.31**(−0.46**)Reactive Control 0.18**(−0.49**) −0.54**(−0.55**) −0.53**(−0.53**)Resiliency −0.16**(−0.39**) −0.37**(−0.39**) −0.23**(−0.40**)Negative Emotionality 0.02(0.35**) 0.39**(0.40**) 0.31**(0.39**)

Genetic Risk

Genetic Risk Composite −0.01(0.10*) 0.09*(0.10*) 0.08(0.10*)

*p<0.05. **p<0.01. Correlations with latent factor scores. () Correlations with DSM-IV ADHD symptom counts

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ality traits, examined separately, with corrected Tukeyposthoc comparisons. Significant multivariate bifactorprofile differences were evident for child problembehaviors (F[24,1156]=6.51; p<0.01), cognitive control(F[16,1716]=4.56; p<0.01), and personality traits (F[32,1280]=6.87; p<0.01), and significant multivariateDSM-IV subtype differences were evident for childproblem behaviors (F[16,600]=34.18; p<0.01), cognitivecontrol (F[8,864]=4.62; p<0.01), and personality traits (F[16,638]=11.20; p<0.01). As shown in Table 4, thegeneral ADHD and general ADHD + specif ichyperactive-impulsivity groups exhibited elevated socialproblems, rule-breaking behaviors, and aggressive behav-iors, compared to the group with low symptoms (all p<0.01). The general ADHD, general ADHD + specifichyperactivity-impulsivity, and specific inattentive groupsall exhibited elevated anxiety/depression and somatic com-plaints compared to the group with low symptoms (p<0.01).As shown in Table 5, the DSM-IVADHD subtypes exhibiteda similar pattern of results with the ADHD-PI subtypeexhibiting levels of social problems, rule-breaking behaviors,and aggressive behaviors intermediate between that of theADHD-C subtype and control groups, and both ADHDsubtypes exhibiting elevated anxiety/depression and somaticcomplaints compared to controls.

The bifactor profile-based ADHD groups exhibited adistinct profile of cognitive control deficits. While thespecific inattentive group exhibited significantly slowercognitive speed than the other groups, the general ADHD +specific hyperactive-impulsive group exhibited worse set-shifting, and the general ADHD and general ADHD +specific hyperactive-impulsive groups exhibited signifi-cantly worse response inhibition and greater responsevariability. The DSM-IV ADHD subtype comparison didnot indicate such a differentiated profile; the ADHD-Csubtype exhibited significantly worse performance on allmeasures except for cognitive speed, on which there wereno subtype differences.

Finally, the personality trait profiles of the groups wereas expected with the general ADHD and general ADHD +specific hyperactive-impulsive groups exhibiting worse, ormore extreme, personality trait profiles (i.e., lower effortful/reactive control, lower resiliency, higher extraversion, andlower agreeableness and conscientiousness) compared tothe low symptoms group with the specific inattention groupexhibiting an intermediate profile between the extremegeneral ADHD and general ADHD + specific hyperactive-impulsive groups and the low symptoms group. For theDSM-IVADHD subtype comparisons, neuroticism, consci-entiousness, effortful control, resiliency, and negativeemotionality were significantly worse in children withADHD versus controls. Children with ADHD-C had higherextraversion, lower agreeableness, and less reactive controlthan control children and children with ADHD-PI.

Discussion

The present study provided external validation of the newly-supported bifactor structural model of ADHD (Martel et al.2010b; Toplak et al. 2009) using two approaches: (1) acontinuous symptom severity approach using latent factorscores, similar to ADHD symptom domains, and (2) acategorical subtype approach using profiles derived from thelatent factor scores, similar to subtypes. The continuous andcategorical extensions of the bifactor ADHD model werethen evaluated via examination of their utility in explainingheterogeneity within ADHD in relation to child problembehaviors, cognitive control, and temperament and person-ality traits and as compared to DSM-IV ADHD symptomcounts and subtypes. Using a continuous approach, specificinattention (or poor sustained attention, based on itemloadings) exhibited a unique pattern of associations andwas associated with withdrawal/depression. In contrast,specific hyperactivity-impulsivity (or overactivity, based onitem loadings) and general ADHD were associated with

Loglikelihood AIC BIC entropy

2-profile −1103.34 2226.68 2269.44 0.88

3-profile −947.22 1922.44 1982.32 0.91

4-profile −907.70 1851.39 1928.37 0.89

5-profile −785.75 1615.50 1709.59 0.91

6-profile −785.75 1623.50 1734.69 0.92

n inatt hyper ADHD ADHD% ADHD-PI/C%

profile 1 160 −0.10 −0.56 −0.61 6.85 3.75/0.63

profile 2 63 0.95 −0.10 −0.65 93.55 66.67/1.59

profile 3 135 −0.05 0.05 0.09 62.28 20/17.04

profile 4 101 −0.26 0.48 0.71 95.65 3.96/62.38

profile 5 73 −0.71 0.76 1.31 97.01 4.11/61.64

Table 3 Latent profile analysisfit indices and five-profile de-scriptive statistics

ADHD% percentage of childrenwith ADHD within each profilegroup, ADHD-PI/C% percent-age of children in each profilegroup with ADHD-PI/ADHD-C

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anxiety/depression, social problems, rule-breaking and ag-gressive behavior. Specific inattention was associated withhigh reactive control, high agreeableness, and low extraver-sion, while specific hyperactivity-impulsivity and generalADHD were associated with low reactive control, lowagreeableness, and high extraversion. Using a subtypeapproach, individuals with high specific inattention exhibitedslower speed-based cognitive performance compared toindividuals with other ADHD bifactor subtypes. Whilethese patterns of association were reassuringly similar topatterns seen with a DSM-IV model, the associationsevidenced using the bifactor model indices were moredifferentiated than those using DSM-IV-based indices.Thus, these findings provide support for the validity ofthe bifactor ADHD model in that it seems to provide abetter parsing of DSM-IV ADHD heterogeneity, consis-tent with a partially distinct dual-pathway model ofADHD (Nigg 2006; Sonuga-Barke 2005).

Person-centered data analysis of general ADHD, specificinattention, and specific hyperactive-impulsive latent factorsrevealed five groups of children across those with and without

ADHD: those with low symptoms, high specific inattention,normative levels of symptoms, general ADHD, and generalADHD + specific hyperactivity-impulsivity. Within theADHD diagnostic category, there appeared to be three“subtypes”: high specific inattention, general ADHD, andgeneral ADHD + specific hyperactivity-impulsivity. Childrenin the specific inattention group with low “g” ADHD werecharacterized by a different cognitive profile than children inthe general ADHD and general ADHD + specifichyperactivity-impulsivity groups. Children with specificinattention appear to be slower at completing speededcognitive tasks, in line with the conception of a group ofchildren with ADHD characterized by a restrictive inattentivepresentation as in the proposed DSM-5 criteria (dsm5.org)and similar to the restrictive inattentive or “ADD” groupstudied in this sample in other reports (Carr et al. 2010; Goth-Owens et al. 2010) and proposed by McBurnett et al. (2001).In contrast, the general ADHD and general ADHD +specific hyperactive-impulsive groups exhibited a morecharacteristic cognitive profile of impaired set-shifting, poorresponse inhibition, and greater response variability.

Table 4 Latent profile external validation and correlates

Low Symptoms Inattention Normative Symptoms ADHD ADHD+hyper

Mean (standard deviation)

Males (percentage) 45.22 53.97 59.40 63.37 82.19**

Age 12.49(3.14)a 12.43(3)a 11.77(2.98)a 10.81(2.89)b 9.90(2.28)b**

Psychopathology

Anxiety/depression 2.07(2.21)a 3.93(3.54)b 4.60(3.86) 4.66(3.36)b 5.12(3.97)b**

Withdrawal/depression 1.80(2.37) 2.82(2.94) 2.80(2.75) 2.3(2.71) 2.58(2.98)

Somatic complaints 1.11(1.78)a 2.68(3.47)b 2.32(2.31) 2.79(3.26)b 2.09(2.27)b**

Social problems 1.32(1.86)a 2.73(2.55) 3.15(2.93) 4.77(3.58)b 5.33(3.95)b**

Rule-breaking 0.98(1.46)a 2.61(2.28) 3.31(3.35) 5.28(3.62)b 4.91(3.48)b**

Aggression 2.26(3.59)a 6.48(5.31) 7.33(5.36) 11.98(6.66)b 12.12(8.53)b**

Cognitive Control

Trails A Time 22.71(0.89)a 28.83(1.42)b 23.82(.96)a 26.46(1.12)a 26.19(1.30)a**

Trails B Time 49.96(28.54)a 57.62(32.71) 58.26(45.53) 59.19(44.99) 67.56(54.23)bTrails B Errors 0.44(0.10)a 0.67(0.17)a 0.60(0.11)a 0.65(0.13)a 1.11(0.15)b*

SSRT 243.46(96.91)a 304.54(167.57)a 295.05(140.14)a 332.03(152.99)b 359.25(177.53)b**

RT variability 148.43(43.29)a 167.87(55.85)a 166.58(64.91)a 192.41(66.63)b 196.17(61.57)b**

Traits

Neuroticism 4.06(1.32)a 4.70(1.36) 4.43(1.28) 4.58(1.22)b 4.48(1.08)b*

Extraversion 5.37(1.13)a 5.50(1.28) 5.60(1.50) 6.40(1.26)b 6.63(1.25)b**

Agreeableness 6.92(1.03)a 6.38(1.21) 6.12(1.21) 5.47(1.26)b 5.47(1.30)b**

Conscientiousness 6.17(1.43)a 4.21(1.62) 4.57(1.39) 3.96(1.33)b 3.96(1.22)b**

Effortful control 3.46(0.55)a 2.78(0.68) 3.01(0.69) 2.71(0.66)b 2.61(0.74)b**

Reactive control 5.10(0.87)a 4.68(1.05) 4.67(1.07) 3.71(0.88)b 3.53(0.99)b**

Resiliency 6.02(0.96)a 5.47(0.94) 5.62(1.11) 5.34(0.97)b 5.44(0.99)b**

Negative emotion 3.76(1.28)a 4.49(1.23) 4.45(1.34) 4.58(1.27)b 4.80(1.17)b**

*p<0.05; **p<0.01, via chi-square or MANOVA. Subscripts indicate significant differences

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The three latent profile subgroups found in the currentstudy appear to be substantively distinct from the currentDSM-IV ADHD subtypes, particularly in regard to theircognitive profiles. The DSM-IV ADHD-C subtypeexhibited worse cognitive performance than ADHD-PIand non-ADHD children. In contrast, children with highspecific inattention exhibited slow cognitive speed, whereaschildren with high general ADHD exhibited the classiccognitive profile of poor response inhibition and highperformance variability, and children with general ADHDliability + high hyperactivity-impulsivity additionallyexhibited classic impulsivity, making many errors. Further,the current study did not find a group of children withrelatively “pure” specific hyperactivity-impulsivity, ratheronly a group of children with specific hyperactivity-impulsivity combined with general ADHD liability.

Thus, there may be some merit in distinguishing childrenwith “pure” inattention from children with a clinicallysignificant number of symptoms across both domains(including those with particularly high levels ofhyperactivity-impulsivity). The bifactor ADHD model heresuggests that these children are characterized by poorsustained effort, consistent with conceptions of low arousal,poor attention, or perhaps sluggish cognitive tempo (Carr et al.2010; Goth-Owens et al. 2010; McBurnett et al. 2001) and

perhaps related to the ADHD-Restrictive Inattentive presen-tation subtype put forward for DSM-5 (www.dsm5.org on3/1/2011). Consideration of these different cognitive controldeficit profiles associated with ADHD is also importantbecause the nature of cognitive control deficits have animpact on children’s academic performance (Biederman et al.2004; Klingberg et al. 2005; Rueda et al. 2005).

Child problem behaviors exhibited differential associa-tions with specific and general ADHD latent factor scores,as compared to their associations with DSM-IV ADHDsymptom counts. In line with previous reviews (Barkley2003; Milich et al. 2001), the current study found thatspecific inattention was uniquely associated with withdraw-al/depression, while specific hyperactivity-impulsivity andgeneral ADHD were associated with rule-breaking andaggressive behavior, as well as social problems and anxiety/mood problems. The current study extends prior work bysuggesting that the specific inattention domain is specifi-cally related to withdrawal/depression. Further, the associ-ation of rule-breaking and aggressive behaviors and socialproblems with hyperactivity-impulsivity may explain thehigh comorbidity between ADHD and other disruptivebehavior problems like ODD (Angold et al. 1999; Jensen etal. 1997). Thus, it should be noted that the pattern ofassociations between the bifactor latent factor scores and

Control ADHD-Inattentive ADHD-Combined

Mean (standard deviation)

Males (percent) 50.81 50.91 67.19

Age 11.95 12.35 10.81

Psychopathology

Anxiety/depression 2.80(2.90)a 4.32(3.54)b 5.03(3.97)b**

Withdrawal/depression 2.18(2.72) 2.62(2.55) 2.73(2.80)

Somatic complaints 1.41(2.1)a 2.66(2.66)b 2.67(3.19)b**

Social problems 1.87(2.52)a 2.89(2.67)b 4.92(3.59)c**

Rule-breaking 1.54(1.81)a 2.96(2.45)b 5.24(3.99)c**

Aggression 3.76(4.85)a 6.34(4.85)b 11.86(7.26)c**

Cognitive Control

Trails A Time 23.77(9.40) 26.58(10.66) 25.50(11.03)

Trails B Errors 0.50(1.13)a 0.61(0.91) 0.86(1.39)b**

SSRT 271.62(124.52)a 281.09(142.09)a 343.41(165.05)b**

RT variability 161.38(57.07)a 161.41(53.70)a 188.21(65.23)b**

Traits

Neuroticism 4.12(1.28)a 4.76(1.29)b 4.59(1.17)b**

Extraversion 5.56(1.31)a 5.54(1.41)a 6.42(1.30)b**

Agreeableness 6.49(1.23)a 6.31(1.16)a 5.53(1.31)b**

Conscientiousness 5.58(1.58)a 4.08(1.40)b 3.90(1.23)bEffortful control 3.23(0.71)a 2.86(0.63)b 2.69(0.69)b**

Reactive control 4.86(1.08)a 4.62(0.97)a 3.68(0.94)b**

Resiliency 5.93(0.97)a 5.35(1.07)b 5.36(0.98)b**

Negative emotion 4.03(1.33)a 4.53(1.30)b 4.72(1.20)b**

Table 5 ADHD subtype exter-nal validation and correlates

*p<0.05; **p<0.01, via chi-square or MANOVA. Subscriptsindicate significant differences,based on posthoc Tukey tests

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child problem behaviors were similar to what one wouldexpect based on prior work conducted on DSM-IV ADHDsymptoms. Yet, the external validation profile is somewhatsharper and more differentiated, suggesting some improve-ments in conceptual explanation using the bifactor model.However, it should be noted that associations with the “s”inattentive factor were modest in nature, compared toassociations with the “s” hyperactive-impulsive and “g”ADHD factor.

Personality traits showed largely expected associationswith specific and general ADHD factor scores. In line withprevious work on trait-symptom associations (Martel andNigg 2006; Nigg et al. 2002; Parker et al. 2004), all facetsof ADHD (i.e., specific inattention, specific hyperactivity-impulsivity, and general ADHD) were associated with lowconscientiousness, high neuroticism, and low resiliency.Specific hyperactivity-impulsivity and general ADHD wereassociated with low effortful control and high negativeemotionality. Extraversion, agreeableness, and reactivecontrol exhibited differential relations with specific inatten-tion versus specific hyperactivity-impulsivity and generalADHD. Whereas high extraversion, low agreeableness, andlow reactive control were associated with specifichyperactivity-impulsivity and general ADHD, these rela-tions were reversed for specific inattention. Specificinattention was associated with low extraversion, highagreeableness, and high reactive control. This heteroge-neous pattern of association between traits and ADHDfacets did not emerge when DSM-IV ADHD symptomcounts were examined. Yet, these results help explainprior contradictory findings regarding the association oftraits with ADHD, particularly extraversion (e.g., Nigg etal. 2002).

Present study findings should be interpreted in light ofseveral study limitations. The present study tests a bifactormodel of ADHD in a community-recruited sample enrichedfor ADHD. This is a strength in that this type of sample isneeded to validate the model. However, the model alsoneeds to be validated using other sampling frames,including clinic referred cases and general populationsamples (looking at ADHD traits), as well as in other ageranges (e.g., adulthood). Orthogonal bifactor models, whichexhibited poor fit in the current sample (Martel et al.2010b), should also be further evaluated, and it should benoted that orthogonal bifactor models have receivedsupport in other studies of the bifactor model of ADHD(e.g., Toplak et al. 2009). In addition, the prospective utilityfor utilizing general and specific ADHD latent factors mustbe further evaluated using a longitudinal design in order toassess the predictive power of such a method. Personalitytrait and child comorbid problem behavior measurementwas based on mother ratings on questionnaires; teacherratings and observational measures should also be exam-

ined. Future research should evaluate this model usingadditional external validation indices (e.g., course of illness,treatment response, neuroimaging correlates; Cantwell1992), as well as evaluate the utility of bifactor models ofgeneral psychopathology (e.g., Krueger et al. 2002; Martelet al. 2010a).

Overall, the current study provides some externalvalidation of the bifactor model of ADHD via examinationof child problem behaviors, cognitive control, and person-ality traits in that the bifactor model appears to provide amore nuanced and accurate description of the heteroge-neous group of children diagnosed with ADHD. Usingvariable- and person-centered data analytic strategies,specific inattention was associated with depression/with-drawal and slower cognitive task performance, as well asintroversion, agreeableness, and high reactive control,whereas specific hyperactivity-impulsivity was associatedwith rule-breaking and aggressive behavior, social prob-lems, a more impulsive response pattern, extraversion,disagreeableness, and low reactive control. These profilesmerit attention during clinical assessment for ADHD andmay have implications for the planning of individually-tailored intervention for this heterogeneous group ofchildren. Finally, study results suggest future directions forthe development of more homogeneous behavioral compo-sitions that could be used to elucidate etiological geneticassociations with complex behavioral phenotypes likeADHD.

Acknowledgments This research was supported by NIH NationalInstitute of Mental Health Grant R01-MH63146 and MH59105 to JoelNigg and MH70542 to Karen Friderici and Joel Nigg. We are indebtedto the families and staff who made this study possible.

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