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Effects of IQ on Executive Function Measures in Children with ADHDE. Mark Mahone; Kathleen M. Hagelthorn; Laurie E. Cutting; Linda J. Schuerholz; Shelley F. Pelletier;Christine Rawlins; Harvey S. Singer; Martha B. Denckla

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To cite this Article Mahone, E. Mark , Hagelthorn, Kathleen M. , Cutting, Laurie E. , Schuerholz, Linda J. , Pelletier,Shelley F. , Rawlins, Christine , Singer, Harvey S. and Denckla, Martha B.(2002) 'Effects of IQ on Executive FunctionMeasures in Children with ADHD', Child Neuropsychology, 8: 1, 52 — 65To link to this Article: DOI: 10.1076/chin.8.1.52.8719URL: http://dx.doi.org/10.1076/chin.8.1.52.8719

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Child Neuropsychology 0929-7049/02/0801-052$16.002002, Vol. 8, No. 1, pp. 52–65 # Swets & Zeitlinger

Effects of IQ on Executive Function Measuresin Children with ADHD

E. Mark Mahone1,2, Kathleen M. Hagelthorn1,2, Laurie E. Cutting1,2, Linda J. Schuerholz1,2,

Shelley F. Pelletier1,2, Christine Rawlins1,2, Harvey S. Singer2, and Martha B. Denckla1,2

1Kennedy Krieger Institute, Baltimore, MD, USA, and 2Johns Hopkins University School of Medicine, Baltimore, MD, USA

ABSTRACT

The present study compared children with Attention-Deficit Hyperactivity Disorder (ADHD) and controls ona selected set of clinical measures of executive function (EF). A total of 92 children (51 ADHD, 41 control),ages 6–16, completed measures chosen from a larger neuropsychological battery to illustrate diversecomponents of the EF construct (planning, inhibitory control, response preparation, memory search). Theselected measures were moderately correlated with one another, and moderately correlated with IQ. Aftercontrolling for age, sex, presence of learning disability (LD), ADHD, and IQ test version, Full Scale IQ wassignificantly related to four of the five selected EF measures. A second analysis showed group differences onthe EF measures at different IQ levels. After covarying for age, there was a significant multivariate effect forIQ level (average, high average, superior) and a significant multivariate interaction between group (ADHDvs. control) and IQ level. Three of the five selected EF measures showed significant univariate group effects(controls performing better than ADHD) at the average IQ level; however, there were no significant groupdifferences between children with ADHD and controls at high average or superior IQ levels. These resultssuggest that clinical measures of EF may differ among children with ADHD and controls at average IQlevels, but there is poorer discriminatory power for these measures among children with above average IQ.

In recent years, neuropsychological investiga-

tions of Attention-Deficit Hyperactivity Disorder

(ADHD) have focused on executive function and

the role of the frontal-striatal-cerebellar brain

systems (Barkley, 2000; Castellanos, 1997;

Heilman, Voeller, & Nadeau, 1991; Pennington

& Ozonoff, 1996). The construct of EF is

especially important in children, as it is consid-

ered central in successful acquisition and efficient

use of academic skills – particularly in efforts to

overcome learning disorders (Denckla, 1996a).

Executive function (EF) is a term used to refer to

self-regulatory behaviors necessary to select and

sustain actions and guide behavior within the

context of goals or rules. In essence, EF involves

developing and implementing an approach to

performing a task that is not habitually performed

(Mahone et al., 2002). Initiation, planning, shift-

ing of thought or attention, organization, inhibi-

tion of inappropriate thought or behavior, and

efficiently sustained and sequenced behavior are

crucial elements of the EF construct. As such, EF

should be treated as a collection of constructs –

fundamental resources separable from the specific

cognitive (i.e., linguistic, visuospatial) domains in

which they are assessed (Harris et al., 1995). A

very influential model de-emphasizing ‘‘atten-

tion’’ while highlighting inhibition (Barkley,

1997a) posits deficient inhibitory control as the

core of ADHD. Other researchers, however, have

proposed that response inhibition acts in parallel

with other ‘‘intentional’’ skills including response

Address correspondence to: E. Mark Mahone, Department of Neuropsychology, Kennedy Krieger Institute, 1750East Fairmount Ave., Baltimore, MD 21231 USA. E-mail: [email protected] for publication: July 27, 2002.

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preparation and working memory (Pennington,

1997).

Typically developing children demonstrate

major periods of gain on measures of EF during

the school years. These gains are thought to

correspond to periods of myelination and func-

tional maturation of the prefrontal cortex (Levin

et al., 1991; Welsh, Pennington, & Grossier,

1991). Investigations comparing children with

ADHD to controls on clinical measures of EF,

however, have yielded inconsistent findings

(Barkley & Grodzinzky, 1994; Barkley,

Grodzinsky, & DuPaul, 1992; Sergeant, Guerts, &

Oosterlaan, 2002). The most robust group differ-

ences involved constructs captured by continuous

performance tests (CPTs), including commission

errors, response latency (Levy & Hobbes, 1997),

and variability in response time (Harris et al.,

1995). Barkley (1994) re-analyzed some of his

earlier published data (Barkley et al., 1992) and

found that CPTs and letter word fluency (LWF)

tasks had adequate positive predictive power in

discriminating children with ADHD from con-

trols. Conversely, while other EF measures (e.g.,

Wisconsin Card Sorting Test, Trailmaking Tests,

and Stroop Test) showed significant group differ-

ences between controls and children with ADHD,

they often produced an unacceptably high false

negative rate (Lovejoy et al., 1999), suggesting

that some children with ADHD can perform

normally on these EF measures (Gordon &

Barkley, 1998).

The inconsistency of findings and failure of

clinical EF measures to consistently distinguish

children with ADHD from controls may be attrib-

uted to a variety of factors. These include diverse

definitions of the EF construct (Barkley, 1997b;

Denckla, 1996b), variability in criteria used to

define experimental populations with ADHD

(NIH, 1998), dosing and effects of stimulant

medicines used during testing (Nigg, Hinshaw,

& Halperin, 1996; O’Toole, Abramowitz, Morris,

& Dulcan, 1997), as well as the relationship

between performance on EF measures and sex

(Grodzinsky & Diamond, 1992; Seidman et al.,

1997), presence of learning disabilities (Seidman,

Biederman, Monuteaux, Doyle, & Faraone,

2001), or IQ (Ardila, Pineda, & Rosselli, 1999;

Arffa, Lovell, Podell, & Goldberg, 1998; Dodrill,

1997, 1999; Welsh & Pennington, 1988). Barkley

(1998) asserted that inconsistencies in research

may be due to a lack of theory driving clinical

studies. Further, he argued that current definitions

of ADHD, especially the Diagnostic and Statis-

tical Manual of Mental Disorders, Fourth Edition

(DSM–IV; American Psychiatric Association

[APA], 1994) fail to define how developmental

inappropriateness of behavioral symptoms should

be established and measured at different age

levels. Barkley’s theory presents ADHD as a

disorder of performance, not of knowledge.

Indeed, previous research has demonstrated that

the behavioral deficits seen in ADHD occur at

all levels of intellectual functioning (Alyward,

Gordon, & Vehulst, 1997); however, many of

the measures used to assess executive dysfunction

(EdF) in ADHD correlate highly with IQ (Reader,

Harris, Schuerholz, & Denckla, 1994). Arguably,

the failure to find consistent group differences

between children with ADHD and controls has

been due to group differences in IQ, or the ability

of brighter children to use their intellectual skills

to compensate within the structured setting of

laboratory measures, even when they may not

be able to do so in the less structured reality of

everyday life.

The issue of neuropsychological test perfor-

mance among individuals with above average IQ

has been the topic of some controversy (Jung,

Yeo, Chiulli, Sibbitt, & Brooks, 2000; Russell,

2001; Tremont, Hoffman, Scott, & Adams, 1998).

Dodrill (1997, 1999) observed that while IQ

scores below the average range are often corre-

lated with a variety of neuropsychological mea-

sures, the same relationship does not hold true for

individuals with average to above average IQ. The

reason for this finding may be that in contrast to

IQ tests, most neuropsychological measures were

designed to measure deficits. Ceiling effects of

the neuropsychological measures may limit the

correlation with IQ among individuals with above

average IQ (Russell, 2001). Children with ADHD

are often found to have impairments in ‘‘real-

world’’ functioning, even when they have above

average intelligence and are free from associated

learning disorders (Denckla, 1996b). These find-

ings call into question the ecological validity of

laboratory measures of EF, especially among

IQ, ADHD AND EXECUTIVE FUNCTION 53


bright or gifted children, and suggest that perfor-

mance-based measures of EF may be of limited

utility in these groups.

The purpose of the present experiment was to

examine the relationship between IQ and perfor-

mance on commonly used clinical measures of

EF, while controlling for variables likely to share

variance with EF measures. Specifically, we

hypothesized that IQ would contribute signifi-

cantly and independently to variance in measures

of EF, even after controlling for age, sex, diag-

nostic status (i.e., ADHD, LD), and use of med-

ication. We also hypothesized that performance

differences observed between children with

ADHD and controls would be less pronounced

or absent among children with above average IQ.

METHOD

ParticipantsNinety-two children participated in the present study.There were a total of 51 children with ADHD (32males, 19 females) and 41 controls (18 males, 23females). These children were participants in a largerstudy (Neurodevelopmental Pathways to LearningDisabilities) at the Kennedy Krieger Institute betweenthe years 1990 and 2001, and part of two cohorts (i.e.,

those recruited and tested from 1990 to 1995 and thoserecruited and tested from 1996 to 2001). There were noprocedural differences in recruitment between the twocohorts. Mean ages for the ADHD and control groupswere 8.9 (SD¼ 2.0) and 10.1 (SD¼ 2.4) respectively.Children were included in the study if they werebetween the ages of 6 and 16, and free from a history ofseizures, head injury or other neurologic illness. Allparticipants had Full Scale IQs of 85 or above (range85–145). The sample was drawn from largely middleclass SES, and was predominantly caucasian (94% forthose given WISC–R; 93% for those given WISC-III).Demographic information is listed in Table 1.

ADHD Group CriteriaChildren included in the ADHD group met diagnosticcriteria for ADHD as follows: (1) identification andreferral by community professionals (psychologists,psychiatrists, pediatricians, neurologists) as having acurrent diagnosis of ADHD at the time of referral; and,(2) independent diagnosis of ADHD at the time oftesting according to DSM-III–R (American PsychiatricAssociation [APA], 1987) criteria for children testedprior to 1995, or DSM-IV criteria (any type) forchildren tested 1995 and after, with diagnosis based ona positive rating on two of three measures: (a) at least 8of 14 items endorsed from the ADHD Scale of theDiagnostic Interview for Children and Adolescents –Revised, Parent Form (DICA–R; Welner, Reich,Herjanic, Jung, & Amado, 1987), conducted by atrained interviewer and confirmed by a licensed

Table 1. Demographic Information.

ADHD Control Total

N 51 41 92WISC–R 51 30 81WISC-III 0 11 11Male 32 18 50Female 19 23 42With LD 13 1 14CNCBCL mean�� 77.6 (6.9) 47.5 (12.4) 64.2 (17.9)Mean age� 8.9 (2.0) 10.1 (2.4) 9.4 (2.3)Mean FSIQ 114.9 (13.9) 113.1 (12.6) 114.1 (13.3)Reading Composite mean 108.5 (19.8) 114.9 (13.9) 111.3 (17.7)Math Composite mean 111.4 (16.7) 116.2 (15.7) 113.6 (16.4)

Note. Standard deviations in parentheses. WISC–R¼Wechsler Intelligence Scale for Children – Revised; WISC-III¼Wechsler Intelligence Scale for Children, Third Edition; CNCBCL¼T score from either theHyperactivity Scale of the Conners’ Parent Rating Scale, or the T score from the Attention Problems Scalefrom the Child Behavior Checklist; FSIQ¼ Full Scale IQ. Reading Composite¼ either the Broad ReadingComposite from the Woodcock Johnson Revised Tests of Achievement or the Reading Composite from theWechsler Individual Achievement Test (WIAT). Math Composite¼ either the Broad Math Composite fromthe Woodcock Johnson Revised Tests of Achievement or the Math Composite from the WIAT.�p< .05. ��p< .01.

54 E. MARK MAHONE ET AL.


psychologist or child neurologist; (b) a positive scoreon either the ADHD Rating Scale (DuPaul, 1991;DuPaul, Power, Anastopoulos, & Reid, 1998), with apositive score indicating a parent rating of 2 or higher(on a 4-point Likert Scale ranging from zero to three)for 6 of 9 items assessing inattention or 6 of 9 itemsassessing hyperactivity/impulsivity; and, (c) a positiveCNCBCL1 Index score. Individuals were excludedfrom the ADHD group if they met criteria for any otherpsychiatric diagnosis based on parental responses fromthe DICA–R, or if they or an immediate family memberhad a reported history of an exclusionary neuropsy-chiatric disorder (i.e., autism, ADHD, Conduct Dis-order, anxiety disorder, psychosis). For all participants,excluding psychiatric diagnoses (measured by theDICA–R) included psychosis, Conduct Disorder, MajorDepressive Disorder, mania or hypomania, DsythymicDisorder, Separation Anxiety Disorder, Panic Disorder,Generalized Anxiety Disorder, Phobias, ObsessiveCompulsive Disorder, and Somatization Disorder. Be-cause the ADHD groups were recruited under bothDSM-III–R (prior to 1995) and DSM-IV (1995 andlater) criteria, inattentive and hyperactive/impulsive,and combined types were included in the ADHD group.

Control Group CriteriaChildren in the control group were selected fromparticipants who responded to community-wide adver-tisements. The control group also included unaffectedsiblings from ongoing research projects assessingFragile X, Neurofibromatosis Type 1 and TurnerSyndrome studied at the Kennedy Krieger Institute.Individuals were excluded from the control group ifthey met criteria for any psychiatric diagnosis based onparental responses from the DICA–R (Welner et al.,1987) or if they or an immediate family member had areported history of an exclusionary neuropsychiatricdisorder (i.e., autism, ADHD, Conduct Disorder, anx-iety disorder, psychosis).

Assessment of Learning DisabilitiesLearning disability (LD) status in reading or mathe-matics was calculated for each participant. For thepresent study, LD was defined as a 1.5 standard

deviation discrepancy between FSIQ and achievementon the either the Reading or Math composite fromthe Wechsler Individual Achievement Test (WIAT,Wechsler, 1992), for individuals given the WISC-III(i.e., those tested 1996 and later), or a 1.5 standarddeviation discrepancy between FSIQ and achievementon the Basic Reading or Broad Math composites fromthe Woodcock Johnson Tests of Achievement, Revised(Woodcock & Johnson, 1989), for individuals given theWISC–R (i.e., those tested before 1996). A total of 14children in the sample (13 in the ADHD group and 1 inthe control group) met criteria for LD. Of those meetingcriteria for LD in the ADHD group, 8 had ReadingDisability, 3 had Math Disability, and 2 met criteria forboth Reading and Math Disabilities. The single child inthe control group met criteria for Reading Disability.

ProceduresAll participants completed the assessment as part of aday-long battery of psychoeducational and neuropsy-chological testing. Evaluators were blind to subjectdiagnosis. Mothers of participants completed the be-havior rating scales at the time of the testing. None ofthe participants in either group were on stimulantmedication at the time of the testing. Children withADHD taking other types of psychotropic medicationwere excluded from the study. Those children withADHD who had been taking stimulant medicationwere asked to withhold medication for 48 hr prior totesting.

Rating Scale and Interview Measures

Child Behavior Checklist (CBCL)The CBCL is a child behavior rating scale completedby parents who have a child between the ages 4 and 18(Achenbach, 1991). The checklist consists of a set ofsocial competence items and 118 behavioral problemitems. The behavioral problem items require the parentto use a 3-step response scale (not true, somewhat/sometimes true, very often true). The T score from theAttention Problems scale was analyzed for the presentstudy, and used in the calculation of the CNCBCLIndex.

Conners’ Parent Rating Scale (CPRS)The CPRS assists in the evaluation of problembehaviors by obtaining reports from parents (Conners,1989, 1997). The CPRS Short Form consists of 27items. Parents respond to a 4-point Likert scaleindicating severity of a particular behavior (not true atall; just a little true; pretty much true; very much true).The T score from the Hyperactivity scale was used forthe calculation of the CNCBCL Index.

1The CNCBCL Index was used because some of thechildren in the study had been given the Child BehaviorChecklist (CBCL, Achenbach, 1991), while others hadbeen given the Conners’ Parent Rating Scales (Conners,1989, 1997). The CNCBCL Index was the child’s Tscore on either the Hyperactivity Scale of the Conners’Rating Scale, or the T score from the Attention Prob-lems Scale from the CBCL. Children were excludedfrom the control group if they had a T score greater than60 on the CNCBCL Index.



ADHD Rating ScaleThis is an 18-item scale completed by parents abouttheir child’s behavior over the past 6 months (DuPaul,1991; DuPaul et al., 1998). The scale items weredeveloped to directly assess symptoms of ADHD asdefined by DSM-IV. The items on this scale corresponddirectly with DSM-III–R (for the 1991 version) andDSM-IV (for the 1998 version) diagnostic criteria forADHD. Responses are coded on a 4-step Likert scalefrom ‘‘not at all’’ to ‘‘very much.’’ The normativesample for the 1998 version consisted of 2000 childrenselected to approximate the 1990 U.S. census data.Separate norms are available for boys and girls.

Diagnostic Interview for Children

and Adolescents (DICA–R)This is a semi-structured interview that is designed fordetermining selected current and retrospective psychi-atric diagnoses (Welner et al., 1987). Separate versionsexist for parents, adolescents, and children. The parentversion was administered to parents about their child.The modules administered in the current study includedthose assessing present and retrospective reports of:ADHD, Conduct Disorder, Oppositional Defiant Dis-order, Major Depressive Disorder, Bipolar Disorders,Dysthymic Disorder, Separation Anxiety Disorder,Panic Disorder, Generalized Anxiety Disorder, SpecificPhobia, Obsessive Compulsive Disorder and Adjust-ment Disorders.

IQ Measures

WISC–R and WISC-IIIMeasures of Full Scale IQ were obtained for allparticipants using the Wechsler Intelligence Scale forChildren – Revised (WISC–R; Wechsler, 1974) forchildren tested prior to 1996, or the WechslerIntelligence Scale for Children – Third Edition(WISC-III; Wechsler, 1991), for children tested 1996and later.

EF MeasuresThe neuropsychological measures were selected inorder to provide a representative sample of skills knownto comprise the EF construct, sampling both lan-guage, visuospatial, and motor domains, and includingmeasures of attention, working memory, planning,organized memory search, vigilance and responseinhibition, emphasizing measures shown previously tobe deficient in children with ADHD (e.g., CPT, WordFluency). The variables were also selected to highlightdifferent components of the EF construct (e.g., self-regulation of arousal, internalization of speech, andreconstitution) described in Barkley’s (1997a) model.

Five EF variables were selected for analysis on the basisof these characteristics, and are outlined below.

Rey Osterrieth Complex Figure (ROCF)The child is initially asked to copy a complex, hard-to-label figure, using five different colored pens presentedat regular intervals (Osterrieth, 1944; Rey, 1941).Incidental recall is requested immediately after thecopy presentation, and delayed recall is requested,without prompting, 15–20 min later. For the presentstudy, the productions were scored for organizationaccording to the Developmental Scoring System (DSS,Bernstein & Waber, 1996). The organization score isconsidered to tap the child’s appreciation of the figuralorganization of the design (Waber & Holmes, 1985),with scores ranging from 1 (poorly organized) to 13(highly organized). For the present study, the organiza-tion scores from the copy (ROCF–C) and immediaterecall (ROCF–IR) conditions were analyzed. The copycondition examines the extent to which the child usesan appropriate organization strategy in approaching thetask (e.g., emphasizing the planning component of EF),while the immediate recall score reflects the child’sincidental encoding of the motor code (i.e., emphasiz-ing spontaneous self-organization in learning, and to alesser extent, visual working memory). Kirkwood andcollegues found that children who performed poorlyon the ROCF Recall failed to spontaneously utilizethe organizing features of the design when encod-ing (Kirkwood, Weiler, Bernstein, Forbes, & Waber,2001). Raw scores for both conditions were used in theanalyses.

Tests of Variables of Attention – Visual

Test (TOVA–V)The TOVA–V is a continuous performance task thatuses two geometric designs, a target and a non-target,displayed on a computer monitor and requires a manualresponse in a go/no-go format (Greenberg, Leark,Dupuy, Corman, & Kindschi, 1996). It is designed as ameasure of sustained attention, inhibition and persis-tence. The test was normed on 775 children (377 boys,and 398 girls) between ages 6 and 16 (Greenberg &Waldman, 1993). In the present study, raw score totalsanalyzed for commission errors (COM) and for thevariability score (VAR). Commissions represent thefailure of the subject to inhibit a response for which adrive state has been established, and as such are ameasure of the self-regulation or inhibitory controlcomponent of EF. In contrast, variability represents ameasure of the subject’s response time variance orinconsistency, and is calculated as the standarddeviation of the mean correct response times. In thiscontext, variability is thought to measure the responsepreparation component of EF. According to Barkley



(1997a), individuals with deficits in internalization ofspeech demonstrate ‘‘greater variability in patterns ofresponding to laboratory tests, such as those involvingreaction time or continuous performance tests’’ (page82). Children with ADHD have been shown todemonstrate increased commission errors and vari-ability scores relative to controls (Harris et al., 1995).

Letter Word FluencyMeasures of verbal fluency are commonly used inclinical practice with children and adults, and arethought to represent behavioral demands involvingorganized memory search and sustained production(Benton, Hamsher, & Sivan, 1994). Verbal fluencymeasures are further divided into letter and semantictasks, and poor performance on letter fluency relative tosemantic fluency is considered evidence for executivedysfunction (Denckla, 1994). The child is asked toproduce as many words as possible beginning with aparticular letter within 1 min (for each letter). Levin et al.(1991) have shown significant increases in productivitywith age, which they attributed to frontal lobe matura-tion. Barkley (1997a) cited deficits in verbal behaviorand discourse in children with ADHD, including tests ofsimple verbal fluency. Using PET, Elfgren and Risberg(1998) found increased left frontal activation during aletter fluency task compared to bilateral frontal ac-tivation during a design fluency measure, suggestingdifferences in cortical areas engaged under the differenttask demands. The total correct score for the three trialsof Letter Word Fluency (LWF) was used for analysis.

RESULTS

The data were initially analyzed to determine

demographic group differences between the

ADHD group and the control group. One-way

analyses of variance (ANOVAs) were performed

for age, FSIQ, and the CNCBCL index. Chi-

square analyses were performed for sex distribu-

tion between the ADHD group and control

groups. There were no significant between-group

(ADHD vs. control) differences found for sex or

FSIQ. There were, however, significant between-

group differences found for age, F(1, 91)¼ 6.27,

p< .05, and for CNCBCL, F(1, 91)¼ 215.4,

p< .01. The between-group difference found on

the CNCBCL was expected (distinguishing the

groups as control subjects and children with

ADHD). Age and LD status were controlled

statistically in subsequent analyses.

Relationship Among EF Measures

The correlations among EF dependent measures,

age and FSIQ are listed in Table 2. The variables

were moderately correlated with one another, with

the largest correlations occurring from measures

drawn from the same test. The highest correlations

among EF measures occurred between TOVA

Commission (COM) and Variability (VAR) scores

(r¼ .77), and between the Rey Osterrieth Complex

Figure Copy (ROCF–C) and Immediate Recall

(ROCF–IR) scores (r¼ .58). The correlations

between FSIQ and EF measures ranged from .21

(ROCF–IR) to .49 (Letter Word Fluency-LWF).

Age was most strongly correlated with LWF.

Relationship Between IQ and EF

Performance

Levene’s test was used to compare error variances

between diagnostic groups in each of the five EF

Table 2. Correlations Among Executive Function Measures, FSIQ, and Age.

COM VAR LWF ROCF–C ROCF–IR FSIQ

Variability .77�

LWF �.35� �.47�

ROCF–C �.24 �.34� .48�

ROCF–IR �.17 �.26 .41� .58�

FSIQ �.32� �.35� .49� .34� .22Age �.23 �.30� .61� .46� .35� .05

Note. COM¼TOVA Total Commissions Raw Score; VAR¼TOVA Total Variability Raw Score; LWF¼LetterWord Fluency total number correct; ROCF–C¼Rey–Osterrieth Complex Figure, Copy ConditionOrganization Score (raw); ROCF–IR¼Rey–Osterrieth Complex Figure, Immediate Recall Condition(raw); FSIQ¼ Full Scale IQ score; age¼ age in years.�p< .01.



variables. None of the tests were statistically

significant, indicating appropriateness of the

distributions for parametric statistical tests. Hier-

archical multiple regression analyses were used

to examine the unique contribution of IQ to each

of the five EF measures, after controlling for

potentially confounding variables. In each of the

analyses, age was entered first, followed by sex,

presence of LD, ADHD versus control status, IQ

test version (WISC–R or WISC-III), and finally

FSIQ. The results of the analyses are outlined in

Table 3. For these analyses, a Bonferroni correc-

tion was used to control for multiple comparisons

(p¼ .01). After controlling for the aforemen-

tioned variables, FSIQ was uniquely and sig-

nificantly associated with performance on four of

the five EF measures, and approached signifi-

cance (p¼ .03) on the ROCF–IR trial. In contrast,

diagnostic group status (i.e., ADHD vs. control),

LD status, and IQ test version (WISC–R vs.

WISC-III) were not significant predictors of EF

performance on any of the five selected measures.

Table 3. Multiple Regression Analyses.

EF Measure Predictor Entered � R2 Change F Change

ROCF–Copy Age� .46 .21 23.69Sex .06 .00 .37LD Status .07 .00 .45ADHD vs. control .04 .00 .17WISC–R vs. WISC-III �.03 .00 .05FSIQ� .32 .10 11.84

ROCF–IR Age� .35 .13 12.91Sex .00 .00 .01LD Status �.04 .00 .16ADHD vs. control �.06 .00 .26WISC–R vs. WISC-III .08 .00 .39FSIQ .22 .05 4.64

Commissions Age �.23 .05 5.05Sex� �.35 .13 13.31LD Status �.07 .00 .44ADHD vs. control .09 .00 .73WISC–R vs. WISC-III .05 .00 .14FSIQ� �.29 .08 8.86

Variability Age� �.30 .09 9.14Sex �.24 .06 6.11LD Status �.06 .00 .29ADHD vs. control .07 .00 .44WISC–R vs. WISC-III .12 .01 .90FSIQ� �.34 .11 12.33

Word Fluency Age� .61 .38 53.94Sex� .22 .05 7.43LD Status �.02 .00 .07ADHD vs. control .08 .01 .82WISC–R vs. WISC-III .16 .02 2.62FSIQ� .45 .19 45.72

Note. Commissions¼TOVA Total Commissions Raw Score; Variability¼TOVA Total Variability Raw Score;Word Fluency¼Letter Word Fluency total number correct; ROCF–C¼Rey–Osterrieth Complex Figure,Copy Condition Organization Score (raw); ROCF–IR¼Rey–Osterrieth Complex Figure, Immediate RecallCondition (raw).�p< .01.



Group and IQ Comparisons

A 2 (group)� 3 (IQ group) multivariate analysis

of covariance (MANCOVA), covarying age, was

used to examine the potential interaction between

IQ and diagnostic group status on EF perfor-

mance. Three IQ groups were created: average

(85–109), high average (110–119) and superior

(120þ) to correspond to the conventions outlined

in the WISC manuals (Wechsler, 1974, 1991),

with the exception of including individuals with

FSIQ of 85–89 in the average IQ group. There

were a total of 41 children in the average IQ group

(22 ADHD, 19 controls), 19 children in the high

average IQ group (9 ADHD, 10 controls), and

32 children in the superior IQ group (20 ADHD,

12 controls). There was a significant multivar-

iate main effect (Pillai’s V) for IQ group,

F(10, 164)¼ 4.00, p< .01, with performance

improving with increased IQ level [effect size

(Cohen’s d)¼ .31]. There were also significant

univariate effects for IQ level on four of the five

selected EF measures: ROCF–C, F(2, 85)¼ 6.87,

p< .01, d¼ .57; ROCF–IR, F(2, 85)¼ 3.52,

p< .05, d¼ .41; LWF, F(2, 85)¼ 18.63, p< .01,

d¼ .94; and VAR, F(2, 85)¼ 2.00, p< .05,

d¼ .31, also indicating improved performance

with greater IQ. The main multivariate effect for

diagnostic group (ADHD vs. control) on the five

EF variables was not significant, F(5, 81)¼ .04,

p¼ .65, d¼ .09. There was, however, a significant

multivariate group by IQ-group interaction effect,

F(10, 164)¼ 2.47, p< .01, d¼ .25, reflecting

varying patterns of performance differences

between the ADHD and control groups, on the

selected EF variables, at different IQ levels. Only

VAR demonstrated a significant univariate inter-

action effect, F(2, 85)¼ 3.23, p< .05, d¼ .39.

To clarify the IQ-group by diagnostic group

interaction effect, separate analyses of covariance

(ANCOVAs) of the diagnostic group differences

(covarying for age) were made at each of the IQ

levels. These comparisons are outlined in Table 4.

At the average IQ level, there were significant

differences between children with ADHD and

Table 4. Comparisons Between ADHD and Control at Different IQ Levels.

EF Measure IQ Group ADHD Control Fa p d

ROCF-Copy 85–109b 5.41 (2.99) 6.00 (3.64) 1.53 .22 .40110–119c 5.67 (4.00) 6.20 (3.26) 0.40 .54 .31120þd 7.65 (2.66) 8.67 (3.45) 3.09 .09 .65

ROCF–IR 85–109 3.05 (2.65) 6.16 (4.55) 4.12 .05 .66110–119 2.44 (1.33) 5.10 (3.31) 2.58 .13 .80

120þ 7.15 (4.28) 5.58 (4.40) 0.22 .64 .17

Commissions 85–109 21.23 (19.21) 10.21 (10.05) 3.81 .05 .63110–119 12.33 (11.53) 11.10 (10.08) 0.02 .88 .07

120þ 9.35 (7.67) 9.83 (12.29) 0.09 .76 .11

Variability 85–109 348.59 (150.54) 226.52 (116.46) 6.80 .01 .85110–119 206.00 (69.56) 260.50 (142.97) 3.05 .10 .87

120þ 218.05 (116.49) 201.00 (146.99) 1.36 .25 .43

Word Fluency 85–109 16.05 (8.65) 21.95 (6.55) 0.79 .38 .29110–119 19.56 (8.40) 25.60 (12.08) 0.00 .96 .02

120þ 30.20 (10.57) 29.17 (9.93) 0.60 .44 .29

Note. ROCF–C¼Rey–Osterrieth Complex Figure, Copy Condition Organization Score (raw); ROCF–IR¼Rey–Osterrieth Complex Figure, Immediate Recall Condition (raw); Commissions¼TOVA Total CommissionsRaw Score; Variability¼TOVA Total Variability Raw Score; Word Fluency¼Letter Word Fluency totalnumber correct. Standard deviations are in ( ). Effect size d¼ (mean of control group�mean of ADHDgroup)/pooled standard deviation of two groups.aANCOVA with age as covariate within IQ groups. bn¼ 41 (22 ADHD, 19 control), ANCOVA df¼ 1,38.cn¼ 19 (9 ADHD, 10 control), ANCOVA df¼ 1,16. dn¼ 32 (20 ADHD, 12 control), ANCOVA df¼ 1,29.



controls on three of the five EF measures (ROCF–

IR, COM, and VAR). In contrast, there were no

significant differences between children with

ADHD and controls on any of the EF measures

at the high average or superior IQ group levels.

Because the failure to find significant group

differences at the high average and superior IQ

levels may have been a result of fewer children in

those groups (and thus lower statistical power),

we examined the effect sizes for comparisons at

each IQ level. Effect size is a standardized quan-

titative index that can represent the magnitude of

change that one variable produces in another

variable as reflected in the difference between

two means, independent of sample size (Cohen,

1988). Effect size values were computed using the

d statistic. Interpretation of the effect size d is

based on a convention suggested by Cohen, such

that 0.20 is considered a ‘‘small’’ effect size, 0.50

considered ‘‘medium,’’ and 0.80 or greater, a

‘‘large’’ effect size. The effect sizes for all com-

parisons are listed in Table 4. The mean effect size

for comparisons between children with ADHD

and controls on the five EF measures was .57 at

the average IQ level, .41 for the high average IQ

level, and .33 at the superior IQ level, suggesting

that the pattern of stronger effects at average

IQ was not due to sample size alone, and a

small effect size for comparisons of children at

high average and superior IQ levels on the EF

measures.

DISCUSSION

The purpose of the present experiment was to

clarify the utility of selected clinical measures of

EF in distinguishing children with ADHD from

controls at average or above IQ levels. We

specifically addressed the impact of age and IQ

on performance, while controlling the potential

effect of medications in this group by having

children take the tests while off medication.

Because we analyzed raw scores, we were able

to demonstrate that age was strongly related to all

five of the selected EF measures, for both ADHD

and control subjects. Across measures, children

performed better as they got older. This finding is

highly consistent with the previous findings of

Culbertson and Zillmer (1998), Grodzinsky and

Diamond (1992), and Levin et al. (1991). As

predicted, after controlling for the effects of age,

sex, LD and ADHD status, and IQ test version,

there was a significant relationship between IQ

and performance-based measures of EF. The

selected EF measures were strongly related to

IQ. In general, the performance of both ADHD

and control groups improved with higher IQ. The

most striking finding, however, was that IQ scores

accounted for a consistently greater proportion of

variance in the EF measures (uniquely accounting

for an average of 10% of the variance) than the

diagnosis of ADHD (accounting for an average of

0.4% of the variance). In fact, the diagnosis

variable (ADHD vs. control) did not make a

significant contribution to performance on any of

the five EF measures.

This finding strongly suggests that IQ is a

powerful moderator variable, particularly in

understanding the impact of ADHD and the

ability of affected children to compensate for

biological deficits. At average IQ, the negative

effects of ADHD are more salient, and the pre-

frontal component necessary for what Barkley

(1997a) described as ‘‘motor control and fluency’’

may be insufficient to meet the added demands.

At above average and superior IQ, however, the

prefrontal component may be more intact, sug-

gesting that the source of the behavioral dysfunc-

tion described by parents may be in other brain

components (Teeter & Semrud-Clikeman, 1995).

The issue of subcortical mechanisms involved

in EF has been previously described (Denckla &

Reiss, 1997), and may potentially be relevant to

the IQ issue observed in our findings. Based on

evidence involving the frequency of motor signs

referable to the basal ganglia (e.g., tics, chorei-

form movements) and cognitive slowing (e.g.,

choice reaction times) in above average IQ chil-

dren with ADHD and/or Tourette Syndrome, the

authors argued that developmental anomalies in

pathways to the basal ganglia may account for the

some of the EF deficits, and may explain the

sizable number of children who continue to func-

tion well in life despite their disorders. Indeed, in

our high IQ individuals with ADHD, the possibil-

ity of ‘‘overgrowing’’ (i.e., the cortex maturating

to dominate subcortical deficits) may in fact be



the mechanism by which these children perform

well on the EF measures, and possibly ‘‘outgrow’’

the primary functional deficits of their disorders

later in life. This hypothesis still needs to be

corroborated with data from imaging studies.

The relationship between EF and IQ may also

reside in the test-taking demands with which

neuropsychological assessment presents the

child with ADHD. The neuropsychologist’s chal-

lenge is to relate any test to real life. Of course,

there may be a certain circularity in this situation;

those who have the ADHD diagnosis but less EF

deficit may be better able to cope with the task

demands of IQ testing, and thus score relatively

well. Similarly, the highly-structured clinical test-

ing setting may not place a high enough demand

on EF, because of the external constraints and

supports necessarily imposed on the child by the

examiner (Bernstein & Waber, 1990).

Taken together, these findings support the idea

that EF, as measured by clinically available neu-

ropsychological tests, improves over the course of

childhood. Importantly, however, our findings

provide little support for the use of perfor-

mance-based EF measures alone to discriminate

children with ADHD from controls, especially if

those children have above average IQ. Our results

are consistent with a growing literature that finds

a high degree of variability on tests of EF and

attention among children with ADHD, and thus

limits their efficiency in making diagnostic

discriminations (Doyle, Biederman, Seidman,

Weber, & Faraone, 2000). The moderating role

of IQ is also clearly relevant in understanding

children’s performance on clinical measures of

EF. Our findings corroborate Denckla’s (1994)

assertion that EF should be considered relative to

overall ability level, and not in isolation. Our

findings indicate that this assertion has the great-

est validity around the average IQ level, while

children with higher IQ may be able to perform

normally on clinical measures of EF.

While this study was developed as a step

toward understanding the influence of IQ on

clinical EF measures, several aspects of the

study limit interpretation of the findings. First,

the literature about validity of commonly used EF

measures in children with above average IQ is not

well established. In particular, the TOVA was

developed using a normative sample with average

intelligence, and prior research has cited lower

correlations between IQ and TOVA among con-

trols than we found in our sample (Stein et al.,

1994). This difference may be because the test

norms presume intellectual functioning in the

mid-range of the distribution, and poorer group

discrimination at the higher IQ levels may be, in

part, due to ceiling effects – especially for the

commission errors (i.e., one can do no better than

zero commissions). These issues were minimized

in the present study by using raw scores (rather

than deviation based standard scores); neverthe-

less, the distribution of scores and the con-

struct validity of the TOVA and other measures

at the higher IQ range needs to be established

empirically.

A second potentially confounding issue is the

choice of measures used to operationalize EF. We

selected measures from among a variety of instru-

ments commonly used in clinical practice, based

in part on prior literature, and in an attempt to

characterize diverse and theoretically-relevant

components of the construct. As defined, the

constructs appear to assess distinct skills, partic-

ularly as output is measured in different

modalities. In our study, however, they were

significantly correlated with one another, suggest-

ing that they share a good deal of common

variance. Despite the different modalities of

response (graphomotor output, speaking, clicking

a button), the EF measures chosen in the present

study all correlate significantly with one another,

and likely limit multivariate discriminatory

power. Clearly, there are other measures that can

assess similar constructs (e.g., naming interfer-

ence, card sorting, and tower tests). Similarly, in

clinical practice, performance-based neuropsy-

chological measures of EF can be supplemented

with parent/teacher interviews (e.g., Vineland

Scales) or caregiver rating scales geared toward

measurement of EF (i.e., Behavior Rating Inven-

tory of Executive Function – BRIEF; Gioia,

Isquith, Guy, & Kenworthy, 2000) in order to

obtain a more ecologically valid set of conclu-

sions about the daily function of the child. Instru-

ments with wider variation in responses and age

ranges may produce more robust group differ-

ences, and future research should seek to choose



measures based on known intercorrelations with

one another.

Several issues which affect interpretation of

data were not able to be addressed in the present

investigation, and will be important in future

research. First, children with both inattentive

and hyperactive/impulsive varieties of ADHD

were included. Given the interaction between

ADHD type and sex (Weiler, Bellinger, Marmor,

Rancier, & Waber, 1999), this relationship may

have influenced our findings, particularly as there

were moderate sex differences on three of the five

EF measures. It will also be important for future

research to address the differences in the relation-

ship between IQ and EF test performance among

children with different types of ADHD, with

particular emphasis on the developmental course

of these patterns. Secondly, more than half (i.e.,

51 of 92) of our school-aged sample had FSIQs in

the high average range or above. The high number

of children in our sample with above average IQ

may be an artifact of our stringent entry/exclusion

criteria. Because our sample of children with

ADHD was relatively ‘‘pure’’ diagnostically,

interpretation of these findings in populations

with greater comorbidities should be made with

caution. Also, in high functioning children, such

as many of those in our sample, the structure

inherent in the testing situation may allow them to

perform normally on laboratory tests, even though

they may continue to struggle in the classroom

setting, and when independently doing home-

work. In contrast, in the preschool years, they

are more susceptible to the regulatory deficits

inherent in ADHD. Third, we used an IQ-achieve-

ment discrepancy to define (and eliminate var-

iance attributable to) LD in our sample. A number

of researchers have challenged the validity of this

discrepancy model (Fletcher, 1998; Francis,

Shaywitz, Stuebing, Shaywitz, & Fletcher,

1996), citing similarities in core phonological

processing skills between ‘‘garden variety’’ poor

readers and those whose reading is discrepant

from IQ. We used the discrepancy model in

order to be conservative in our analyses, espe-

cially given the high IQ level of many of our

participants. Indeed, using different criteria, our

sample can be considered relatively free from LD.

In our sample, only 7 children had reading and

only 2 children had math composite scores below

the 20th percentile. Future research examining the

relationship among IQ, EF, and the basic pro-

cesses fundamental to reading and math (e.g.,

phonological processing, rapid naming, automa-

ticity of calculation) may clarify whether those

process account for more variance than the dis-

crepancy model.

In conclusion, future research addressing the

link between neuropsychological test results and

‘‘real-world’’ difficulties among children with

ADHD or other disorders involving executive

dysfunction (e.g., learning disabilities, Tourette

Syndrome, high functioning Autism, Obsessive

Compulsive Disorder, Stereotypic Movement

Disorder) continues to be of importance, espe-

cially in children with otherwise intact intel-

lectual functioning. An emphasis on research

involving the interactions between age, sex, IQ

and presumed brain dysfunction will be crucial in

understanding the developmental impact of these

disorders.

ACKNOWLEDGEMENTS

A portion of this research was presented at the 18thannual meeting of the National Academy of Neuro-psychology in Washington, D.C., November 4–7, 1998.This research was supported by Grant NS-35359,Neurodevelopmental Pathways to Learning Disabil-ities, and by the Mental Retardation and Devel-opmental Disabilities Research Center, GrantHD-24061.

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