Traumatic Brain Injury in Children and Adolescents: An Evaluation of the WISC-III...

TRAUMATIC BRAIN INJURY IN CHILDREN AND ADOLESCENTS:

AN EVALUATION OF THE WISC-III FOUR FACTOR MODEL

AND INDIVIDUAL CLUSTER PROFILES

Micheal E. Shafer, M.S.

Dissertation Prepared for the Degree of

DOCTOR OF PHILOSOPHY

UNIVERSITY OF NORTH TEXAS

August 2008

APPROVED: Craig S. Neumann, Major Professor Randall J. Cox, Committee Member Joan W. Mayfield, Committee Member D. Shane Koch, Committee Member Kenneth Sewell, Committee Member and Program

Coordinator Linda Marshall, Chair of the Department of

Psychology Sandra L. Terrell, Dean of the Robert B. Toulouse

School of Graduate Studies

Shafer, Micheal E., Traumatic Brain Injury in Children and Adolescents: An Evaluation

of the WISC-III Four Factor Model and Individual Cluster Profiles. Doctor of Philosophy

(Clinical Psychology), August 2008, 154 pp., 30 tables, 6 figures, references, 39 titles.

Traumatic brain injury (TBI) is the leading cause of death and disability among children

and adolescents in the US. Children and adolescents who sustain moderate and severe head

injuries are much more likely to evidence significant deficits in neuropsychological functioning

when compared with children with mild head injuries. Information about the recovery process

and functional sequelae associated with moderate and severe head injuries remains limited,

despite clear indications that children who experience such injuries typically exhibit notable

deficits in intellectual functioning, particularly during the acute phase of recovery. Thus, the

present study was conducted to augment research on intellectual functioning in children with

moderate or severe head injuries. To accomplish this, the study first examined the proposed

factor model of the WISC-III in children with moderate and severe TBI. Given high prevalence

rates and similar trends in cognitive impairment, particularly within the frontal lobe structures

(e.g., disrupted cognitive flexibility and divided attention), the study also examined this same

factor model for a group of children with attention-deficit/hyperactivity disorder (ADHD) and

compared it with the model fit from the TBI group. In the second phase of the study, both the

TBI and AHDH groups were evaluated to determine if distinct WISC-III index score cluster

profiles could be identified. Lastly, the cluster groups for both the TBI and ADHD samples were

validated using important demographic and clinical variables, as well as scores from independent

neuropsychological measures of attention, executive functioning, and working memory. Parent

reports of psychological and behavioral functioning were also used in an attempt to further

distinguish the cluster groups. Study limitations and future research implications were also

discussed.

Copyright 2008

by

Micheal E. Shafer

ii

TABLE OF CONTENTS

Page

LIST OF TABLES........................................................................................................................ v LIST OF FIGURES ....................................................................................................................vii Chapters

1. LITERATURE REVIEW ..................................................................................... 1

History of Clinical Neuropsychology

Neuropsychological Assessment

Epidemiological Rates of Neuropsychological Dysfunction

Neuropsychological Functioning and Traumatic Brain Injury

Intellectual Functioning following Traumatic Brain Injury

WISC-III Performance Patterns

The WISC-III Factor Structure with Traumatic Brain Injured Children

WISC-III Factor Index Cluster Analyses

Additional Validating Variables for Cluster Subtype Patterns

Age of Onset

Injury Severity

Time Since Injury

Specific Cognitive Processes and IQ

Executive Functioning

Attention

Working Memory

Study Parameters 2. METHOD ........................................................................................................... 37

Participants

Procedures

Measures

Data Analyses 3. RESULTS ........................................................................................................... 49

Preliminary Analyses

iii

Confirmatory Factor Analysis

Description of WISC-III Performances by Group

TBI-ADHD Group Comparisons

Additional WISC-III TBI & ADHD Sample Comparisons

Multivariate Analysis of the WISC-III

Additional TBI-ADHD Group Comparisons

Cluster Analysis

Analysis of Variance by Cluster Groups (TBI Sample)

Cluster Validation with Clinical Variables (TBI Sample)

Validation w/ Ext. Neuropsychological & Psychological Measures (TBI Sample)

4. DISCUSSION ..................................................................................................... 84


Intellectual Functioning Post TBI

Psychological and Behavioral Functioning

Identifying Cluster Profile

Validating the Clustering Process

Clinical Implications

Study Limitations and Future Research REFERENCES ......................................................................................................................... 138

iv

LIST OF TABLES

Page

1. A Breakdown of Ethnicity for the Total, TBI, and ADHD Samples............................ 102

2. Mean and Standard Deviation BASC Scores for the TBI & ADHD Groups ............... 103

3. BASC, Demographic, and Clinical Variable Correlations for TBI Sample ................. 104

4. BASC Scales and Age Correlations for ADHD Sample .............................................. 105

5. Goodness of Fit Indices for the 4 and 3 Factor Models................................................ 106

6. Factor Loadings for WISC-III 4 Factor and 3 Factor Models ...................................... 107

7. TBI and ADHD WISC-III Factor Mean & Standard Deviation Scores ....................... 108

8. WISC-III Factor Index Correlation Coefficients .......................................................... 109

9. WISC-III Subtests and Indice Correlations .................................................................. 110

10. WISC-III Factor Index Correlations w/ Glasgow, Age, and Length of Coma ............. 111

11. WISC-III Correlation Matrix for TBI Sample .............................................................. 112

12. WISC-III Reliability Coefficients for the TBI and ADHD Groups.............................. 113

13. WISC-III Correlation Matrix for ADHD Sample......................................................... 114

14. WISC-III and BASC Scale Correlations (TBI Sample) ............................................... 115

15. WISC-III and BASC Scale Correlations (ADHD Sample) .......................................... 116

16. WISC-III and External Validation Variable Correlation Matrix (TBI Sample) ........... 117

17. WISC-III and External Validation Variable Correlation Matrix (ADHD Sample) ...... 118

18. TBI and ADHD WISC-III Factor Mean and Std. Deviation Scores............................. 119

19. Hierarchical Cluster Solutions for the TBI Sample ...................................................... 120

20. Male, Female, and Total Factor Index Scores and Std. Deviation of k-means 3 Cluster Solution-TBI Sample .................................................................................................... 121

21. WISC-III Factor Index Correlation Coefficients for the TBI Clusters ......................... 122

22. WISC-III and External Validation Variables by Clusters Correlation Matrix (TBI Sample) (Cluster 1) ....................................................................................................... 123

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25. Hierarchical Cluster Solutions for the ADHD Sample ................................................. 126

26. Factor Index Scores and Standard Deviations of Two WISC-III Cluster Subtypes for the ADHD Sample (k-means)............................................................................................. 127

27. Mean and Standard Deviation Scores for the External Validation Variables (TBI 3 Cluster Solution) ........................................................................................................... 128

28. Mean and Standard Deviation Scores for the External Validation Variables (ADHD 2 Cluster Solution) ........................................................................................................... 129

29. WISC-III and External Validation Variables by Cluster Correlation Matrix (ADHD Sample) (Cluster 1) ....................................................................................................... 130

30. WISC-III and External Validation Variables by Cluster Correlation Matrix (ADHD Sample) (Cluster 2) ....................................................................................................... 131

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vii

LIST OF FIGURES

Page

1. WISC-III Four Factor Model (TBI Sample)................................................................. 132

2. WISC-III Four Factor Model (ADHD Sample)............................................................ 133

3. WISC-III k-means Cluster Analysis for the TBI Sample ............................................. 134

4. WISC-III k-means Cluster Analysis for the ADHD-2 Cluster Solution....................... 135

5. K-means Cluster Analysis for the ADHD-3 Cluster Solution ...................................... 136

6. Cluster Comparison from the Current and Donders & Warschausky Studies.............. 137

1

CHAPTER 1

LITERATURE REVIEW

History of Clinical Neuropsychology

Neuropsychology as a professional field of scientific study has existed (to some degree)

since the late seventeenth century, although it has gained considerable appreciation over the last

three decades (Golden, 1981; Hartlage & Long, 1997; Horton, Wedding, Webster, 1997, Synder

& Nussbaum, 1998). Initially, neuropsychology was defined as the "scientific study of brain-

behavior relationships" (Meier, p. 289, 1974); however, subsequent definitions have been

expanded to recognize the scientific study of brain-behavior relationships in relation to clinical

problems such as loss of vision, depression, or ataxia (see Horton, Wedding, Webster, 1981).

Hitherto, clinical neuropsychology has been strongly influenced by advancements in

biology and medicine, particularly studies on the architectural features of brain systems (Horton,

Wedding, & Webster, 1997). Willis and colleagues (1664) were among the first to provide

empirical observations of the brain and supporting nervous systems (Walsh & Darby, 1999). In

their seminal manuscript first published in 1664 (see Cerebri Anatome), Willis and colleagues

(1965) provided detailed accounts of complex cortical and vascular systems. Interestingly, many

of the terms (e.g., neurology) and structures (e.g., Circle of Willis, Cranial Nerves) delineated in

the manuscript have remained salient in the field of neuropsychology (Horton et al., 1999).

Although such observations were met with acclaim and support, advancements in brain study

(and by default neuropsychology) progressed slowly throughout the 17th and 18

th centuries.

Joseph Franz Gall was among the earliest scientists to attempt to conduct scientific (i.e.,

replicable) studies on brain-behavior relationships. Specifically, Gall attempted to identify

empirical correlations between separate mental functions (psychological functioning) and

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localized regions of the brain (e.g., parietal lobe) (see Zawidzki & Bechitel, 2005 for a complete

discussion). Gall's emphasis on the importance of cortical structures was landmark (Walsh &

Darby, 1999) and while it enjoyed limited support, it served as an important impetus in the early

study of functional and structural brain correlates by fueling the ideological discourse between

localization and holistic brain theorists. Contemporaries who supported a holistic theoretical

position (e.g., Karl Lashley and equipotentiality) proposed that most cognitive functions (e.g.,

language) were not localized to one region of the brain, but rather, worked as an integrated

system (Walsh & Darby, 1999). While not disregarded, this position was disputed more than

five decades later when Broca (1865) provided empirical support for localization theorists by

identifying motor speech deficits in the left posterior frontal lobe region (inferior frontal gyrus)

(Kolb & Winshaw, 2003). Approximately 15 years later, Wernicke (1874/1977) augmented this

body of research by providing empirical evidence for the localization of receptive language

centers in the posterior temporal lobe region. In more recent years there has been steady

advancement in the study of structural and functional neuropsychology, with strong evidence for

the localization of visuoperceptual processing systems, visual memory, and non-verbal reasoning

centers to the right hemisphere of the brain while language processing and verbal memory

systems have been largely localized to the left hemisphere (Reitan & Davison, 1974; Reitan &

Wolfson, 1985; Semrud-Clikeman, 2001). Despite mounting evidence, the debate among

holistic and localization theorists has continued.

Contemporary neuropsychology has undergone other considerable changes since its

modern inception; none larger than noted advancements in neuroimaging technology (e.g.,

positron emission tomography (PET), magnetic resonance imaging (MRI), and functional

magnetic resonance imaging (fMRI) (Kolb & Whishaw, 2003, Raichle, 2001, Darby & Walsh,

3

1999). Today, such advancements are regularly used to augment the study of localized brain-

behavior disorders (e.g., aphasia) and direct treatment practices. For the first time in the history

of neuroscience, the concurrent study of behavior and brain activity (i.e., cellular activity) can be

conducted via computer evaluations of processes such as blood flow and the oxidative

metabolism of glucose (Raichle, 2001), rendering some of the neuropsychological assessment

issues of the past (i.e., detection of organicity) irrelevant (Bigler, Lowry, & Porter, 1997). Gale

and colleagues (1994), for example, investigated a cohort of adults with traumatic brain injuries

to evaluate the relationship between specific structural disruptions and functional (behavioral)

outcomes. Results found evidence for increased cognitive dysfunction among individuals who

demonstrated greater temporal horn volume (i.e., cortical atrophy). Findings like these have

helped focus treatment practices while bolstering the exploration of correlates between structural

deficits and neuropsychological findings.

Despite the novelty and ostensible utility of neuroimaging, it has generally remained a

largely unexplained technology (Raichle, 2001) with notable limitations. One of the primary

concerns among clinicians and researchers alike is the absence of replicable one to one

correlations between observed structural damage in the brain and behavioral outcomes.

Regardless of the scanning method (e.g., MRI, fMRI, PET scan) most neuroimaging studies of

task analysis produce variable findings that implicate multiple cortical and sub-cortical systems

and fail to consistently isolate a single cognitive process such as verbal memory from person to

person. Generalizing research findings from neuroimaging studies is confounded by a number of

other variables. For example, Price and Friston (2001) point out that post-injury processes such

as neuronal reorganization are inevitably influenced by the presence (or absence) of pre-morbid

neuronal connections, which is affected by factors such as age, gender, medical history, and

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history of trauma. Despite all of these concerns, the science behind this technology has clearly

intrigued most neuropsychologists (Raichle, 2001) and it will be important for researchers to

continue to purse this field of study.

Neuropsychological Assessment

While advancements in technology have been promising, neuropsychological assessment

has remained one of the most important aspects in the clinical evaluation of brain-behavior

relationships and cerebral dysfunction. In addition to the identification of structural and

functional deficits, neuropsychological assessment has also been shown to be an invaluable tool

for treatment planning and treatment evaluation (Lezak, 2005). Moreover, numerous

neuropsychological studies have demonstrated how effective particular instruments can be at

identifying impairment that is often "too subtle to be detected by many neurological procedures

(e.g., Brain CT scan) that depend on the detection of structural alterations in the brain" (Brady &

Walsh, p. 446, 1999).

As an objective tool (Russell, 2000), neuropsychological evaluations have traditionally

been conducted using a "fixed-battery" approach (Horton, 1997). To date, the most commonly

administered fixed-battery is the Halstead-Reitan Neuropsychological Test Battery (1947).

Considerable support has been generated for instruments such as the Halstead-Reitan,

particularly for their "straightforward administration, scoring, and interpretation" (Kolb &

Whisham, p. 533, 1999). Designed to enhance reliability and validity, fixed-batteries were

intended to be administered to all patients, regardless of the presenting pathology (e.g., traumatic

brain injury, seizure disorder, vascular dementia). In a landmark case (Chapple v. Ganger, 1994)

the Daubert standard was applied for the very first time to the use of fixed neuropsychological

batteries in federal court. In this case, the court gave greater weight to the results obtained from

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a fixed-battery than those obtained from flexible neuropsychological test batteries (Reed, 1996).

Despite widespread support, changes influenced by factors such as treatment reimbursement

policies have forced neuropsychologists to strongly consider the practicality of a "flexible-

battery" approach.

Described as a hypothesis driven approach to testing (Kolb & Whishaw, 1999), flexible-

batteries tend to utilize a combination of empirically validated neuropsychological tests to

clinically evaluate specific neurological and behavioral symptoms (Goldstein, 1996). Testing for

specific impairments allows the clinician to identify specific levels of impairment within a

shorter period of time, which is advantageous to the patient and families as well as appreciated

by insurance companies. The forensic superiority of the fixed-battery has also been called into

question recently. In a California case (Kelly/Frey, 1998) the court allowed for expert testimony

based on the findings from a flexible-neuropsychological battery to be entered to into evidence

and considered as expert testimony, despite criticisms (Mckinzey & Ziegler, 1999).

Regardless of the ongoing discourse about the incremental validity of burgeoning

technologies such as fMRI, it is clear that the neuropsychological assessment of brain

dysfunction has demonstrated particular clinical utility over the last two decades, particularly in

the field of traumatic brain injury for children and adolescents. Most notably, such empirically

driven practices have greatly assisted with diagnostic specificity of structural and functional

damage post head injury, impacted treatments practices, and facilitated research efforts in

neuropsychology.

Epidemiological Rates of Neuropsychological Dysfunction

Traumatic brain injury (TBI) is currently the leading cause of death and disability among

children and adolescents in the United States (Chapman, McKinnon, Levin, Song, Meirer, &

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Chiu, 1997; Kraus, 1995; Kraus, Rock, & Hemyari, 1990; Rodriguez & Brown, 1990; Semrud-

Clikeman, 2001). Most research indicates at least one million children and adolescents

experience a closed head injury each year (CDC, 2000; Lehr, 1990; Teeter & Semrud-Clikeman,

1997), with epidemiological studies showing adolescents and young adults to be a particularly

high risk (CDC, 1990; CDC, 1998; CDC, 2000 Fletcher et al., 1999; Waxweiler, 1995). Studies

suggest that approximately four out of every one hundred boys and two out of every one hundred

girls will have sustained a head injury by the time they turn sixteen years of age (Annegers,

1983). In each school district, a small proportion of the children who sustained a TBI

(20/10,000) will require substantial special educational resources as a result (Arroyos-Jurado et

al., 2000).

Among the one million children who sustain a head injury each year, over half will be

admitted to the emergency department as a result (Guerrero, Thurman, Sniezek, 2000). Of this

group, more than one-third will die from TBI related complications (Michaud, Rivara, Grady, &

Reay, 1992; Thurman, Alverson, Dunn, Guerrero, & Sniezek, 1999), with mortality rates (50%)

highest among children ages 1 to 15 (Fletcher et al., 1995).

While minor brain injuries actually comprise the most commonly diagnosed head injury

in the U.S. (Levin, Eisenberg, & Benton, 1989) and severe head injuries only account for 10% of

all injuries (Sorenson & Kraus, 1991), the long-term effects associated with moderate and severe

head trauma (i.e., mortality rates, morbidity, and monetary costs) are more distressing. Schalen

and colleagues (1994) noted, for example, that approximately fifty percent of all TBI related

deaths occur within the acute post-injury phase of recovery within this cohort. For those with a

moderate or severe TBI who survive the initial phases of recovery, many are likely to require

extensive hospitalization and long-term care-giving. Gray (2000) has warned that the increased

7

reliance on acute facilities and rehabilitation services will likely reach epidemic proportions over

the next two decades. The impact on the families of head-injured victims can also not be

overstated (McGregor & Pentland, 1997). Max and colleagues (1991) noted that costs associated

with the provision of direct and indirect services for TBI patients neared 40 billion dollars in

1985. Alarmingly, this rate has grown precipitously over the twenty five years. Conservative

estimates project costs for direct and indirect services may grow to 50 or 60 billion dollars

annually (Thurman, 2001). For individuals and families, government reports indicate that

lifetime care for individuals who have sustained a severe traumatic brain injury range from

$600,000 to 9 million dollars per person (NIH, 1998; Papastrat, 1992). These statistics are even

more alarming for younger children for whom the subsequent risk of academic failure is high

(Arroyos-Jurado et al., 2000; Dennis, 2000; Jaffe, Plissar, Fay, & Liao, 1995). Research has

shown that children who sustained moderate and severe traumatic brain injuries are very likely to

exhibit persistent and complicated medical and cognitive problems throughout their lives (Klein,

Houx, & Jolles, 1996) which ultimately impedes quality of life (Cattelani, Lombardi, Brianti, &

Mazzucchi, 1998).

Neuropsychological Functioning and Traumatic Brain Injury

Children and adolescents who sustain severe and moderate head injuries are much more

likely to experience significant deficits in neuropsychological functioning than children who

sustain mild head injuries (Donders & Warchausky, 1997; Green, Foster, Morris, Muir, &

Morris, 1998; Semrud-Clikeman, 2001). In particular, research has consistently shown that the

frontal and temporal lobe structures typically sustain the most focal damage following a severe

head injury, although functional deficits are rarely restricted to these particular regions and loss

of functioning can be observed across several systems, including memory, visual-spatial

8

(perceptual organization), motor, and language abilities (see Ratey, 2001; Schwartz & Begley,

2002). The aggregate of these findings raises interesting questions pertaining to the loss of

specific cognitive processes, as well as broad cognitive systems, post head injury, given the

inter-relations of various brain regions.

A review of the literature shows that the development, structure, and decline of critical

cognitive processes such as memory has been widely studied in children, adolescents, adults, and

elderly populations (e.g., Damasio, 1994; Freud, 1912; Piaget, 1969; Schacter, 1996). In

particular, research has examined various aspects of memory (e.g., long-term memory, short-

term or working memory, subjective memory, explicit memory, implicit memory, episodic and

semantic memory) among a number of clinical pathologies (e.g., depression, schizophrenia,

attention-deficit problems) including traumatic brain injury (see Delis, Kramer, Kaplan, & Ober,

1994; Dennis, Roncardin, Barnes, Guger, & Archibald, 2000; McDowell, Whyte, & D'Esposito,

1997). With regard to TBI, a majority of the studies have focused on persistent deficits in verbal

memory which are often evidenced post-injury (e.g., Roman et al., 1998). According to Levin

and colleagues (1982), problems with verbal memory can be so pervasive following a head

injury that they are readily observable a full year after the initial trauma. Findings suggest

memory deficits are typically most problematic for younger children because of the ostensible

relationship between these abilities and impaired learning in school-aged children (Catroppa &

Anderson, 2002; Semrud-Clikeman, 2001).

Visuoperceptual processing deficits and problems with perceptual organization are also

commonly sited problems in children with head injuries. Bawden and colleagues (1985) have

shown a direct correlation between visual perceptual organization disruption and injury severity.

Semrud-Clikeman (2001) indicates that children with head injuries often demonstrated difficulty

9

copying complex figures (e.g., Rey Complex Figure Test) and integrating forms and figures.

Disruptions within this domain appear to be particularly prevalent among children under twelve

years of age (Semrud-Clikeman, 2001).

Processing speed is another critical cognitive process that is affected by TBI. Bowden et

al., (1985) and others have repeatedly demonstrated that children with severe head injuries are

impaired compared to healthy children on timed neuropsychological tests. Chaplin, Deitz, and

Jaffe (1993) for example, drew similar conclusions after studying a group of children with mild,

moderate, and severe head injuries 16 months post-injury. When compared to healthy age-

matched controls, Chaplin and colleagues found that the TBI group performed poorer on

measures of gross motor function and timed tasks. Interestingly, fine motor tasks not involving a

timed response did not differentiate the groups. This is of particular concern given the fact that

most performance factors on tests assessing intellectual functioning are laden with timed tasks

for which a "good" performance is typically the aggregate of completion time and response

accuracy. As a result, more research on the role of processing speed post head injury is

necessary to better understand the scope of deficits which are typically exhibited on performance

scale subtests in children with head injuries. One way to address this topic is to examine the

relationship of factor index scores of children with severe head injuries compared to other

pediatric groups (e.g., ADHD) on neuropsychological measures of visual-spatial ability and

processing speed, as well as comparison with expected norms.

One of the most prevalent findings in brain injury research is the preservation of language

skills, in comparison to perceptual organization and processing speeds in children post-injury;

although, among children who present with acquired aphasias during childhood (for a discussion

about acquired language disorders see Trudeau et al., 2000) traumatic brain injury is the most

10

common etiology (Murdoch, 1990). According to Dennis (1989), functional language deficits

among children with head injuries are commonly presented as slowed speech, poor sequential

processing, or word finding difficulties, and may result from either diffuse or focalized damage

(Jordan & Ashton, 1996). Despite the widespread interest in preserved language ability post-

injury, research has not fully identified the factors that contribute to preserved functioning within

this population (Anderson et al., 2001). This is in part because studies generally indicate that

children recover a majority of their pre-morbid verbal functioning after a year (see for Anderson

et al., 1997; Chadwick et al., 1981; Taylor et al., 1995). Due to the inherent complexities of the

relationship between acquired aphasia (receptive and expressive language deficits) and cognitive

impairment (e.g., processing speed) following a head injury, more research is needed to identify

common patterns of functioning in this population, particularly among younger children for

whom fewer studies have been conducted (Trudeau et al., 2000).

There has already been some effort to identify profiles of neuropsychological functioning

that goes beyond that which is already well documented (e.g., children with severe head injuries

typically have low average IQs, particularly within 6 months post-injury, and that the Verbal IQ

is generally less affected than Nonverbal IQ). Identification of such within group differences

(i.e., clusters) may further help explain why one child is able to return to pre-morbid levels of

functioning after a sustaining a head injury while another child (with similarly post-injury

deficits) fails to rebound from their injury. Nonetheless, additional research is needed to

delineate how inter-related domains of intellectual functioning verbal comprehension, perceptual

organization, working memory, and processing speed covary following a moderate or severe

head injury and to what extent specific underlying processes such as attention and executive

functioning might be able to explain changes in higher-order intellectual functions. In this way,

11

such research may help clinicians develop empirically supported treatment programs which

accurately facilitate improvement in intellectual functioning following a moderate or severe head

injury.

Intellectual Functioning following Traumatic Brain Injury

Information about the recovery process following a moderate or severe head injury

remains limited (Anderson, Northam, Hendy, & Wrennal, 2001) despite clear indications that

children who experience such injuries typically show significant deficits in intellectual

functioning, particularly during the acute phase of recovery (Donders, 1997; Donders &

Warschausky, 1997; Ewing-Cobbs and Fletcher, 1990; Fletcher et al., 1995; Lezak, 1994; Rutter,

Chaewick, Shaffer, & Brown, 1980; Slate & Kohr, 1989). As a whole, findings from early TBI

studies were ambiguous and suggested that identifying significant differences based on injury

severity was difficult (e.g., Levin, 1995; Levin, Eisenberg, Wigg, & Kobayashi, 1982). More

recently, this position has been reversed with repeated demonstration that children with moderate

or severe head injuries exhibit greater deficits on subtests from the Wechsler intelligence scales,

when compared with groups of children with mild head injuries (Chadwick, Rutter, Brown,

Shaffer, & Traub, 1981; Dalby & Obrzut, 1991; Fletcher, Levin, & Butler, 1995; Goldstein &

Levin, 1987; Knights et al., 1991). Tremont, Mittenberg, and Miller (1999) compared WISC-III

scores from a group of 30 head injured children and a group of 30 orthopedic injured children

with no history of head trauma. Results from this study showed that children without brain

injuries received higher scores within each of the four WISC-III IQ (Verbal Comprehension,

Perceptual Organization, Working Memory, and Processing Speed) domains than the children

with head injuries. For the children with head injuries, the most notable factor discrepancies

occurred among performance-based subtest scores, with processing speed showing the greatest

12

sensitivity to injury. Within group analyses further showed that PIQ scores, regardless of severity

of injury, were lower for children with head injuries than VIQ scores.

Although this pattern has been widely demonstrated, some investigators have questioned

the accuracy of this conclusion, arguing evidence for the VIQ/PIQ split at one year post-injury is

less conclusive (e.g., Hawkins et al., 2002). According to Hawkins and colleagues, many of the

theoretical positions on brain recovery processes are based on studies that were conducted during

the acute recovery phase, in spite of the clear evidence that 85% of recovery occurs between

twelve and eighteen months post-injury (Anderson et al., 2001). Hawkins and colleagues

reviewed results from a series of adult TBI studies and found less evidence of the VIQ-PIQ

discrepancy at one year post-injury than previously reported. In another well cited study,

Chadwick and colleagues (1981) evaluated intellectual functioning in a large group of children

and adolescents on two separate occasions: immediately post-injury, and again at one year after

the accident. Findings showed that the 30 point discrepancy which was observed between the

VIQ and PIQ scores immediately following the injury was almost negligible at one year post

injury. Some researchers (e.g., Ryan et al., 1996) have gone as far as to argue that when base

rates are appropriately considered, a significant discrepancy between verbal and non-verbal IQ is

generally not observable, even among individuals with known structural compromise. While this

issue remains a focal point for discussion, the simple comparison of the VIQ/PIQ scores "may

underestimate the complexity of the cognitive issues involved in children with TBI" (Donders, p.

431, 1993).

WISC-III Performance Patterns

Although there is still a considerable amount of conjecture about the theoretical structure

of intelligence (see Anderson, 2001 for a complete discussion), most researchers concur that

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individuals possess certain innate fundamental intellectual or functional abilities, such as

language and visual-spatial abilities. The Wechsler Intelligence Scale for Children (WISC;

Wechsler, 1991) is one instrument that was developed to assess these domains of functioning in

children. Originally designed to provide a general measure of overall, verbal, and non-verbal

intellectual functioning, current conceptualizations of the WISC have evolved. At present, the

WISC-III is generally seen as a complex multidimensional measure that provides a depth and

breadth of clinical information about a number of cognitive functions (Lezak, 1994). As a result,

today the Wechsler scales are commonly used in neuropsychological testing (Boll, 1981; Lees-

Haley, Smith, Williams, & Dunn, 1996), with particular utility for hypothesis testing (Spreen &

Strauss, 1998) that extends beyond the simple evaluation of discrepancies in global ability.

The widespread use of the Wechsler scales to evaluate the recovery of intellectual

functions secondary to head injury has produced variable results over the last three decades. To

some degree, inconsistent findings resulted from the use of numerous statistical approaches to

quantify clinically relevant findings among TBI samples. In the 1980s, for example, numerous

researchers and clinicians strongly advocated analyzing WISC performances at the subtest level

(Glasser & Zimmeran, 1976; Kaufman, 1994) because there was concern that an over-reliance on

the VIQ/PIQ discrepancy was clinically restrictive (Watkins & Marley, 1994). The popularity of

this approach (McDermott et al., 1994) facilitated numerous efforts to correlate subtest profiles

with homogenous cohorts of children (e.g., emotionally disturbed children, learning disability).

This approach experienced limited success identifying such profiles (Butler, et al., 1995;

Donders, 1999; Kaufman, 1990; Lezak, 1994; McDermott, Fantuzzo, & Glutting, 1990; Sattler,

1988). Most notable, were attempts to construct unique profiles (e.g., ACID: Arithmetic, Coding,

14

Information, & Digit Span) for children with learning disabilities (e.g., Joschko & Rourke, 1984;

Anastropoulos et al., 1994) and later, attentional problems.

Similar to other studies conducted during that period, Donders (1993) attempted to

identify a unique subtest profile for children with traumatic head injuries (mild, moderate, and

severe). Using the statistical method cluster analysis, Donders purportedly identified four

clusters unique to TBI. Two of the four clusters (cluster 2 and cluster 4) were exclusively

differentiated by level of performance on each of the WISC-R subtests (i.e., Average subtests

scores vs. Below Average subtest scores) while two of the four clusters (cluster 1 and cluster 3)

were differentiated by performance patterns (i.e., discrepancies between VIQ and PIQ subtests).

These unique clusters were then compared with clusters patterns from the WISC-R

standardization sample. From this review, Donders concluded that head injured children did

exhibit a different subtest profile than healthy children. Unfortunately, in the end, many of the

conclusions regarding the subtest patterns were eventually characterized as (and thus relied

upon) differences between the VIQ and PIQ scales and not based on the identification of unique

TBI subtest patterns as claimed.

While this study prompted a dialogue about identifying unique TBI subtest profiles, it

was later criticized for failing to further expand the knowledge base about intellectual

functioning post head injury (Donders, 1996) because it lacked empirical support (e.g.,

McDermott et al., 1990 for a comprehensive discussion). In particular, concern about

inconsistent (and often low) levels of subtest reliability (Donders, 1996) limited the validity of

the observed profiles. McDermott and colleagues (1990) further point out that the use of subtest

profiling inherently violates one of the central tenets of inferential research; notably the null

hypothesis. In fact, McDermott and colleagues assert that "without clear knowledge of the types

15

of and prevalence of subtest profiles that exist in the population of normal individuals we simply

cannot know whether profiles elsewhere discovered are uncommon or clinically meaningful (p.

296). Noted limitations with this statistical method facilitated suggestions to rely on a "more

conservative approach to Wechsler interpretation based on scale rather than subtest variation"

(Naglieri & Paolitto, p. 210, 2005) to improve the predictive validity associated with observed

profiles (Donders, 1996).

The WISC-III Factor Structure with Traumatic Brain Injured Children

Advancements in statistical analyses (e.g., cluster and factor analysis) led many

researchers to favor interpreting performances (for both normative and clinical samples) at the

factor index level rather than the individual subtest level (Kaufman, 1990; Kaufman et al., 2000;

Saklofske et al., 2000), largely because of the noted reliability associated with the evaluation of

factor index scores (Donders, 1996). Burton and colleagues (2001) reference (i.e., Burton, Ryan,

Paolo, & Mittenberg, 1994) the particular need to examine how subtests may "covary in a

predicted manner" (Burton, Sepehri, Hecht, VandenBroek, Ryan, & Drabman, p. 150, 2001).

A normative study of the WISC-III using confirmatory factor analysis was conducted

with a large sample of healthy children between the ages of 6 and 16 (Wechsler, 1991). In this

standardization study, five different structure models were evaluated for goodness of fit, and

after careful consideration it was determined that the four-factor model provided the best fit to

the data for the total sample (Burton et al., 1994). The four identified factors included: Verbal

Comprehension, Perceptual Organization, Freedom from Distractibility, and Processing Speed.

Both the Verbal Comprehension and Perceptual Organization factors were included on WISC-R

(Wechsler, 1991) and compromised of four separate subtests each (Verbal Compression:

Vocabulary, Information, Similarities, & Comprehension; Perceptual Organization: Picture

16

Completion, Picture Arrangement, Block Design, Object Assembly). The WISC-III also

includes the Freedom from Distractibility and Processing Speed factors for which the Digit

Span/Arithmetic and Coding/Symbol Search subtests are loaded, respectively. Although there

has been some dispute about the four factor model (see Grice, Krohn, & Logerquist, 1999;

Sattler, 1992) the four factor model has been independently replicated in a large sample of

healthy children from the United States (see Roid, Prifitera, & Weiss, 1993) and Canada (see

Roid & Worrall, 1997). Similar to the standardization study, the factor loadings for both the

U.S. and Canadian samples were highest among the individual subtests and the designated

factors (e.g., Vocabulary, Similarities, Information, Comprehension & the Verbal

Comprehension Index). Moreover, both studies showed no statistical improvement with the

three or five factor models over the four factor model.

While the four-factor model is generally agreed upon for healthy samples, additional

research is necessary to evaluate the latent constructs measured by the WISC-III for various

clinical populations (Burton et al., 2001) including neurologically impaired patients for which

there little investigation has generally occurred (Donders & Warschausky, 1996). If observed,

"variant factor structures would imply that the WISC-III was measuring different attributes

among these groups" (Watkins & Kush, p. 4, 2002) which would clearly compromise the use of

this measure for interpretative purposes. This would also present a predicament given the fact

that the WISC is one of the most commonly administered measures in child and adolescent

neuropsychology (Sattler, 1990), and based on an adult measures which has been described as

the "gold standard" of intelligence testing (Ivnik, Malec, Smith, Tangalos, Peterson, Kokmen, &

Hurland, 1992).

Given the numerous indications (Lezak, 2005; Semrud-Clikeman, 2001; Kaufman et. al.,

17

1993; Horton, Wedding, & Phay, 1981; Fletcher, Levin, & Butler, 1995) of disrupted cognition

secondary to TBI, including intellectual functioning, establishing support for the latent structure

of the WISC-III within this population is necessary. One might speculate that notable changes

may be observed among the skills which make up the processing speed and freedom from

distractibility (working memory) factors, which have shown a particular sensitivity to TBI.

Several studies have argued that a three-factor solution may more accurately characterize

intellectual functioning for certain clinical populations, including children with ADHD who

similar to TBI patients routinely evidence problems with sustained attention, divided attention,

and response inhibition. For this reason, additional support for the use of the four-factor model

with TBI must first be established.

In early research, Donders and Warschasuky (1996) provided general support for a four-

factor model with TBI. In this study, Donders and Warschausky examined the construct validity

of the WISC-III in a clinical sample of children with head injuries using structural equation

modeling (SEM). The sample included a large group of children (N = 170) with a history of

severe (n = 70), moderate (n = 54), and mild (n = 45) head injuries ranging from ages six to

sixteen who had completed WISC-III within one year post-injury. Eight competing latent

variable models "were evaluated for goodness of fit and parsimony" (Donders & Warchasuky, p.

186, 1996). Donders and Warschausky noted that five of the models replicated analyses

conducted in the original standardization study (see Wechsler, 1991); the sixth model was based

on proposals made in early research (i.e., Roid et al., 1993), and the seventh and eight models

were a variation of a three-factor model previously proposed for the WISC-R (Kaufman, 1975)

and WISC-III (Reynolds & Ford, 1994) and an alternate model proposed by Kaufman (1994)

(Donders & Warschausky, 1996).

18

Results from this study were among the first to provide empirical support for the four-

factor structure with a large group of children with closed head injuries. Good fit was found for

the four-factor model, which contained the following factors: Verbal Comprehension, Perceptual

Organization, Freedom from Distractibility, and Processing Speed. In this study, Donders and

Warschausky (1996) provided support, which had been previously variable, for the Verbal

Comprehension and Freedom from Distractibility factors. Moreover, the processing speed factor

was particularly well defined, with factor loadings of .79 and .92 for Coding and Symbol Search,

respectively. The study also demonstrated a strong correlation between low scores on the

perceptual organization index with injury severity. Greater impairment on the perceptual

organization and processing speed indices has subsequently been exhibited among groups of

children with head injuries (e.g., Hoffeman, Donders, & Thompson, 2000). Impairment with the

latter has been shown in other studies (e.g., Donders, 1996) suggesting that disrupted processing

speed scores may be one of the factors which most accurately differentiates healthy samples and

children with head injuries.

Although the Donders and Warschausky (1996) study provided support for the use of the

four-factor model with children with head injuries, a number of limitations still need to be

addressed. First, examination of the factor structure using SEM needs to incorporate a larger

group of children with a history of moderate and severe head injuries. In the Donders and

Warschausky study, approximately 35% of the children were characterized as having a history of

mild (n = 45) head injury. Although some argument has been made about the degree of

disruption that children with moderate head injuries exhibit one year post-injury, it is clear that

the unique set of deficits this cohort exhibits is distinct from those exhibited by children with

mild head injuries. In fact, the study of children with mild head injuries has consistently shown

19

negligible findings at one year post-injury. Therefore, the inclusion of a large sample of mild

TBI participants makes it difficult to determine if the findings were accurate for children with

moderate and severe head injuries. It is possible that the inclusion of a large sample of children

with mild head injuries may have incorrectly estimated the goodness of fit for the four-factor

model with this group. Therefore, the present study was conducted, in part, to re-evaluate the

four-factor model with a large group of children with moderate and severe head injuries to

determine if empirical support for the proposed model could be found.

Second, the four-factor model for children with a history of severe head injury should be

compared to children who manifest other neuropsychological or neurological impairments. This

can provide further empirical support for the use of the WISC-III with pediatric neurological

samples as well as further the understanding of the specific deficit in intellectual functioning

within this population. To address this, the current study compared the factor scores of two large

groups of children with neuropsychological deficits, including children with severe head injuries

and children with a primary diagnosis of attention-deficit/hyperactivity disorder (ADHD).

Finally, fewer studies have evaluated functioning in head injured children at one year

post-injury. In the past, studies have been conducted at the more common time intervals of

three, six, and nine months. With indications that children continue to demonstrate signs of

recovery at 15 months post-injury (see Hawkins et al., 2002) the study addressed this by focusing

on participants at one-year post-injury.

WISC-III Factor Index Cluster Analyses

Once empirical support for the proposed factor structure has been demonstrated, and that

the underlying structure of intellectual abilities does not differ between healthy and clinical

pediatric samples, then factor index scores can be confidently used to identify unique cluster

20

groups in terms of cognitive strengths and weaknesses. Donders (1993, 1997) suggests the use

of factor index profiles in clinical research may help to improve both the diagnostic accuracy as

well as enhance the study of clinical subtypes (based on performance) for children who have

sustained a moderate or severe head injury. However, to date, the use of factor index cluster

analyses has not been widely incorporated into areas of clinical research such as traumatic brain

injury.

In an early study, Glutting and colleagues (1994) used cluster analysis to explore factor

index patterns for the WISC-III standardization sample. Results identified six specific profiles

predominantly distinguished by overall level of performance, although some variability to the

patterns was observed. There were a number of limitations to that study; most notable was the

inclusion the Wechsler Individual Achievement Test (WIAT) into the overall cluster process. To

address this limitation, Donders conducted a study with the same sample and only used the

WISC-III as a clustering variable. This study identified five distinct clusters which were

primarily differentiated by performance (i.e., quantitatively). These identified clusters were

subsequently validated with variables that were not previously included in the clustering process,

including level of parental education.

As an extension of their earlier research with healthy samples, Donders and Warchausky

(1997) conducted a cluster analytic study of factor index scores from the WISC-III using a large

group of pediatric TBI patients. The study aimed to determine if a cluster pattern unique to TBI

could be identified using a sample of children with head injuries. In total, the study identified

four distinct clusters; although Donders and Warchausky suggested the most notable was the

cluster solutions included participants who demonstrated higher scores on the Verbal

Comprehension and Freedom from Distractibility indices in comparison to the Perceptual

21

Organization and Processing Speed indices. Because this pattern was absent from the previous

study with healthy samples and because the cluster was shown to have the greatest proportion of

children with severe head injuries it was considered to be unique for TBI. While membership in

the "TBI" cluster was not directly related to notable demographic or clinical characteristics, a

strong correlation between processing speed and length of coma was noted for children within

that cluster (Donders & Warchausky, 1997).

Although these findings provided a foundation from which future studies could evaluate

factor index profiles among children with head injuries the study was not without problems. For

example, the inclusion of a large group of children with mild head injuries may have artificially

influenced the nature of the cluster solutions, confounding the conclusion regarding the

identification of a unique TBI cluster. Thus, further understanding of factor index patterns may

be obtained by utilizing a more restricted cohort of moderately and severely impaired children

with head injuries. Moreover, while validation with clinical variables such as length of coma or

Glasgow Coma Scale scores helped to further distinguish the clusters it did not provide

qualitative support for the clusters (Aldenderfer & Blashfield, 1984). One way to address this

limitation would be to further validate the clusters with independent neuropsychological

instruments which were not originally included in the clustering process.

To date, no TBI cluster studies with pediatric samples have included this validation

procedure; however, some evidence for what might be expected can be drawn from a cluster

profile study conducted with adults with a history of head injury (see van der Heijden &

Donders, 2003). Findings identified three distinct clusters based on performance; although none

reflected the unique TBI profile previously described by Donders and Warchausky (1997).

Moreover, no significant effect for variables such as age, ethnicity, education, length of coma, or

22

gender was observed, although as expected a significant difference was observed among the

clusters for injury severity. As a supplement to the cluster process, the cluster solutions were

compared on two independent measures of neuropsychological functioning (i.e., Trail Making

Test (Part A & B, Wisconsin Card Sorting Test). Findings showed statistically significant

differences among each of the three clusters for both Part A and Part B of the Trail Making Test

and on the Wisconsin Card Sorting Test. As expected, the participants who demonstrated the

greatest level of sustained functioning on the WAIS-III obtained the highest scores on the

validation instruments. Similarly, participants who demonstrated the second greatest level of

sustained functioning on the WAIS-III obtained the second highest scores on the validation

instruments. This pattern was consistent for the participants who evidenced the lowest level of

functioning on the WAIS-III.

Although the study failed to identify a specific cluster profile that was unique for adult

TBI patients, it did support previous conclusions that individuals with head injuries tend to

perform relatively poorly on tasks requiring processing speed. However, as the authors noted,

the generalizability of the findings to be pediatric samples may be limited given different

compositions of the processing speed indices in the WISC and WAIS measures. To address this

limitation, the present study attempted to validate any viable cluster groups obtained through

cluster analysis with independent neuropsychological measures of attention, processing speed,

language, working memory, an executive functioning. In addition, demographic variables such

as age and gender as well as information from a series of self, parent, and teacher report

questionnaires rating behavior and psychological functioning were also used in an attempt to

independently validate the cluster groups.

23

Additional Validating Variables for Cluster Subtype Patterns

In the past, studying the association between indices of injury severity (i.e., length of

coma, Glasgow Rating score) and variables such as age of injury, time since injury, and gender

has proven efficacious to TBI research. Evaluating these same variables in conjunction with the

identified cluster groups may help predict outcome in children and adolescents with head injuries

(Anderson, Northam, Hendy, & Wrennal, 2001).

Age of Onset

The issue of neuroplasticity, first speculated upon by Kennard (1940), has remained of

particular interest to researchers studying brain injuries (e.g., Brazelli et al., 1994; Dennis &

Barnes, 1990; O'leary & Boll, 1984; Semrud-Clikeman, 2001). The prevailing consensus among

early studies indicated that children with head injuries who were injured early in childhood

exhibited fewer disruptions in cognitive functioning when compared with older children and

adolescents (e.g., Kennard, 1940) (Benton & Tranel, 2000; Dennis, 2000; Semrud-Clikeman,

2001). More recent research, however, has repeatedly disputed this finding and shown the long-

term effects associated with TBI to be greater in younger children than older children (Anderson

and Pentland, 1998; Mckay, Halperin, Schwartz, & Sharma, 1994). This body of research has

grown substantially over the last several years (see Chapman et al., 2000; Dennis & Barnes,

1990; Tranel & Eslinger, 2000; Verger et al., 2000), particularly among children with diffuse

damage (Teeter, 1986). For example, children who sustain a head injury before three years of

age exhibit greater impairment on tasks measuring intellectual functioning (Aram & Eisele,

1994; Levin et al., 1995), expressive language (Ewing-Cobbs et al., 1989), and visuoperceptual

processing (Thompson et al., 1994) than older children and adolescents with similar injuries.

Similarly, Anderson and Moore (1995) have shown that children who suffer insults prior to

24

seven years of age are less likely to demonstrate recovery of fluid intelligence.

In a comprehensive review of the childhood TBI literature, Kolb and Whishaw (1990)

identified three critical periods of development for children who experience severe head injuries:

before the age of 1, between the ages of 1 and 5, and after 5 years old. According to Kolb and

Whishaw, each of these groups is likely to exhibit significantly different functional outcomes.

For this reason, it remains important to study this variable when researching TBI in pediatric

samples.

Injury Severity

Over the last twenty years, research has consistently demonstrated a strong relationship

between injury severity and poor performance on neuropsychological and intellectual assessment

measures (e.g., Begali, 1992; Donders, 1996; 1997; Spreen & Strauss, 1998; Teeter & Semrud-

Clikeman, 1997). When compared to children with mild head injuries, children with moderate

and severe brain injuries exhibit much greater deficits in functioning (Semrud-Clikeman, 2001;

Donders, 1996). A severe head injury is characterized by a score of less than 8 on the Glasgow

Coma Scale (GCS; Jennett & Teasdale, 1981); typically this definition includes a loss of

consciousness that extends beyond 24 hours. A moderate head injury is characterized by a

Glasgow Coma Scale score of 9 to 12 (Jennett & Teasdale, 1981). To date, a majority of the

literature has evaluated children within the acute phase of recovery although there is evidence

that the recovery process extends beyond 18 months. For this reason, more information is

needed about the relationship between injury severity as measured by a Glasgow Rating score

and cognitive functioning at approximately one year post-injury. It was hypothesized that there

will be a positive correlation between low IQ factor index scores and low Glasgow Rating

25

scores, particularly with regard to processing speed, for which a strong correlation has already

been established.

Time since Injury

The relationship between severity of injury and intelligence has been found to change

over time (Banich et al., 1990). There is considerable conjecture about the amount of time

required for recovery following a head injury. Some researchers argue that recovery can occur

(depending on initial injury severity) in as little as six months, while others suggest a recovery

continues to occur as late as five years post-injury (Semrud-Clikeman, 2001). In a study of

moderate and severe TBI, recovery (recovered intellectual functioning) was characterized as

"rapid" within the first few months post-injury and noted to level off at six months (Dikeman et

al., 2000). Fewer studies have evaluated outcomes at one year post-injury (Lanoo et al., 2001).

In one early study that evaluated recovery over an extended period, Dikeman and colleagues

assessed head injured patients at three intervals (i.e., 1, 12, and 24 months post-injury) and found

marked improvements in all levels of functioning during the first year; however, improvement

within the second year was limited and varied depending on injury severity.

Specific Cognitive Processes and IQ

Although intellectual functioning as a global (higher-order) process is important to study

following a head injury, a closer examination of the various roles that more specific cognitive

processes play, such as executive functioning, attention, and working memory play in the

preservation of the different domains of intellectual functioning post-injury has not been fully

explored. It is clear from studies that children who sustain a head injury (moderate or severe)

tend to exhibit greater levels of functional impairment than children with mild head injuries.

26

Less is understood about how specific cognitive processes differentiate levels of intellectual

functioning post-head injury.

Executive Functioning

Executive functioning is one of the most widely studied neuropsychological constructs,

and yet the concept remains abstract (Burgess, 1997), poorly defined, and often misunderstood.

In general, executive functioning has been characterized as a multidimensional construct that

covers a range of higher-order cortical functions (Lehto et al., 2003), including goal directed

behavior, attention control, planning, problem-solving, and inhibition (Anderson, 1998; Berg,

1986; Burgess, 1997; Lezak, 1995; Stuss, 1991). There is a growing body of evidence which has

localized these functional abilities in the frontal and pre-frontal lobe regions in the human brain

(Fuster, 1997; Goldman-Rakic, 1987; Lezak, 1995; Semrud-Clikeman, 2001) and yet the

relationship between the structural and functional deficits remains ambiguous in comparison to

other regions of the brain (Pennington, 1997).

Developmental physiology studies have shown frontal lobe development to be

particularly evident during early childhood (i.e., first five years) (Hudspreth & Pribram, 1990),

with continued growth through early adulthood (Thatcher, 1991). Despite evidence linking

frontal and pre-frontal lobe with executive functioning, and suggestions that these structures

develop early, there is still much conjecture about how early in life children actually develop

such executive skills (e.g., Anderson, 1998).

There are a number of competing theories that speculate about the structure of executive

functioning. Miyake and colleagues (2000) for example, have provided empirical support for a

three-factor model of executive functioning. Based on a combination of theoretical and empirical

findings, Miyake and colleagues argue executive functioning consists of three unique processes,

27

including shifting, updating, and inhibiting. Miyake and colleagues have operationalized shifting

as the ability to change between mental tasks. Updating refers to the cognitive process of

developing visual representations in working memory (Lehto, 1996) while inhibition is defined

as the ability to deliberately suppress a dominant (and automatic) response (e.g., comment).

Miyake and colleagues (2000) suggest these three constructs function independently and are

stable over developmental periods. Other researchers such as Russell (1999) working from a

Piagetian perspective believe a two-factor model of executive functioning is more appropriate.

Russell coined the term "executive Piagetian" and suggests inhibition and working memory are

the core cognitive processes of this construct.

As the debate about the theoretical structure and time period for development continues,

it is clear that deficits in executive functioning are common among children with traumatic brain

injuries (Begali, 1992; Lezak, 1994; Nyob et al., 1999). For this reason it is important to study

executive functions across a wide development period in an effort to better understand how these

higher-order processes relate to sustained intellectual functioning in children with head injuries.

According to Denckla (1994), this becomes increasingly complicated when considering

disruptions in executive functioning may be more difficult to assess in younger children, for

whom these skills are still developing. This caveat not withstanding, evaluating how executive

functions differ among children with head injuries will continue to be a focus point in the future,

particularly with regard to development of empirically validated treatment programs for children

with TBI.

Frontal lobe dysfunction has also been implicated for other clinical childhood disorders

such as Attention-Deficit/Hyperactivity Disorder (ADHD) (Barkley, 2003; Willcutt, Doyle,

Nigg, Farone, & Pennington, 2005). Much of the research in this area has focused on processes

28

such as inhibition, vigilance, and planning (Willcutt et al., 2005) which have been linked to

neuroanatomical regions in the frontal lobe, most notably the dorsalateral prefrontal (Seidman,

Valera, & Makris, 2005) and frontalstriatal (Bradshaw & Sheppard, 2000) systems. Given this

evidence, the study of executive functions in children with ADHD may provide a useful

comparison from which to study these processes in children with head injuries.

Levin et al. (1993) examined the effects of age and injury severity on a series of tasks

measuring executive functioning with a group of children with mild, moderate, and severe head

injuries. Findings showed younger children evidenced lower scores than did older children even

when injury severity was controlled. Slomine and colleagues (2002) found similar results in a

recent study of sixty-eight children with moderate or severe head injuries. Results indicated age

was an important component of performance. Specifically, older children (13 to 15)

demonstrated fewer sorting errors on the Wisconsin Card Sorting Test (WCST). The authors

claimed these findings provided further empirical support for the "vulnerability hypothesis"

(Spreen & Strauss, 1997), which argues structural and functional deficits acquired early in life

have a greater impact on cognitive development than deficits acquired during adolescence or

young adulthood.

In 1999, Nyob and colleagues completed a longitudinal study of children who sustained

moderate and severe head injuries as children. The study followed a small sample of pre-school

aged boys (n = 19) and girls (n = 14) from childhood through adulthood. Approximately 90% of

the children in this study were struck by a moving motor vehicle in the years between 1959 and

1969. Two significant findings resulted from this study. First, among children with moderate and

severe head injuries, those that were able to re-integrate back into the school system following

their injury were significant more likely to be employed full time as adults. The second finding

29

was the strong correlation between full time work and scores on the WCST. This suggests a link

between executive functioning and adaptive functioning in daily living which needs to be

explored further.

Still, additional research on executive functioning among children with severe head

injuries is still needed (Pennington, 1997) particularly given their susceptibility to damage

following a trauma (Denkla, 1994). In the past, executive cognitive processes have been related

to good academic performance in school (Semrud-Clikeman, 2001), vocational success (Nyob et

al., 1999), and successful re-integration into the community (Denkla, 1994). It is, however,

unclear to what degree executive processes contribute to sustained intellectual functioning

following a head injury. It is also unclear, to what degree disruptions in executive functioning

may be able to help differentiate different clusters of children with moderate and severe head

injuries.

Attention

There has been some discussion that attention skills develop gradually throughout

childhood and early adulthood, with less complex skills such as sustained attention developing

earlier than more complex skills (e.g., capacity to focus, shift attention) (see Passler, Isaacc, &

Hynd, 1985). This assertion parallels observations made about the developing of executive skills

in children. Attention deficits are frequently observed in children who have experienced a severe

head injury as young children. Such deficits have been widely associated with impaired

functioning at school and problems with emotional adjustment within this population (Dennis et

al., 1995).

While discussed globally, attention is widely considered to be multidimensional construct

which consists of components involving alertness, sustained attention, divided attention, and

30

selective attention (Sohlberg & Mateer, 2001). Research has shown that problems with attention

and concentration are common among children with head injuries, but for most, they naturally

resolve within the first year post-injury (Dennis et al., 1995; Johnson & Roethig-Johnson, 1989).

There is speculation, however, that problems with attention may persist well after the first year

(Ewing et al., 1998), particularly among children with severe head injuries (Kaufmann et al.,

1993). This is further complicated if evidence of restored functioning in other areas is

considered (Goldberg, 2001). For example, Dennis and colleagues (1995) found younger

children performed poorer on attention tasks than older children with similar injuries. In another

study, Dennis (1989) noted that the earlier a child experienced an injury the greater the

likelihood for prolonged impairment in attention. In turn, the disruption of attentional abilities

may inhibit the development of other cognitive skills, causing a "snow ball effect" (Gil, p. 345,

2003). Fenwick and Anderson (1999) examined the attention capabilities of 18 children ages 8

to 14 using various attentional tasks. Age at the time of injury consistently predicted task

performance. Kaufman and colleagues (1993) investigated sustained attention in children with

TBI at six months post-injury using a continuous performance task. Findings showed that older

children with severe head injuries outperformed younger children. Persistent difficulties with

attention are most common among younger children with severe injuries and these deficits have

been related to problems learning basic academic skills (Semrud-Clikeman, 2001), with

continued disruption more related to severe head injury than mild head injury (Asarnow et al.,

1995).

Research has continually demonstrated the important role that attention plays in the

development of intellectual functioning and furthermore, these skills have been shown to be

important for the acquisition of additional more complicated abilities in adolescence and early

31

adulthood. Despite the central role attentional processes play in the cognitive development at all

ages (Barkley, 1998; Dennis et al., 1995; Kaufman, Fletcher, Levin, Miner, & Ewing-Cobbs,

1993), relatively little is known about the direct contribution this attention processes make to

sustained intellectual functioning following a head injury. Of particular interest are age

distributions; specifically, are attention more strongly correlated with other cognitive processes,

and IQ in particular, in older children who experience a severe head injury, compared to younger

children, or are the inter-relations among these processes similarly disrupted across age groups?

Working Memory

Memory deficits have been widely observed in adults (e.g., Alzheimer's, dementia, and

amnesia) and children (e.g., head injuries). Researchers have shown a particular interest in study

working memory because of its strong relationship with other specific cognitive functions.

Levin and colleagues (2002) describe working memory as the limited capacity process for

storage, monitoring, and manipulation of information. Research has shown working memory is

enhanced with age (e.g., Swanson, 1999) and also related to frontal lobe processes (Goldman &

Alexander, 1977) such as problem-solving, receptive language, and mathematics. Working

memory has been shown to be particularly susceptible to severe traumatic brain injury (e.g.,

Levin et al., 1993; Lezak, 1995; McAllister et al., 1999; Schacter, 1995; Semrud-Clikeman,

2001). Given the strong correlation between working memory and learning, it is imperative to

gain more information about the dynamic relationship between attention and working memory,

as well as working memory and sustained intellectual functioning in children with severe head

injuries.

32

Study Parameters

The current study was conducted to examine the structural nature of intelligence in

children with moderate to severe TBI and whether their exist sub-populations of children with

specific profiles of WISC factor scores. Also, the current study sought to validate any derived

cluster groups in terms of demographic, clinical, and independent neuropsychological measures.

Finally, with the goal of providing greater ecological validity for the study findings, the current

study also sought to compare the clinical TBI group to a clinical comparison group of children

with ADHD.

More specifically, the study was designed to address three important questions which

were prominently absent from the current literature on pediatric TBI. The first research question

dealt directly with the wide spread use of the WISC-III as a measure of intellectual functioning

among children with documented neurological impairments. To date, a myriad of empirical

support for the four-factor WISC-III model has been generated for healthy samples, and yet

surprisingly very little support for the use of this model has been documented for children with

moderate or severe head injuries. For this reason, it was important to conduct a factor analysis of

the proposed model to better understand the underlying structure of intelligence in children with

TBI; this would also validate conducting additional cluster analyses. Given the wealth of

confirmatory factor analysis studies on the WISC-III using healthy samples, and preliminary

evidence from head trauma studies with the WISC-III, it was hypothesized that further empirical

support for the four-factor model would be observed. The proposed four-factor model was also

compared with a three-factor model to determine which model provided a better fit for the data.

As a supplement to the first component of this study, a confirmatory factor analysis of the

WISC-III for a large group of children with Attention-Deficit/Hyperactivity Disorder (ADHD)

33

was also conducted. Given the plethora empirical evidence regarding the four-factor model with

ADHD children (see Roid et al., 1993) it was hypothesized that further support for this model

would be observed with the ADHD group in this study. Statistical procedures (i.e., ANOVA)

were used to determine if the two groups exhibit significant differences on the factor index

scores. Additional variables such as gender and ethnicity were also examined for group

differences. It was hypothesized that in comparison to the ADHD group, the TBI group would

exhibit greater deficits in intellectual functioning, particularly in perceptual organization and

processing speed.

Secondly, this study was conducted to determine if distinct subgroups (i.e., clusters)

could be identified in a sample of children with moderate and severe head injuries using factor

index scores. As a statistical procedure, cluster analysis aims to identify "clusters" within a

specific sample that have distinguishable attributes which may be useful for evaluating and

predicting specific behaviors (Lorr, 1983).

Based on research from previous studies (Donders, 1996; Glutting et al., 1994; van der

Heijden and Donders, 2003), it was expected that at least three distinct clusters would be

identified with the current sample. It was further hypothesized that participants in the most

impaired cluster would evidence the unique TBI factor pattern (i.e., greater disruption for

processing speed and perceptual organization) previously described by Donders and Warchausky

(1997).

Similar analyses were conducted to examine if unique cluster solutions could be

identified among the group of children with ADHD. Identification of cluster groups within this

clinical comparison sample may provide some evidence that the obtained TBI clusters, like any

such derived ADHD clusters, simple differ quantitatively on the WISC indices and not

34

qualitatively. Unless of course, the ADHD cluster could be shown to be related to ADHD

diagnostic subtypes, which might suggest that differences in underlying brain-behavior

relationships contribute uniquely to performance on the WISC indices. As such, given a review

of published reports on the variability of WISC-III performance within groups of children within

groups of children with ADHD, it was hypothesized that at least two unique clusters,

differentiated by type of performance would also be observed. The WISC-III cluster profiles for

the ADHD sample were compared with the profiles from the TBI sample. With a number of

behavioral and cognitive similarities between these two clinical groups (e.g., disruption in

attention) there has been some precedence for comparing these two clinical groups in the past

(see Konrad, Gauggel, Manz, & Scholl, 2000).

In an attempt to validate the cluster analytic results for the TBI group, demographic and

clinical characteristics of the subtypes such as: Length of Coma, Glasgow Coma Rating, Gender,

and Age were examined. There has been some discussion, for example, that processing speed is

significantly influenced by injury severity. Applied to the cluster analysis, it was hypothesized

that factors such as injury severity and length of coma would show a greater correlation with

processing speed for cluster three than clusters one or two.

Historically, one of the criticisms of cluster analysis has been the absence of well defined

cut-off criteria for empirically identifying cluster solutions (Everitt, 1978). To address this

limitation, Aldenderfer and Blashfield (1984) have proposed the use of several statistical

procedures (e.g., Cophentic correlations, significant testing) to validate the cluster solutions.

These procedures are not, however, without criticism. Aldenderfer and Blashfield note, for

example, that because cluster analysis is designed to identify separate groups for which "no over-

lap along the variables being used to create the clusters" is observed (p. 65) significance testing

35

would only stand to confirm the procedure and provides no actual validation for the clusters. To

address this limitation, Aldenderfer and Blashfield propose that researchers divide data sets into

two samples and then re-run the analyses to see if the cluster solutions are replicable. While

replication of the clusters using this process provides evidence for internal consistency, failure to

reject the model does not guarantee the validity of the solution (Aldenderfer & Blashfield, 1984).

Moreover, replication requires a large sample size given the noted statistical restrictions required

for cluster analysis. Therefore, Aldenderfer and Blashfield suggest that conducting significance

testing with external variables which were not used to create the original clusters is the most

efficacious means for generating empirical support for the identified cluster solutions.

To address this last recommendation, the identified cluster subtypes in the present study

were further validated using external, independent neuropsychological and psychological

instruments. Specifically, a series of neuropsychological instruments were selected to determine

if the distinct clusters could be further differentiated in terms of specific processes such as

executive functioning, attention, working memory, and language. It was hypothesized that the

distinct cluster groups identified by performance on the WISC-III factor indices would show

significant differences on such measures of executive functioning, working memory, and

attention. In particular, those children who scored better on the independent measures of

processing speed, cognitive flexibility, and receptive language would be in the cluster that

exhibited sustained intellectual functioning on the WISC-III factor indices. The observed cluster

solutions for the ADHD were similarly evaluated with external neuropsychological measures to

determine if they could be further differentiated. Given the hypothesis that fewer cluster

solutions would be identified for the ADHD group, it was expected that the groups would not

exhibit as much differentiation with external neuropsychological measures.

36

The clusters for the TBI and ADHD samples were also evaluated in an effort to further

describe the clinical characteristics of the two groups, as well as provide a link between brain

and behavior in these groups. In the past, injury severity and damage to the frontal lobes has

been related to changes in emotional regulation, poor impulse control, and diminished flexibility

in thinking (Bigler, 1988). In addition to noted attentional problems, increased levels of anxiety

and depression have also been reported among children with moderate and severe head injuries.

For this reason, it was hypothesized that parents would report higher levels of anxiety and

depression with decreased social and adaptive functioning for the TBI group than parents of

Children with ADHD who would in comparison report higher levels of attentional problems and

hyperactivity.

37

CHAPTER 2

METHOD

Participants

Findings for the present study were derived from a moderately large sample (n = 193) of

boys (n = 118) and girls (n = 78) who presented at a large inner-city pediatric hospital for a

neuropsychological evaluation. Only participants with a primary diagnosis of either traumatic

brain injury (n = 123) or attention-deficit hyperactivity disorder (n = 70) were included in the

present study. Approximately 59 (n = 72) and 66 (n = 46) percent of the participants in the

traumatic brain injury and ADHD groups were male, respectively.

For the TBI sample, criteria for inclusion in the present study included the following: 1).

Diagnosis of a non-penetrating head injury, 2). Ages between 6.0 and 17.0 years, 3). Completion

of the WISC-III and other neuropsychological measures, and 4). Unconsciousness lasting 24

hours or more. Initial injury severity was classified using the Glasgow Coma Scale (GCS;

Teasdale & Jennett, 1974).

For the ADHD sample, criteria for inclusion included the following: 1) Diagnosis of

ADHD post-neuropsychological evaluation, 2). Ages between 6.0 and 17.0, and 3). Completion

of the WISC-III and other neuropsychological measures.

Procedures

Each of the participants in this study completed a comprehensive (flexible)

neuropsychological evaluation. Data collection was conducted with full Internal Review Board

approval from Baylor Research Institute. Participants were referred for a neuropsychological

evaluation by their primary care physician or the primary inpatient physician at Our Children's

House at Baylor. Results from each neuropsychological battery were entered into a single

38

database. No personal identifiers were used when this data was coded. On average, participants

underwent testing 12 months post-injury. Testing was usually completed within a single visit,

lasting between five to seven hours. The evaluations were conducted by either a licensed

neuropsychologist or a supervised doctoral-level psychology student.

Measures

Wechsler Intelligence Scale for Children-Third Edition (WISC-III): The WISC-III ® (The

Psychological Corporation, San Antonio, TX, www.harcourtassessment.com) is a

multidimensional measure of intellectual functioning that was designed to evaluate a number of

verbal and non-verbal processes (Wechsler, 1991). According to the manual, the WISC-III was

standardized with an ethnically and economically diversified sample of children from across the

United States. The sample consisted of 2,200 children, with 200 children (100 Male, 100

Female) represented in each of the eleven separate age groups (ranging from six years to sixteen

years, 11 months).

The administration of the complete battery (i.e., 12 subtests) takes approximately 60 to 90

minutes. For each subtest, raw scores are calculated and converted to standardized scaled scores

with a mean of 10 and a standard deviation of 3. The scaled scores from the individual subtest

scores are then combined to create four higher order factor index scores (Verbal Comprehension,

Perceptual Organization, Freedom from Distractibility, & Processing Speed) with a mean of 100

and a standard deviation of 15. The four factor index scores can be further combined to create

three higher order factors (Verbal IQ, Performance IQ, & Full Scale IQ) with a mean score of

100 and a standard deviation of 15.

Empirical validity for the WISC-III has been widely demonstrated among healthy

children and certain clinical samples (see Roid, Prifitera, & Weiss, 1993). Watkins (2002)

http://www.harcourtassessment.com/

39

examined 12 completing WISC-III models using a large group (N = 1,201) students between the

ages of six and sixteen with learning disabilities. Empirical support for the four factors was

clearly identified.

Reliability coefficients for the entire sample ranged from a low of .69 (Object Assembly)

to a higher of .87 (Block Design). Test-re-test coefficients ranged from .61 to .80 for children

between the ages of 6 and 7, .62 and .87 for children between the ages of 10 and 11, and .54 and

.93 for children between the ages of 14 and 15. Interrater reliabilities were consistently high for

the Vocabulary (.92), Comprehension (.97), and Similarities (.94) subtests.

Trail Making Test (TMT): The Trail Making Test® (Neuropsychology Press, Tucson,

AZ) was added to the Halstead-Reitan neuropsychological battery in 1944 (see Spreen & Strauss,

1998). It is compromised of two distinct tasks (Part A & Part B). Part A of the TMT was

designed to measure visual attention, information processing, and visual scanning. Part A

consists of 25 randomly placed numbers; the purpose of the task is to connect each of the

numbers in the correct ascending order as quickly as possible. In addition to visual sequencing,

the Part B of the TMT also evaluates cognitive flexibility, mental shifting (Spreen & Strauss,

1998) and visuoperceptual processing (Woodruff, Mendoza, Dickson, Blanchard, &

Christenberry, 1995). Part B of the TMT is often considered to be one of the best indicators of

frontal lobe dysfunction among brain injured patients (see Verger et al., 2000). It consists of 13

numbers and letter pairs which are connected in alternating ascending succession. The TMT

(Parts A & B) are timed and participants are encouraged to complete the task as quickly as

possible without making mistakes. However, participants are allowed as much as 150 seconds to

complete Part A and three minutes to complete Part B.

Empirical validating for the TMT (Parts A & B) has been repeatedly demonstrated. For

40

example, the TMT has good interrater reliability: Part A = .94 and Part B = .90 (Spreen &

Strauss, 1998). Spreen and Strauss noted that Snow et al. (1988) found one-year test-retest

reliability coefficients to be .64 and .72 for Part A and Part B, respectively. According to

Heilbronner and colleagues (1991), Part A and Part B of the TMT have a modest correlation of

.49. Hays (1995) found a moderate correlation between the TMT (Part A & B) with IQ.

Children's Category Test (CCT): The Children's Category Test® (Pearson Education,

Inc, San Antonio, TX, www.harcourtassessment.com) is a standardized test that is used to

evaluate problem-solving and executive functioning in children and adolescents (see Boll, 1993).

The CCT was developed for children between five years of age and 16 years-11 months of age.

The CCT takes approximately 20 minutes to complete. The total number of errors can be

calculated and converted into a T-score with a mean of 50 and standard deviation of 10 (Donders

& Nesbit-Greene, 2004). Higher T-scores are indicative of a better performance on the CCT

(Bolls, 1993).

Continuous Performance Test (CPT): The CPT® (Multi-Health Systems, North

Tonawanda, NY, www.mhs.com) is commonly used to assess visual attention, vigilance, and

impulsivity (Spreen & Strauss, 1998). The CPT is a computer-administered measure that takes

approximately 15 minutes to complete. The program is designed to be used with children

between the ages of 6 and 17. Scores from the CPT are converted into standardized T-scores

with a mean of 50 and a standard deviation of 10. T-scores above 60 are indicative of clinical

problems (Spreen & Strauss, 1998). Reaction time index has been shown to be a useful construct

for the evaluation of information processing or processing speed (Reinvang, 1998).

Participants respond to flashing visual stimuli by pressing the space bar on a keyboard or

a button on a computer mouse when they see the appropriate target (i.e., X). The CPT output

http://www.harcourtassessment.com/

http://www.mhs.com/

41

provides a number of indices, including standardized omission and commission scores.

Although there is some disagreement, it is generally agreed that high omission errors on the CPT

reflect deficits in sustained attention (i.e., vigilance). Conversely, high commission errors are

generally considered reflections of problems with impulsivity and inattention. Studies show test-

retest correlation coefficients for the CPT range from a low of .05 (Hit SE ISI Change) to a high

of .92 (Confidence Index) (Spreen & Strauss, 1998).

Test of Memory and Learning (TOMAL): The TOMAL® (Pro-Ed Inc, Austin, TX,

www.proedinc.com) was created to provide a comprehensive and standardized assessment of

memory in children and adolescents (see Dumont, Whelley, Comotois, & Levin, 1994 for a

comprehensive review). The TOMAL was developed to be administered to children between the

ages of 5 and 19. The TOMAL is compromised of 10 regular subtests and it takes approximately

45 minutes to administer. Individual subtests have a mean score of 10 with a standard deviation

of 3; the composite index scores have a mean score of 100, with a standard deviation of 15.

Considerable empirical support for the TOMAL has been generated. Specifically, reliability

indices range between .80 and .90 for all of the individual subtests (Dumont et al., 1994). Test-

retest coefficients are .70 or higher for the individual subtests and .80 for the composite indices

(Dumont et al., 1994).

Oral and Written Language Scales (OWLS): The Listening Comprehension scale from

the Oral and Written Language Scales® (Pro-Ed Inc, Austin, TX, www.proedinc.com) (Carrow-

Woolfolk, 1996) was designed to assess receptive language ability. The OWLS was developed

to be used with individuals ranging from three years of age to twenty-one years of age and takes

approximately fifteen to forty minutes to complete in total (Willis, 2001). Raw scores are

concerted to standardized scores with a mean of 100 and a standard deviation of 10.

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42

The OWLS was standardized on 1,373 children stratified to match nationalized (1991)

sample on demographic characteristics such as age, gender, race, geographic region and level of

maternal education (Willis, 2001). Internal consistency ratings reportedly range from .77 to .94

(Willis, 2001). Test-retest reliabilities were reportedly moderate at .88 and .87. Construct

validity testing showed a good correlation (r = .61) with the WISC-III Verbal Intelligence scale

(Willis, 2001).

Behavior Assessment Scale for Children-Parent Rating Scale (BASC-PRS): The

Behavior Assessment Scale for Children-Parent Rating Scale® (Pearson Assessment Group,

Minneapolis, MN, www.pearsonassessment.com) is a 138 item self-report questionnaire which is

designed to evaluate internalizing and externalizing disorders in children (Reynolds &

Kamphaus, 1992). The BASC is a standardized measure with 12 clinical scales: Atypical

behaviors, Aggression, Anxiety, Attention Problems, Depression, Conduct Problems, Behavioral

Problems, Hyperactivity, Social Skills, Leadership Abilities, Adaptability, and Somatization.

The BASC can be used with children ages 2 to 21 and takes approximately 10 to 20 minutes for

parents to complete. All BASC scores were standardized using T-scores with a mean of 50 and

standard deviation of 10 (Reynolds & Kamphaus, 2002). T-scores between 60 and 70 are

considered clinical precursors and should be evaluated with caution while T-scores over 70 are

characterized as clinically significant (Reynolds & Kamphaus, 2002).

The psychometric properties of the BASC have been widely evaluated. Reynolds and

Kamphaus (2002) report test-retest correlation coefficients of .91, interrater reliability of .80, and

internal consistency coefficients of .89. Convergent Validity with the CBCL (Achenbach, 1991)

was shown to be .81 (Reynolds & Kamphaus, 2002).

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43

Behavior Assessment Scale for Children-Self Report Rating Scale (BASC-SRS): The

Behavior Assessment Scale for Children-Self Report Scale® (Pearson Assessment Group,

Minneapolis, MN, www.pearsonassessment.com) consists of 186 items for children ages 12 to

18 and 152 items for children ages 8 to 11. The BASC is compromised of 3 composite scales

and 14 clinical scales and takes approximately half hour to forty-five minutes to complete

depending on reading ability. The standardized scoring practices for the self-report form are

similar to those described for the parent-rating form.

The psychometric properties of the BASC have been widely discussed. Reynolds and

Kamphaus (2002) describe average internal consistency for both the composite (.84) and clinical

scales (.76). Interrater reliability and convergenty validity were also showed to acceptable

(Reynolds & Kamphaus, 2002).

Behavior Assessment for Children-Teacher Rating Scales (BASC-TRS): The Teacher

rating form® (Pearson Assessment Group, Minneapolis, MN, www.pearsonassessment.com)

was designed as a standardized means for evaluating children ages 6 to 18. The Teacher Rating

Scale consists of 138 items for children ages 6 to 11 and 138 items for children ages 12 to 18. In

addition to the external, internal, and adaptive composites found on the Parent Rating Scale, the

Teacher Rating Scale also provides a school composite. The Teacher Rating Scale utilizes the

same scoring system outlined for the Parent and Self-Report scales.

Numerous factor analytic studies have provided support for the BASC (see Reynolds &

Kamphaus, 2002). Similarly, the internal consistency for the adaptive (.80) and the school (.90)

composite scales has been shown to be average (Weis & Smenner, 2007).



44

Data Analyses

Structural Equation Modeling: Structural equation modeling (SEM) was used to test the

four-factor WISC model. Specifically, a subclass of SEM, confirmatory factor analysis was used

to test the WISC-III four-factor model. Confirmatory factor analysis has been widely used to

evaluate the factor structure of psychological instruments such as the WISC-III in the past

(DeVellis, 1991; Dunn, Everitt, & Pickles, 1993; Hu & Bentler, 1999). When conducting

confirmatory factor analysis it has been traditional to assess model fit. During the present study,

several fit-indices were used to assess "goodness-of-fit." In particular, three types of fit-indices

were utilized to evaluate model fit for the WISC-III four-factor model in the current study (i.e.,

Absolute Fit Indices, Relative Fit Indices, and Parsimonious Fit indices) (see Tanaka, 1993 for

this discussion). As outlined by Tanaka (1993), an Absolute Fit Index is derived from a series of

complex comparisons between the observed and proposed variance and covariance matrices. A

non-significant chi-square suggests that a model's reproduced variances and covariances do not

differ substantially from the observed data. While originally the standard for assessing model fit,

the utility of the chi-square statistic has come under considerable scrutiny as of late (Hu &

Bentler, 1999). One of the primary concerns that has been raised is the notion that the chi-square

statistic cannot be solely relied upon given that it known susceptibility to sample size (i.e., large

samples can create type 1 error; small sample sizes can create type II error) which could result in

a false positive rejection of adequate models. Thus, when conducting confirmatory analyses it is

also important to evaluate other Absolute Fit indices such as the Standardized Root Mean

Residual (SRMR) and the Relative Fit Index (RFI) (Tanaka, 1993). The RFI, for example,

produces a comparison between the proposed model and the null hypothesis (i.e., independence

model) which is based on the assumption that the model has no latent variables (Tanaka, 1993).

45

When evaluating the output, Tanaka (1993) has suggested that the independent model should

always produce a chi-square statistic that approximates 1.0. In the present study, statistics such

as Bentler-Bonett Normed-Fit Index will be used to evaluate RFI. Lastly, the present study will

evaluate Parsimonious Fit Indices (e.g., PNFI) which produces larger values for less complex

models. Additional fit indices such as the comparative fit index (CFI) will also be provided. Fit

indices values ranging from approximately .08 to .94, respectively are indicative of good fit.

Conversely, for fit indices such as the RMSEA, values ranging from .00 to .06 should be

considered a good fit.

Cluster Analysis: Cluster analysis is an exploratory multivariate statistical procedure

which is used to form groups of cases from individual variables (Funk, Ives, & Dennis, 2006) in

such a way that within group similarities (clusters) are maximized while between group

differences are minimized (Donders, 1996). Donders (1997) suggests approaching cluster

analysis as a two stage process in which mean scores from initial hierarchical cluster analyses are

used as "seeds" or starting points for further confirmatory k-means cluster analyses. Hierarchical

cluster analysis is predicated on concepts such as "distance" (which measures how far apart

objects are) and "similarity" (which measures how similar two objects are) (Norusis, 2006).

Hierarchical cluster analysis (which is characterized as "tree-clustering") can be conducted as

either an agglomerative (in which each case starts as a separate cluster) or divisive (in which

each object starts in a single cluster) process and can be computed using either "cases" or

"variables;" to run the analyses using "cases" an alpha numeric string variable must first be

created to allow for the clustering or label procedure. In the present study, an agglomerative

approach with individual cases was used. Once decided upon, an appropriate linkage method

(i.e., single linkage, complete linkage, or average linkage) must be identified. Control for within

46

cluster variance must also be considered; a number of researchers have supported the use of the

Ward's Cluster Method (see Donders, 1996; Donders & Warschausky, 1997; Funk, Ives, &

Dennis, 2006; Wiegner & Donders, 1999). For hierarchical cluster analysis it is essential to

ensure that data from different scales of measurement is standardized (Everitt et al., 2001) before

clustered. If data is not appropriately standardized, distances created by larger variables will

artificially affect the clustering process and skew the output. In the present study,

standardization of the variables was not necessary because each of the cases was evaluated using

the same scale of measurement. Finally, a range of possible cluster solutions must be identified;

based on a review of previous research in this area (see Donders, 1996) SPSS was set to examine

the 2, 3, 4, and 5 cluster solutions. Hierarchical analyses were conducted for both the TBI and

ADHD groups independently.

With the analysis conducted, SPSS (2006) provides a descriptive output of each of the

separate cluster levels. According to Norusis (2006), "dissimilarity measures with small

coefficients tell you that fairly homogenous clusters are being attached to each other. Large

coefficients tell you that you're combining dissimilar clusters. If you're using similarity

measures, the opposite is true: large values are good, while small are bad." The statistical

program SPSS (15.0) constructed a dendrogram plot which provided an additional visual

representation (at which stages individual clusters are combined) of the cluster solutions. A

comparison of the individual cluster means for each solution was also conducted to help identify

the clinical significance of using one cluster solution over another. Finally, given the absence of

strict statistical criteria for evaluating the strength of one solution over another (e.g., chi-square)

at this initial phase of clustering (George & Mallery, 2006) the findings from the Ward's Method

will be compared with the output from another cluster method (e.g., between-group, within-

47

group linkage) to provide additional support (validation) for the observed solution (Jacobus

Donders, electronic email, April 20, 2007).

As suggested by Donders (1996), upon completion of the hierarchical analyses a k-means

cluster analysis was conducted to further evaluate the parameters of the individual clusters. The

k-means cluster analysis (non-hierarchical cluster analysis) is a confirmatory process in which

the number (i.e., k) of cluster solutions to be evaluated was chosen a priori (based on empirical

evidence from the hierarchical cluster analysis). However, before a k-means cluster analysis can

be conducted, cluster centers (centroids) or starting seeds need to be inputted into the statistical

program. In the present study, mean scores for each variable were used as "seeds" for the cluster

solutions. One of the primary differences between the k-means cluster analysis and the

hierarchical cluster analysis is that in the former, individual samples are not restricted to a single

cluster throughout the entire process, but rather, move from cluster to cluster until the most

appropriate fit is identified (Norusis, 2006, ). After the statistical program completes the

analyses and evaluates the fit between each variable, cases are shifted (i.e., moved between

clusters) appropriately. After the first iteration, a second iteration using the new mean scores

from the first k-means analysis is conducted. This process is repeated until the mean scores no

longer demonstrate change. An a priori decision was made to follow Donders (1996)

recommendations to set the convergence criterion level (in SAS this is referred to as semipartial

R2) at .05 to prevent variables from being combined that do not account for at least 5% of the

variance. Upon completion of the k-means cluster analysis, results may be saved and important

group differences (e.g., gender, age, and ethnicity) can be evaluated with univariate and

multivariate analyses.

As suggested (see Cronk, 2006; George & Mallery, 2006; Smith, Budzeika, Edwards,

48

Johnson, & Bearse, 1986 for this discussion), a number of data management procedures were

conducted to identify missing data, outliers, and coding mistakes. Deviations from normality

and problems with skewness and kurtosis were also evaluated before the data was analyzed. For

skew and kurtosis, values greater than 3 and 10, respectively, were considered problematic

(Chou & Bentler, 1995; Kline, 1996). The common procedure to replace missing values "by the

mean (or average) value of all other values for that variable" was utilized in the present study

(George & Mallery, p. 49, 2006). Less than 15% of the aggregate data was replaced this mean

distribution method. Moreover, as recommended (see George & Mallery, 2006), participants

with more than 15% missing data were excluded from the present study.

49

CHAPTER 3

RESULTS

Preliminary Analyses

For the overall group, ages ranged from six years, zero months to sixteen years, eleven

months with almost a three year difference between the mean ages of the TBI (M = 11.39) and

ADHD (M = 8.63) groups. In the TBI sample, the mean age for males (n = 72, M = 11.48) was

higher than for females (n = 51, M = 11.27); in the ADHD sample the mean age for females (n =

46, M = 9.27) was higher than for males (n = 24, M = 8.75). A non-significant chi-square

suggested that gender and diagnosis were independent of one another (x2

(1, N = 193) =.97, p =

.325). A chi-square analysis evaluating ethnic differences by diagnosis was conducted although

demographic information regarding ethnicity was only available for approximately 60% of the

total sample (75% for the TBI group & 25% for the ADHD group). A non-significant chi-square

suggested that ethnicity and diagnosis were independent of one another (x2(3, N = 115) = .787, p

= .675). Among those who reported on ethnicity, approximately 60% were Caucasian (n = 70),

23% were African-American (n = 27), and 15% were Mexican-American/Hispanic (n = 18). For

the total sample, ethnicity and gender were not independent of each other (x2 (2, N = 115) = 8.46,

p = .01). In the TBI sample, approximately 44% of the participants were Caucasian, 19% were

African-American, and 12% were Mexican-American while in the ADHD sample approximately

23% of the sample was Caucasian, 6% was African-American, and 6% was Mexican-American

(see Table 1 for a complete description of demographic information). Approximately 85 percent

of the total sample reported being right handed. No significant difference between handedness

and gender was observed (x2 (2, N = 190) = .336, p = .845).

50

For those for whom Glasgow Rating scores were available (n = 89), scores ranged from 3

(severe) to 12 (moderate) (M = 7.15, SD = 3.02). An independent t-test analysis comparing the

Glasgow Coma Scale scores of males (M = 6.96, SD = 3.26) and females (M = 7.41, SD = 2.68)

found no significant difference between the means of the two groups (t (87) = -.680, p > .05).

Levene's test for Equality of Variances indicates variances for males and females did not differ

significantly from each other (note: p = .343). An analysis of variance was conducted to evaluate

Glasgow Coma Scale scores by ethnicity. A statistically significant difference was observed

(F(2,64) = 5.81, p = .05). A post-hoc analysis showed Glasgow Rating scores were statistically

lower for Caucasian participants (M = 6.30, SD = 3.14) than African-American participants (M =

9.29, SD = 1.59). A two-way ANOVA showed no statistically significant interaction between

ethnicity and gender for Glasgow Rating scores (F(2,61) = 4.53, p = .638).

For children with a history of head injury, length of coma ranged from three days to thirty

nine days (M = 7.44, SD = 5.55). An independent t-test analysis comparing the length of coma

for males (M = 7.67, SD = 5.64) and females (M = 7.12, SD = 5.45) found no significant

difference between the mean scores for these two groups (t (121) = .539, p > .05). Levene's test

for Equality of Variances indicates variances for males and females do not differ significantly

from each other (note: p = .586). An analysis of variance was conducted to evaluate length of

coma by ethnicity. A statistically significant difference was not observed (F(2,88) = 1.03, p =

.36). A two-way ANOVA showed no statistically significant interaction between ethnicity and

gender for length of coma (F(2,91) = 2.12, p = .116). When length of coma was partitioned into

two separate groups (i.e., coma < 1 week & coma > 1 week) still no statistical differences

between males and females were observed with regard to length of coma.

51

Within the TBI group (n = 50), the BASC Parent Rating Form was used to evaluate levels

of internalizing and externalizing problems. A one-way MANOVA was conducted to evaluate

the effect of gender on the clinical scales. Results identified no significant effect for gender

(F(14,35) = .579, p = .863). As is shown in Table 2, parents rated males higher than females on

the externalizing problems such as hyperactivity, aggression, and conduct problems. The same

table shows that parents also reported that males experienced greater problems with attention and

adaptability when compared with females. In contrast, parents rated females higher than males

on internalizing problems such as anxiety, depression, somatic complaints, and withdrawn

behaviors (see Table 2). Parents also reported that females with head injuries exhibited more

atypical behaviors than males.

A one-way MANOVA was also conducted to evaluate the effect of ethnicity on the

BASC clinical scales. No significant effect for ethnicity was observed (F(14,26) = .61, p = .90).

A two-way MANOVA examining gender by ethnicity was conducted. No significant interaction

was observed (F(14,23) = .90, p = .61).

A Pearson bivariate correlation was conducted to examine the relationship between the

BASC clinical scales and age, length of coma, and Glasgow Rating (see Table 3). For children

with TBI, no significant correlation between age and the BASC scales was observed. With the

same sample, a small correlation between length of coma and aggression (r(97) = .24, p < .05),

anxiety (r(97) = .34, p < .05), atypical behaviors (r(97) = .20, p < .05), and attentional problems

(r(97) = .23, p < .05). No significant correlation between Glasgow Coma Scale scores and the

BASC scales was observed. The bivariate correlations for the BASC clinical scales are

presented in Table 3.

52

Within the ADHD group (n = 50), the BASC Parent Rating Form was used to evaluate

levels of internalizing and externalizing problems, particularly with regard to inattention and

hyperactivity. A one-way MANOVA was conducted to evaluate the effect of gender on the

clinical scales. Results identified no significant effect for gender (F(14,35) = .690, p = .769). As

is shown in Table 2, parents rated males higher than females on the aggression, conduct

problems, depression, and the externalizing disorders scales. In contrast, parents rated females

higher than males on the anxiety, somatization, withdrawn, social skills, and internalizing

disorders scales (see Table 2). According to parent ratings, boys in the current study exhibited

clinically significant levels (i.e., T-score >60) of hyperactivity, aggression, conduct problems,

and attentional problems. In comparison, parents reported that the females in the current study

only exhibited clinically significant levels of hyperactivity and attentional problems. Of those in

the ADHD group for whom BASC rating scores were available, only 17 provided information

about ethnicity. A one-way ANOVA conducted to evaluate the effect of ethnicity on BASC

scores was non-significant (F(14,2) = 1.34, p = .43).

A Pearson bivariate correlation was conducted to examine the relationship between the

clinical scales and age (see Table 4). For children with ADHD, a small clinically significant

correlation between age and the attention problems index from the BASC was observed (r(54) =

.290, p < .05). This seemed to suggest that older children in the study exhibited greater

attentional problems when compared with younger children. In contrast, a small negative

correlation between age and the aggression (r(54) = -.282, p < .05) and conduct problems (r(54)

= .22, p < .05) scales was observed. This suggested that younger children were more likely to

exhibit problems with aggression and conduct problems than older children. The bivariate

correlations for the BASC clinical scales are presented in Table 4.

53


Confirmatory Factor Analysis (CFA) was used to evaluate the proposed WISC-III four-

factor model for both the TBI and ADHD samples. In both samples, confirmatory factor

analysis was also used to evaluate a proposed three factor model for which there has been

considerable theoretical speculation with little empirical validation. Strong support for the four-

factor model was observed for both groups, although the fit indices were higher for the ADHD

group than the TBI group (see Table 5).

WISC-III Four-Factor Model (TBI Sample)

For the four-factor model, the chi-square statistic was non-significant for the TBI group

(x2(48) = 49.71, p = .40), suggesting a good fit for the proposed model (Stapleton, 1997).

Additional Relative Fit Indices (RFI) (e.g., NFI = .93) and non-centrality-based indices (e.g., CFI

= .99, RMSEA = .02) for the TBI sample suggested the model fit the data well (Hu & Bentler,

1999). Parsimonious Fit Indices were within acceptable limits for the TBI sample (PNFI = .57).

As shown in Table 6, the factor loadings for the four-factor model ranged from moderate

(Digit Span = .56) to very strong (Symbol Search = .89). Findings from this study found the

Verbal Comprehension and the Perceptual Organization indices to be well defined. The factor

loadings for the Freedom from the Distractibility index were not as strong as those cited in

previously published reports (Donders & Warchausky, 1996), although factor loadings for the

processing speed factor were found to be commensurate with prior findings.

A visual representation of the four factor model is presented in Figure 1. As noted, the

correlations for the latent factors ranged from moderate to strong (see Figure 1). Covariance was

found to be strongest among the Verbal Comprehension and Freedom from Distractibility indices

and the Perceptual Organization index and Freedom from Distractibility, respectively.

54

WISC-III Three-Factor Model (TBI Sample)

For the three-factor model, the chi-square statistic was non-significant for the TBI group

(x2(51) = 60.1, p = < .05), suggesting a good fit for the proposed model (Stapleton, 1997).

Additional Relative Fit Indices (RFI) (e.g., NFI = .92) and non-centrality-based indices (e.g., CFI

= .99, RMSEA = .04) for the TBI sample suggested the model fit the data well (Hu & Bentler,

1999) (see Table 5). Parsimonious Fit Indices were within acceptable limits for the TBI sample

(PNFI = .57). However, a chi-square difference test (i.e., subtracting the four-factor model's Df's

and chi-square from the three-factor model's Df's and chi-square value) indicated that the four-

factor model provided significantly better fit, compared to the three-factor model, (x2(3) = 10.39,

p < .05). Therefore, the four-factor model appears to provide the best representation of the latent

intellectual processes which underlie performances on the WISC subscales in the TBI group.

WISC-III Four-Factor Model (ADHD Sample)

The model for the ADHD group also fit the data well. Specifically, a non-significant chi-

square statistic (x2(48) = 45.75, p = .57) suggested a good fit for the proposed model (see Table

5). Additional Relative Fit Indices (RFI) (e.g., NFI = .89) and non-centrality-based indices (e.g.,

CFI = 1.00, RMSEA = .00) were more variable for the ADHD sample, although an adequate

level of fit was generally observed (Hu & Bentler, 1999). Parsimonious Fit Indices were also

within acceptable limits for the ADHD sample (PNFI = .50).

The factor loadings for the four-factor model ranged from low (Symbol Search = .38) to

very strong (Arithmetic = 1.02). Findings found the Verbal Comprehension and the Perceptual

Organization indices to be well defined. This was consistent with previous evaluations of the

model fit for children with ADHD (Schmean et al., 1993). In comparison, the factor loadings for

55

the Freedom from the Distractibility and the Processing Speed indices were less robust (see

Table 6).

Covariance for the latent factors was observed as less robust for the ADHD sample in

comparison to the TBI sample. However, covariance coefficients for the Verbal Comprehension,

Freedom from Distractibility, Perceptual Organization, and Processing Speed indices were

within acceptable levels (see Figure 2).

WISC-III Three-factor Model (ADHD Sample)

In the past, investigators postulated that a three-factor model may best explain the

correlation between the observed WISC-III variables and the underlying latent factors (e.g.,

Sattler, 1992); however, findings have been variable. To evaluate the strength of this theory, a

confirmatory factor analysis of the three-factor model was conducted for ADHD sample.

Although non-significant chi-square statistics were found for this model (x2(51) = 63.47, p =

.13), a review of the fit indices (see Table 5) showed the data did not fit as well for the three

factor model when compared with the four-factor model (NFI = .75, CFI = .93; RMSEA = .06,

PNFI = .49). The chi-square difference test indicated that the four-factor model provided

significantly better fit, compared to the three-factor model, (x2 (3) = 17.72, p < .01). Therefore,

the four-factor model appears to provide the best representation of the latent intellectual

processes which underlie performance on the WISC subscales in the ADHD group.

Description of WISC-III Performances by Group

TBI Sample

With regard to the factor indices for the TBI sample, the Verbal Comprehension Index

scores ranged from 56 to 123 (M = 86.84, SD = 13.70), Perceptual Organization Index scores

ranged from 50 to 124 (M = 84.78, SD = 15.93), Freedom from Distractibility Index scores

56

ranged from 61 to 137 (M = 91.26, SD = 13.27), and Processing Speed Index scores ranged from

50 to 124 (M = 85.59, SD = 15.89). The mean for the Full Scale (FSIQ), Verbal IQ (VIQ), and

Performance IQ (PIQ) are presented in Table 7.

A one-way repeated measures ANOVA was conducted to evaluate within-group

differences of the WISC-III factors scores the TBI sample. For the TBI group, a statistically

significant difference was observed between VIQ and PIQ scores. Specifically, a review of the

mean scores showed that on average children with head injuries demonstrated greater sustained

verbal functioning (M = 87.33, SD = 14.36) than non-verbal functioning (M = 83.53, SD =

16.12).

A repeated measures ANOVA also found a significant difference between the four factor

index scores for the TBI group (F(3,118) = 10.78, p = .00). Follow-up Bonferroni post-hoc

analyses showed that children with head injuries demonstrated significantly higher scores on the

Freedom from Distractibility (M = 91.26, SD = 13.27) index when compared with scores from

the Verbal Comprehension (M = 86.84, SD = 13.70), Perceptual Organization (M = 84.78, SD =

15.93), and Processing Speed (M = 85.59, SD = 15.89) indices. Gender effects were also

examined with this procedure. No statistically significant effect for gender was observed within

the TBI sample (F(3,117) = 2.53, p = .06). For males, scores ranged from a low of 83.59 for the

Processing Speed Index to a high of 91.62 for the Freedom from Distractibility Index (see Table

7). For females, scores ranged from a low of 83.91 for the Perceptual Organization Index to a

high of 90.75 for the Freedom from Distractibility Index (see Table 7).

ADHD Sample

For the ADHD sample, the Verbal Comprehension Index scores ranged from 60 to 124

(M = 94.19, SD = 12.17), Perceptual Organization Index scores ranged from 57 to 131 (M =

57

95.78, SD = 13.33), Freedom from Distractibility Index scores ranged from 57 to 121 (M =

91.36, SD = 12.36), and Processing Speed Index scores ranged from 58 to 122 (M = 94.60, SD =

11.58). Mean scores for the Full Scale IQ, Verbal IQ, and Performance IQ are presented in

Table 7.


differences for the ADHD group. A statistically significant difference between the VIQ and PIQ

indices was observed (F(1,69) = 3.176, p = .04). Specifically, children with the primary

diagnosis of ADHD had statistically higher scores on the non-verbal index (M = 96.39, SD =

14.14) than the verbal index (M = 93.51, SD = 12.64).

A repeated measures ANOVA found no significant difference between the four factor

index scores for the ADHD group (F(3,67) = 1.945, p = .13). However, among the four

individual factor indices a follow-up post-hoc analysis showed that children with ADHD

obtained significantly lower scores on the Freedom from Distractibility Index (M = 91.36, SD =

12.36) when compared with scores from the Perceptual Organization Index M = 95.78, SD =

13.33). No statistically significant effect for gender was observed (F(3,66) = 1.301, p = .281).

For males, scores ranged from a low of 92.20 for the Freedom from Distractibility Index to a

higher of 96.71 for the Perceptual Organization Index (see Table 7). For females, scores ranged

from a low of 89.74 for the Freedom from Distractibility Index to a high of 96.37 for the

Processing Speed Index (see Table 7).

TBI-ADHD Group Comparisons

Bivariate correlations were separately conducted on the TBI and ADHD samples to

examine the relationship between the four WISC-III factors (see Tables 8). Correlation

coefficients were also calculated to evaluate the relationship among the WISC-III factor index

58

scores and the WISC-III subtests for both the TBI and ADHD samples (see Tables 9).

Additionally, correlations were conducted to evaluate the relationship between the WISC-III

factors and age for both the TBI and ADHD groups (see Table 10). For the TBI sample,

correlation analyses were also conducted to examine the relationship between the WISC-III

factor index and individual subtest scores and length of coma and Glasgow Coma Scale (see

Tables 10 & 11).

As can be seen in Table 11, correlation coefficients among the twelve individual subtests

for the TBI sample ranged from weak to moderately strong (r = .26 to r = .75). As expected,

individual subtest correlations were highest among intra-factor subtests and lowest among inter-

factor subtests (i.e., Verbal Comprehension, Freedom from Distractibility, Perceptual

Organization, and Processing Speed).

As shown in Table 9, one can see how the subtest scores correlated with the respective

WISC index factors (VC, PO, FD, PS). In this table there are notable differences in the pattern

of correlations between the TBI and ADHD groups. Specifically, in the TBI group the different

subtests are more significantly inter-related to the four factor indices, compared to the ADHD

group. As such, this differential pattern of correlations between the groups could suggest that the

cognitive processes which underlie performance on the WISC subtests are too inter-related in the

TBI group, or that the inter-relations among various cognitive processes are too weakly

associated in the ADHD group. Of course, the modeling results are also consistent with the

pattern of manifest variable correlations. WISC correlations between the TBI and ADHD group

suggests that in the TBI group one process such as freedom from distractibility is moderately

related to verbal reasoning, while this same latent correlation is weaker in the ADHD group.

59

Individual factor index scores for factors that compromise the verbal intelligence scale

(VIQ) showed higher correlation with VIQ than with the two factors from the performance

intelligence scale (PIQ), and similarly for those factor index scores that compromise PIQ (see

Table 10). The correlation between the higher order Verbal IQ scale and Performance IQ scale

was moderately strong (r = .64). Overall, reliability coefficients (internal consistency) for the

TBI sample were considered adequate and ranged from moderate to very strong (r = .59 to r =

.88) (see Table 12). The aggregate of these findings were consistent with previous research

(Donders, 1996) and results published in the WISC-III manual (Wechsler, 1991). However,

internal consistency was poor for some of the WISC subtests for the ADHD group, especially

processing speed for males (see Table 12).

In comparison, correlation coefficients among the twelve individual subtests were less

robust for the ADHD sample and ranged from very weak to moderately strong (r = .01 to r = .69)

(see Table 13). Similar to TBI sample, individual subtest correlations for the ADHD sample

were highest among intra-factor subtests and lowest among inter-factor subtests and again no

individual subtest correlated higher with any other factor that the one it was purported to load on

to (see Table 9). For the ADHD sample, individual factor index correlations ranged from

moderate to very strong (r = .06 to r = .75), again intra-factor correlations were shown to be

stronger than inter-factor correlations (i.e., VCI & FD and POI & PSI). The correlation between

Verbal IQ scale and Performance IQ scale was moderate (r = .49) (see Table 8). Again, the

reliability coefficients for the ADHD sample were less robust when compared with the TBI

sample, ranging from weak to very strong (r = .29 to r = .80) (see Table 12).

When other variables were considered, a small correlation (r(122) = -.20, p < .05)

between length of coma and the Freedom of Distractibility Index was observed for the TBI

60

sample. No other significant correlation between length of coma and the remaining WISC-III

factor index scores was observed. Similarly, no significant correlation between length of coma

and performance on any of the WISC-III individual subtests was observed.

A significant correlation (r(88) = .29, p < .01) was observed between Glasgow Coma

Scale and the Processing Speed Index, indicating that children with high Glasgow Scores

perform better on this index when compared with children with low Glasgow Scores (see Table

10). An evaluation of the individual subtests scores showed relatively small correlations for

Glasgow Scale scores (see Table 11). In terms of processing speed, a small correlation (r(88) =

.30, p < .01) between Glasgow Coma Scale scores and the Coding subtests was observed. A less

robust correlation (r(88) = .212, p < .05) between Glasgow Coma Scale and the Symbol Search

subtest was observed. Thus, the correlations in this section suggest that there were significant

associations between length of coma and severity of Glasgow ratings and the WISC index of

poorer processing speed (and to a lesser extent freedom from distractibility).

Additional WISC-III TBI & ADHD Sample Comparisons

A Pearson correlation was also conducted to examine the relationship between the four-

factor indices from the WISC-III and the clinical scales from the BASC for both the TBI and

ADHD samples. For the TBI sample, a significant correlation between the Verbal

Comprehension Index and the Hyperactivity (r(95) = -.253, p = < .01), Depression (r(95) = -

.223, p = .03), Atypical Behavior (r(95) = -.22, p = .03), Attention Problems (r(95) = -.284, p = <

.01), and Social Skills (r(95) = .236, p = < .02) scales was observed (see Table 14). Within the

same sample, a significant correlation between the Perceptual Organization Index and Atypical

Behaviors (r(96) = -.207, p = .04) and Attentional Problems (r(98) = -.267, p = < .01) scales was

observed. A statistically significant correlation was also observed between the Freedom from

61

Distractibility Index and the Hyperactivity (r(96) = -.237, p = .02), Attention Problems (r(98) = -

.363, p = < .01), and Social Skills (r(96) = -.211, p = .04) clinical scales for this group. For the

Processing Speed Index, only a significant correlation was noted for the Attention Problems

(r(98) = -.235, p = .02) scale within this sample.

Fewer positive correlations between the WISC-III factors and the BASC clinical scales

were identified within the ADHD sample (see Table 15). As shown in Table 15, a positive

correlation between the Adaptability (r(50) = .307, p = .03) and Attention Problems (r(50) = .16,

p < .05) scales and Perceptual Organization was found. For the same sample, the Freedom from

Distractibility Index was positively correlated with the Hyperactivity (r(54) = .337, p = .01) and

negatively Conduct Problem (r(54) = -.270, p = .04) scales from the BASC.

A Pearson correlation was also conducted to evaluate the relationship between the WISC-

III factor indices and the external validation variables for both the clinical samples. As shown in

table 16, the Verbal Comprehension Index from the WISC-III was significantly correlated with

Trail Making Test (Part B) (r(88) = .42, p < .05), Children's Category Test (r(101) = .40, p <

.05), TOMAL (letters Backwards subtests) (r(93) = .25, p < .05), and the hit rate index from the

Continuous Performance Test (r(81) = -.27, p < .05) for the TBI sample. Except for the addition

of the Trail Making Test (Part A) (r(88) = .29, p < .05) these same measures were again

significantly correlated with Perceptual Organization Index (see Table 16). Again, with the

exception of the Trail Making Test (Part A) which was only found to have a small positive

correlation with the Processing Speed index, a similar pattern of variable correlation was also

noted for the Freedom from Distractibility and Processing Speed indices (see Table 16).

For the ADHD sample, fewer correlations between the external validating variables and

the WISC-III factors were observed (see Table 17). Specifically, a positive correlation between

62

the Verbal Comprehension Index and the Children's Category Test (r(65) = .36, p < .05) was

found. A positive correlation was also observed between the Perceptual Organization Index and

the Children's Category Test (r(65) = .36, p < .05) within this sample. No significant correlation

between the remaining WISC-III indices (i.e., Freedom from Distractibility & Processing Speed)

and the validating variables was observed.

Multivariate Analysis of the WISC-III

Before univariate and multivariate statistical analyses were conducted, WISC-III data for

TBI and ADHD samples were independently examined for non-normality and missing variables.

Criteria proposed by West, Finch, & Currant (1995) were applied to evaluate the WISC-III

scores for the TBI, and ADHD samples for skewness and kurtosis. Findings showed the data

was neither skewed nor kurtotic, suggesting the data was normally distributed. Due to the

observed disparity in response rates for ethnicity (n = 91 TBI group, n = 24 ADHD group) and

gender (n = 121 TBI group, n = 70 ADHD group) within the two clinical samples, the effect of

these two variables on factor index scores was evaluated separately.

Gender

A two-way MANOVA (gender x diagnosis) was calculated to examine the effects of

gender (male, female) and diagnosis type (TBI, ADHD) on factor index scores. A significant

effect was found for gender (Lambda(4,184) = 2.61, p = .04). As shown in Table 18, males

received higher scores on the Verbal Comprehension, Perceptual Organization, and Freedom

from Distractibility indices while in contrast females received higher scores on the processing

speed index in comparison to males. A significant effect was also found for diagnosis

(Lambda(4,184) = 9.29, p < .01). Follow-up univariate analyses specifically showed a

statistically significant effect for diagnosis and Verbal Comprehension (F(1, 187) = 11.57, p =

63

.001), Perceptual Organization (F(1, 187) = 20.63, p = .000), and Processing Speed (F(1, 187) =

16.11, p = .000). A comparison of the mean scores for each of the groups showed children with

an ADHD diagnosis scored higher on each of the aforementioned indices than children with a

TBI diagnosis.

No significant interaction effect between diagnosis (TBI, ADHD) and gender (male,

female) was observed for WISC-III factor index scores (Lambda(4,184) = .067, p = .992).

Ethnicity

Given the low rate of reporting for ethnicity, a separate two-way MANOVA (ethnicity x

diagnosis) was conducted to evaluate the effect of ethnicity (Caucasian, African-American,

Mexican-American) and diagnosis (TBI, ADHD) on WISC-III factor scores. Although no

significant interaction effect was observed (F(8,214) = 1.24, p = .277), a significant effect for

ethnicity was found (F(8,214) = 4.23, p = 000). Follow-up univariate analyses specifically

showed a statistically significant effect for ethnicity and the Verbal Comprehension (F(2, 115) =

5.54, p = .005), Perceptual Organization (F(2, 115) = 5.67, p = .005), and Freedom from

Distractibility (F(2, 115) = 5.17, p = .007) indices. Post-hoc analyses showed Caucasian (M =

91.38, SD = 14.79) participants received higher scores on the Verbal Comprehension Index in

comparison to African-American (M = 80.33, SD = 10.90) and Mexican-American (M = 84.08,

SD = 12.79) participants. For Perceptual Organization, findings showed that Caucasian (M =

88.42, SD = 16.70) and Mexican-American (M = 87.96, SD = 12.94) participants received higher

scores than African-American (M = 77.78, SD = 13.59) participants. In terms of Freedom from

Distractibility, follow-up analyses showed that Mexican-American (M = 94.79, SD = 12.97) and

Caucasian (M = 91.65, SD = 13.77) participants received higher scores than African-American

(M = 85.61, SD = 11.75) participants. With regard to the Processing Speed Index, Mexican-

64

American participants (M = 91.38, SD = 14.79) received higher scores than both Caucasian (M =

85.62, SD = 15.62) and African-American (M = 86.18, SD = 12.04) participants. However, these

findings should be interpreted cautiously given the noted disparity in ethnicity.

Age

As previously noted, only a few small correlations between age and WISC-III factor

index scores were observed with bivariate analyses (see Table 10). Some researchers have

argued that there may be some utility in studying traumatic brain injury within the conceptual

framework of critical biological developmental periods (Kolb & Whishaw, 1990). Therefore, to

augment the findings listed previously, the variable "age" was transformed into a new categorical

variable from which development periods (i.e., 6 to 8 years old, 8 years to 11 years old, 12 to 16

years old) could be studied using analysis of variance techniques. A one-way MANOVA was

conducted to evaluate the effect of these developmental periods on the WISC-III factor index

scores for the TBI and ADHD samples. No statistically significant differences on the WISC-III

factor index scores were observed between the three distinct age groups for either group.

For the TBI group, Glasgow Rating scores (3 to 12) were collapsed into groups (i.e.,

severe and moderate) to further evaluate the effect of head injury severity on WISC-III factor

scores. Length of coma was also collapsed into two groups (coma < 6 days and coma > 6 days)

to further evaluate its effect on WISC-III factor scores. A two-way MANOVA (Glasgow Rating

Score x Length of Coma) was conducted to evaluate the interaction effect between these two

variables on WISC-III factor scores. Results showed no significant interaction effect between

length of coma and Glasgow Rating score on WISC-III factor scores (F(4,77) = .085, p = .142).

However, follow-up analyses showed a statistically significant interaction effect for Glasgow

Coma Scale scores and length of coma was observed for the Freedom from Distractibility factor

65

(F(1, 80) = 6.917, p = .01). This showed that among children who had had sustained a severe

head injury (i.e., Glasgow Score < 8) those who were in a coma for less than seven days (M =

95.70, SD = 12.65) performed significantly better on the Freedom from Distractibility Index than

children who were in a coma more than seven days (M = 81.24, SD = 14.06). This pattern was

consistent for each of the factor indices, although no significant interaction effects were

observed.

Additional TBI-ADHD Group Comparisons

The TBI and ADHD samples were evaluated on several additional neuropsychological

measures. Specifically, a series of two-way ANOVAs were conducted evaluate the effect of

diagnosis (TBI, ADHD) and ethnicity (Caucasian, African-American, Mexican-American) on

scores from the Trail Making Test, Children's Category Test, Continuous Performance Test, Test

of Memory and Learning, and Behavior Assessment Scale for Children. No significant main

effects or interaction effects were noted for these variables. A series of two-way ANOVAs were

also conducted to evaluate the effect of diagnosis (TBI, ADHD) and gender (male, female) on

scores from the Trail Making Test, Children's Category Test, Continuous Performance Test, Test

of Memory and Learning, and Behavioral Assessment Scale for Children. The individual results

for each instrument are delineated below.

Trail Making Test (TMT)

A two-way ANOVA conducted to evaluate the effect of diagnosis and gender on the Trail

Making Test (Part A & B). A statistically significant effect for diagnosis was observed for Part

A (F(1,115) = 6.123, p = .015). A comparison of the mean scores showed children with a

primary diagnosis of ADHD (M = 109, SD = 9.13) scored higher on the Part A of the Trail

Making Test than children with head injuries (M = 101.53, SD = 16.14). No significant effect for

66

gender was observed (F(1,116) = 1.27, p = .26). Similarly, no interaction effect between gender

and diagnosis was found (F(1,116) = .218, p = .64). A statistically significant effect for diagnosis

was observed for Part B (F(1,116) = 7.38, p = .008). A comparison of the mean scores showed

children with a primary diagnosis of ADHD (M = 110.41, SD = 8.58) scored higher on the Part B

of the Trail Making Test than children with head injuries (M = 98.50, SD = 22.64). No

significant gender differences were observed for Part B (F(1,116) = .263, p = .643). Similarly,

no interaction effect was observed (F(1,116) = .000, p = .999).

A one-way repeated measures ANOVA showed no significant difference between scores

on Part A (M = 101.53, SD = 16.14) and Part B (M = 98.50, SD = 22.64) of the Trail Making

Test for children with head injuries (F(1,87) = 1.698, p = .196). No gender effect was observed

(F(1,86) = .000. p = .986). A one-way repeated measures ANOVA showed no significant

difference between scores on Part A (M = 109.34, SD = 9.13) and Part B (M = 110.41, SD =

8.58) of the Trail Making Test for children with ADHD (F(1,27) = .271, p = .607). No gender

effect was observed (F(1,27) = .537, p = .470).

Children's Category Test (CCT)

A two-way ANOVA was conducted to evaluate the effect of diagnosis and gender on

CCT scores. A statistically significant effect for diagnosis was observed (F(1,165) = 4.06, p =

.04). A comparison of mean scores showed that children with head injuries (M = 44.09, SD =

11.83) received significantly lower scores on the CCT than children with a diagnosis of ADHD

(M = 48.12, SD = 8.56). No significant gender (F(1,165) = 2.25, p = .13) or interaction effects

(F(1,165) = .29, p = .59) were noted for this instrument.

67

Continuous Performance Test (CPT)


CPT scores (i.e., commissions, hit rate, attention scale). No statistically significant effect for

diagnosis was observed for commission scores (impulsivity) (F(1,134) = .462, p = .498).

Similarly, no gender (F(1,134) = .570, p = .451) or interaction (F(1,134) = .516, p = .474) effects

were noted for this index. No statistically significant effect was observed for the hit rate index

(mean response time) (F(1,136) = 1.50, p = .22). Similarly, no gender (F(1,136) = .327, p = .57)

or interaction (F(1,136) = .002, p = .96) effects were observed for this index. In contrast, a

statistically significant effect for the overall index (i.e., attention scale) was observed (F(1,136) =

7.377, p =.006). A comparison of mean scores showed children with head injuries received

significantly lower scores (M = 53.81, SD = 9.45) than children with ADHD (M = 58.17, SD =

12.59). No significant gender or interaction effects were noted for this index.

Behavioral Assessment Scale for Children (BASC)

A two-way MANOVA was conducted to evaluate the effect of diagnosis and gender on

BASC scores. A statistically significant effect for diagnosis was observed for the externalizing

factor which included the Hyperactivity, Aggression, and Conduct Problem scales (F(3,143) =

9.54, p < .00). Follow-up univariate analyses showed that children in the ADHD sample

received higher scores on the Hyperactivity (M = 67.37, SD = 16.06), Aggression (M = 60.59,

SD = 13.66), and Conduct Problem (M = 61.35, SD = 14.89) scales in comparison to the TBI

sample (Hyperactivity: M = 54.13, SD = 15.10; Aggression: M = 53.28, SD = 15.34; Conduct

Problems: M = 51.20, SD = 13.64). A statistically significant effect for gender was also

observed for these clinical scales (F(1,145) = 2.96, p = .03), although follow-up analyses only

identified a significant effect for gender on the aggression (F(1,145) = 2.96, p = .03) and conduct

68

problem (F(1,145) = 2.96, p = .03) scales. In both cases, males (Aggression: M = 58.50, SD =

15.48; Conduct Problems: M = 64.18, SD = 5.82) scored significantly higher when compared

with females (Aggression: M = 51.79, SD = 13.66; Conduct Problems: M = 56.55, SD = 12.05).

No interaction effect was observed (F(3,143) = .149, p = .93).

No statistically significant effect for diagnosis was observed among the internalizing

factor which included the Anxiety, Depression, and Somatic Complaint scales (F(3,144) = 1.60,

p < .19). Similarly, no gender (F(3,144) = 2.39, p < .07) or interaction effects were observed for

these scales (F(3,144) = .77, p < .51).

A two-way ANOVA evaluating the effect of gender and diagnosis on the clinical scales

(i.e., Withdrawn Behaviors & Attentional problems) was also conducted. A significant effect

was observed for diagnosis (F(2,147) = 1.95, p < .01), although a clinically significantly

difference was only observed for the attentional scale for which children with a primary

diagnosis of ADHD (M = 69.00, SD = 8.52) scored significantly higher than children with a head

injury (M = 58.63, SD = 11.36). No effect for gender was observed (F(2,147) = 1.95, p = .14).

A two-way ANOVA was conducted to evaluate the effect of gender and diagnosis on

social functioning. A significant effect for diagnosis was found (F(2,96) = 4.81, p = .01).

Follow-up analyses demonstrated a significant effect for diagnosis for both Adaptability (F(1,97)

= 7.95, p = .006) and Social Skills (F(1,97) = 8.78, p = .004). In both cases, children with a

primary diagnosis of ADHD (Adaptability: M = 38.14, SD = 8.56; Social Skills: M = 40.70, SD

= 9.09) scored lower than children with a head injury (Adaptability: M = 44.39, SD = 12.36;

Social Skills: M = 47.75, SD = 12.18). No significant gender (F(2,96) = .364, p = .69) or

interaction (F(2,96) = .33, p = .72) effects were observed.

69

Oral and Written Language Scales (OWLS)


OWLS receptive language scores. No statistically significant effect for diagnosis was observed

for receptive language (F(1,139) = .443, p = .51). No significant gender effects were noted for

this instrument (F(1,139) = .813, p = .737).

Cluster Analyses

As suggested by Donders and Warschausky (1997), a two-stage cluster analysis process

was conducted using factor index scores from the WISC-III separately for both the TBI and

ADHD samples as individual cases. As previously discussed, the Hierarchical cluster analyses

were conducted using the squared Euclidean distance with Ward's method. Second-stage

analyses consisted of a nonhierarchical k-means square cluster analysis for which cluster means

derived from the previous hierarchical analysis were used as the "seeding point" for the analyses.

Hierarchical Cluster Analysis for the TBI Sample

Based on previous research, a range of cluster solutions were evaluated for both statistical

and clinical significance. The mean scores for the two, three, and four cluster solutions are

presented in Table 19. As shown in Table 19, the two cluster solution was comprised of almost

equal sized groups, although cluster one, whose scores were all within one-half standard

deviations from the normative mean (M = 100, SD = 15) had slightly more participants than

cluster two, whose scores ranged from a standard and half deviation to more than two standard

deviations below the mean. For the three cluster group solution the pattern of WISC index

scores reflected one cluster with average scores (all within half a standard deviation from the

mean), one cluster with low average scores (which ranged from one and half standard deviations

to almost three standard deviations) and one cluster with below average scores (which ranged

70

from almost two standard deviations to more than three standard deviations). Among the three

clusters, the greatest variability among the factor indices was observed in cluster three. The

three cluster solution seemed to reflect groups that differed more quantitatively rather than

qualitatively. Cluster one and two had significantly more participants than cluster three for this

solution (see Table 19).

For the four cluster solution the cluster sizes remained the same, with the exception of

cluster one which was subsequently partitioned into two separate groups of generally average

range scores (see Table 19). Although the mean scores were higher in the newly identified

cluster, the difference between mean scores for the two new clusters was not observed as

statistically or clinically significant.

Given the absence of scoring variance within the two cluster solution and the scarcity of

valuable clinical and statistical information gained by a four cluster solution, a three cluster

solution was decided upon for additional k-means analyses.

K-means Cluster Analysis for the TBI Sample

The k-mean cluster analysis was conducted to further evaluate a three cluster solution.

As suggested, the mean scores derived from the previous hierarchical analyses were used as

starting points or "seeding points" for the k-means cluster analyses for each of the cluster

variables (see Table 19). After a single iteration, changes in cluster centers were .878, 1.247, and

2.770 for clusters one, two, and three, respectively. After a single iteration, cluster membership

shifted and two participants were removed from cluster two and placed into cluster three, raising

the membership total for cluster three from fourteen to sixteen. A second iteration was

conducted but mean scores for each group remained stable and no changes were observed for

any of the clusters. The final mean scores for each of the WISC-III factors are presented by

71

cluster in Table 20. Results showed a statistically significant difference between all of the

clusters was observed at the p = .000 level; although this finding should only be used for

descriptive purposes because the cluster analysis procedure was designed to maximize group

differences among the cases in different clusters and thus producing statistically significant

differences (SPSS, 2006).

A chi-square test of independence was conducted to determine if there was a gender

effect among the three clusters. Results from the chi-square analysis showed that the cluster

groups were composed of equal proportions of males and females (x2(2) = .90, p = .64).

Findings showed that approximately 45, 58, and 68 percent of the participants were male in

clusters 1, 2, and 3 respectively. A similar chi-square analysis was conducted to evaluate

ethnicity and cluster membership. The results showed significant group differences for ethnicity

and cluster membership (x2(4) = 11.10, p = .02). Within cluster one (n = 46), 69% of the sample

was Caucasian, 13% was African-American, and 17% was Mexican-American. Within cluster

two (n = 34), approximately 41% of the sample was Caucasian, 41% was African-American, and

17% was Mexican-American. In cluster three (n = 11), 73% of the sample was Caucasian and

27% of the sample was African-American. No Mexican-American participants were identified

within cluster three.

Analysis of Variance by Cluster Groups (TBI Sample)

As shown in Figure 3, observable differences between the clusters were identified with

regard to performance. A two-way (gender x cluster membership) MANOVA was conducted

with WISC-III factor indices. A statistically significant effect for gender was not observed (F(4,

112) = 1.362, p = .252). A significant effect for cluster membership was found (F(8,113) =

25.023, p < .01). No significant interaction effect was noted (F(8,113) = .420, p = .91).

72

Additional univariate analyses showed a significant effect for each of the four-factors. In terms

of Verbal Comprehension, follow-up Bonferroni post-hoc analyses showed that cluster one (M =

95.56, SD = 10.35) significantly differed from cluster two (M = 77.50, 8.84) and three (M =

73.40, SD = 9.36); no significant difference between clusters two and three were noted.

Approximately half of the variance was accounted for by cluster association in each index

(Verbal Comprehension σ2 = .51, Freedom from Distractibility σ2 = .43, Processing Speed σ2 =

.49) with the exception of the Perceptual Organization Index for which approximately two-thirds

(σ2 = .63) of the variance was accounted for by cluster membership. The remainder of the scores

are presented in Table 20.

A two-way (ethnicity x cluster membership) MANOVA was conducted with the WISC-

III factor indices. As previously noted, a statistically significant effect was observed for cluster

membership (F(8,162) = 15.50, p < .01). No significant ethnicity (F(8,162) = 1.71, p = .10) or

interaction effects were observed (F(12,246) = .531, p < .89).

A three-way (ethnicity x gender x cluster membership) MANOVA was conducted with

the WISC-III factors indices. No significant interaction was observed (F(20, 396) = .511, p =

.962). A one-way repeated measures ANOVA with a Bonferroni post-hoc analysis which

controlled for multiple comparisons was calculated to evaluate within-group differences for each

of the three TBI clusters. Within cluster one, a statistically significant effect was found

(F(1,117) = 4.113, p < .01). A post-hoc analysis which calculated for multiple comparisons

showed that Freedom from Distractibility (M = 98.77, SD = 10.99) scores were significantly

higher than Verbal Comprehension (M = 95.56, SD = 10.35), Perceptual Organization (M =

95.32, SD = 10.17), and Processing Speed (M = 92.55, SD = 13.06) scores. No statistically

73

significant effect for gender was observed within this cluster (F(3,114) = 1.273, p = 2.87). Mean

scores for males and females within cluster one are presented in Table 20.

A one-way repeated measures ANOVA showed a statistically significant effect was

observed for participants within cluster two (F(3,117) = 7.959, p < 01). A follow-up post-hoc

analysis showed that Freedom from Distractibility (M = 84.22, SD = 9.19) and Processing Speed

(M = 85.57, SD = 9.73) scores were significantly higher than Verbal Comprehension (M = 77.50,

SD = 8.84) and Perceptual Organization (M = 76.64, SD = 9.39) scores. No statistically

significant effect for gender was observed within this cluster (F(3,62) = 1.68, p = .18). Mean

scores for males and females within cluster two are presented in Table 20.

A one-way repeated measures ANOVA was conducted to evaluate within group

differences within cluster three. A statistically significant effect was found (F(3,117) = 20.56, p

< .01). A follow-up post-hoc analysis showed scores for Verbal Comprehension (M = 73.40, SD

= 9.63) and Freedom from Distractibility (M = 77.00, SD = 9.32) were significantly higher than

scores for Perceptual Organization (M = 60.13, SD = 9.33) and Processing Speed (M = 57.67, SD

= 6.54). No statistically significant effect for gender was observed within this cluster (F(3,11) =

.287, p = .834). Mean scores for males and females within cluster three are presented in Table

20.

Bivariate Correlations by Cluster Membership

Correlation coefficients were calculated for the WISC-III manifest and latent variables

for each cluster. The results are provided in Table 21. However, given the disparity in sample

size among the clusters and the small correlations between the variables, the reliability of these

findings should be interpreted cautiously. Similar correlations were conducted to evaluate the

relationship between the external neuropsychological measures and the WISC factor indices.

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The results are provided in tables 22, 23, and 24. The same caveat regarding the small sample

size should be applied to these findings. Bivariate correlations were also conducted to evaluate

the relationship between ethnicity and the validating variables for each of the clusters. No

significant correlations were observed within any of the cluster solutions.

Hierarchical Cluster Analysis for the ADHD Sample

Due to the sample size of the ADHD comparison group, a smaller range of cluster

solutions was evaluated for both statistical and clinical significance. The mean scores

(hierarchical cluster analysis) for the two and three cluster solutions are presented in Table 25.

As shown in Table 25, the two cluster solution was comprised of two almost equal sized groups

(i.e., 30 & 40), in which the mean scores for each of the indices within the first cluster (i.e.,

average group) were just above the mean (M = 100, SD = 15) with the exception of the Freedom

from Distractibility index which was slightly below the standardized mean (i.e., 100). In

comparison, in cluster two, means scores for each of the variables were approximately two-thirds

of a standard deviation below the mean. A trend for decreased freedom from distractibility

relative to perceptual organization and verbal comprehension was exhibited in both clusters. The

discrepancy in IQ scores reflected quantitative differences rather than qualitative differences.

By way of comparison, a three cluster solution was also generated in which a third cluster

was partitioned from cluster one (listed above) to create two separate clusters (i.e., clusters 1 &

3) (see Table 25). The two new clusters differed significantly with regard to the Freedom from

Distractibility Index which was in the average range for cluster one and in the below average

range, or more than one standard deviation below the mean, for cluster three. However, the

number of participants within these two new clusters was considerably below the recommended

level (i.e., 30) (Norusis, 2006) and thus would compromise the reliability and validity of any

75

findings produced with this cluster solution. As a result, a three cluster solution was not further

evaluated. Analysis of the two cluster solution is provided below.

K-means Cluster Analysis for the ADHD Sample

Based on findings from the hierarchical cluster analysis, a k-mean cluster analysis was

conducted to evaluate the two cluster solution. Similar to the process used for the TBI group, the

mean variables scores for each cluster were used as starting points or "seeding points" for the k-

means analyses.

2 Cluster k-means Analysis

For the two cluster solution, after a single iteration, changes in cluster centers were 1.33

and .72 for clusters one and two, respectively; cluster membership shifted after one participant

was removed from cluster one and placed into cluster two, raising the membership from forty to

forty one in cluster two. A second iteration was conducted, but the mean scores within each of

the clusters remained stable (see Table 26). A visual representation of the two cluster solution is

provided in Figure 4. A two-way analysis of variance (gender x cluster membership) was

conducted to evaluate between group mean scores for each of the factor indices. Results showed

a statistically significant difference between all of the clusters was observed at the p = .000 level;

although as previously noted, this finding should only used be for descriptive purposes because

the cluster analysis procedure was designed to maximize group differences among the cases in

different clusters (SPSS, 2006). Specifically, a statistically significant effect was observed for

the Verbal Comprehension (F(1,68) = 41.494, p = .000), Perceptual Organization (F(1,68) =

41.092, p = .000), Freedom from Distractibility (F(1,68) = 20.996, p = .000), and Processing

Speed (F(1,68) = 2.852, p = .000) indices. The amount of variance in the factor index scores

which was explained by cluster membership ranged from moderate for the Verbal

76

Comprehension (σ2 = .379) and Perceptual Organization (σ2 =

.377) indices to small for the

Freedom from Distractibility (σ2 = .236)

and Processing Speed (σ2

= .252) indices. Findings

showed that the mean scores for each of the factor indices were consistently higher in cluster one

when compared with cluster two (see Table 26). No significant effect for gender was observed

(F(4,63) = 2.07, p = .207). Similarly, no interaction effect between gender and cluster

membership was observed (F(4,63) = 1.93, p = .116).


differences of the WISC-III factor indices for the two cluster solution. For cluster one, no

significant effect was observed (F(3,29) = 1.570, p = .20) and no significant difference between

factor index scores was noted. No significant gender effect was observed for cluster one

(F(3,25) = 1.44, p = .16). A one-way repeated measures ANOVA was conducted to evaluate

differences within cluster two. Results found no significant difference between factor indices

(F(3,38) = 1.204, p = .31). No significant effect for gender was observed for cluster two

(F(3,25) = .578, p = .63).

Additional Between Cluster Analyses for the 2 Cluster Solution (ADHD Sample)

2 Cluster Solution:

A chi-square test of independence was conducted to determine if there were statistically

observable differences among males and females within the two cluster solution. Sixty-five

percent of the participants within cluster one of the two cluster solution were male; a similar

percent for males (65.9%) was observed in cluster two. Results from the chi-square analysis

showed that gender and cluster membership were independent of each other in this sample (x2(1)

= .001, p = .997) and the observed number of males and females within each cluster did not

differ significantly from the expected number of males and females within each cluster.

77

Cluster Validation with Clinical Variables (TBI Sample)

A two-way ANOVA (ethnicity x cluster membership) was conducted for each of the

validating variables. No significant main effects or interaction effects were noted for any of the

variables.

A two-way ANOVA was conducted to evaluate the effect of gender and cluster

membership on length of coma. No main effect for gender was observed (F(1,120) = .468, p =

.495). No significant effect for cluster membership was found (F(1,120) = 1.65, p = .197). In

contrast, a significant interaction effect for gender and cluster was observed (F(2,98) = 3.442, p

= .035). Specifically, a comparison of mean scores for cluster two showed that coma duration

was significantly longer for males (M = 10.14, SD = 8.75) than females (M = 5.94, SD = 3.39).

In cluster one, the length of coma for males (M = 5.92, SD = 2.62) and females (M = 7.48, SD =

6.81) was not statistically different. The same finding was also observed within cluster three, for

which length of coma for males (M = 9.30, SD = 3.56) and females (M = 9.40, SD = 2.61) was

almost equivalent.


membership on Glasgow Coma Scale scores. No main effect for gender was observed (F(1,87)

= .367, p = .547). No significant effect for cluster membership was observed (F(2,87) = .733, p

= .484). No significant interaction effect for gender and cluster membership was observed

(F(2,87) = .811, p = .448).

78

Validation with External Neuropsychological & Psychological Measures (TBI Sample)

3 Cluster Solution (TBI Sample)

A two-way ANOVA (ethnicity x cluster membership) was conducted for each of the

validating variables. No significant main effects or interaction effects were noted for any of the

variables.


membership on scores from the Children's Category Test (CCT). No main effect for gender was

observed (F(2,98) = 22.37, p = .636). Results showed a significant effect for cluster membership

(F(2,98) = 21.96, p < .01). No interaction effect for gender and cluster membership was

observed (F(2,98) = .037, p = .964). Follow-up univariate analyses showed a statistically

significant difference between each of the clusters. Specifically, children in cluster one received

higher scores on the CCT (M = 49.64, SD = 10.00) than children in cluster two (M = 40.49, SD =

10.00) and children in cluster three (M = 29.45, SD = 8.46) (see Table 27).


membership on scores from the Attention Index from the computerized Continuous Performance

Test (CPT). No main effect for gender was observed (F(1,81) = .574, p = .451). No effect for

cluster membership was observed (F(1,81) = 3.064, p = .053). No interaction effect between

gender and cluster membership was observed (F(1,81) = .710, p = .495). The mean scores for

this variable are presented in Table 27.


membership on response inhibition (Commission Errors). Results showed no significant effect

for cluster membership (F(1,81) = .087, p = .917) or gender (F(2,81) = 1.306, p = .257). No

interaction effect was observed (F(2,81) = .383, p = .683).

79


membership on CPT response rate (processing speed). Results showed no significant effect for

cluster membership (F(2,81) = .109, p = .897) or gender (F(1,81) = .047, p = .829). No

interaction effect was observed (F(2,81) = .145, p = .865). The mean scores for this variable are

presented in Table 27.


membership on scores from the Trail Making Test (Part A & B). For Part A, no significant

effect for gender was observed (F(1,87) = .000, p = .989). Results showed no significant effect

for cluster membership (F(1,87) = 2.254, p = .111). Similarly, no interaction effect for gender

and cluster membership was observed (F(1,87) = .074, p = .929). A review of Table 27 shows

mean scores for this variable.

For Part B, no significant effect for gender was observed (F(1,87) = .012, p = .913).

Results showed a significant effect for cluster membership (F(1,87) = 9.26, p = .000). No

interaction effect for gender and cluster membership was observed (F(1,87) = .074, p = .929).

Follow-up univariate analyses showed a statistically significant difference between the clusters at

each level. As shown in Table 27, children in cluster one received higher scores on Part B of the

TMT (M = 107.13, SD = 12.40) than children in cluster two (M = 94.72, SD = 21.55) and

children in cluster three (M = 73.83, SD = 34.79).


membership on scores from the Letter Backwards task from the TOMAL. No main effect for

gender was observed (F(1,92) = .004, p = .947). Results showed a significant effect for cluster

membership (F(2,92) = 13.31, p < .01). No interaction effect for gender and cluster membership

was observed (F(2,92) = 1.034, p = .360). A follow-up post-hoc analysis showed that the

80

children in cluster three (M = 5.00, SD = 1.87) received statistically lower scores on the Letter

Backwards task than children in cluster two (M = 7.84, SD = 1.68) or cluster one (M = 8.60, SD

= 2.17).


membership on scores from the Oral and Written Language Scale (OWLS). No significant main

effect for gender was observed (F(2,89) = 3.89, p = .052). Results showed a significant effect

for cluster membership (F(2,98) = 12.06, p = .000). A interaction effect for gender and cluster

membership was observed (F(2,98) = 3.47, p = .036). Follow-up univariate analyses showed a

statistically significant difference between each of the clusters. Specifically, children in cluster

one received higher scores on the OWLS (M = 98.10, SD = 13.81) than children in cluster two

(M = 86.90, SD = 13.01) and three (M = 79.30, SD = 13.58). With regard to gender, a significant

difference was observed within cluster three where females (M = 67.00, SD = 6.55) received

lower scores on the OWLS than males (M = 87.50, SD = 5.36).


membership on scores from the BASC parent rating form (BASC). No significant effect for

gender (F(28,64) = 1.561, p = .07) and cluster membership (F(14,31) = .589, p = .853) was

observed. Similarly no significant interaction effect was found between the two variables

(F(28,64) = .901, p = .610). Follow-up univariate analyses showed a significant effect for cluster

membership and the Anxiety (F(2,49) = 4.11, p = .023), Somatic Complaints (F(2,49) = 4.15, p

= .022), and Educational Problems (F(2,49) = 3.39, p = .042) clinical scales. Post-hoc analyses

showed participants in cluster two (M = 46.43, SD = 12.15) received significantly lower scores

than children in clusters one (M = 53.16, SD = 10.29) and three (M = 60.50, SD = 15.07) on the

Anxiety scale. In terms of somatic complaints, children in cluster two (M = 49.62, SD = 10.97)

81

received significantly lower scores than children in clusters one (M = 55.56, SD = 13.06) and

three (M = 60.25, SD = 14.36). In terms of educational problems, post-hoc analyses showed

children in cluster one (M = 43.29, SD = 11.49) received significantly lower scores than children

in cluster two (M = 50.52, SD = 10.35).

2 Cluster Solution (ADHD Group)


membership on scores from the Children's Category Test (CCT). No significant effect for

gender was observed (F(1,64) = 3.513, p = .066). Results showed a significant effect for cluster

membership (F(1,64) = 9.584, p = .003). Specifically, the children in cluster two scored lower

(M = 45.06, SD = 8.17) on the CCT than did the children in cluster one (M = 51.89, SD = 7.70).

No interaction effect for gender and cluster membership was observed (F(1,64) = .638, p = .427).


membership on scores from the Attention Index from the computerized Continuous Performance

Test (CPT). No main for gender was observed (F(1,55) = 3.017, p = .088). No effect for cluster

membership was observed (F(1,55) = 3.827, p = .056). No interaction effect between gender and

cluster membership was observed (F(1,55) = .251, p = .618). The mean scores for this variable

are presented in Table 28. A two-way ANOVA was conducted to evaluate the effect of gender

and cluster membership on response inhibition (Commission Errors). Results showed no

significant effect for cluster membership (F(1,55) = .010, p = .922) or gender (F(1,55) = .003, p

= .958). No interaction effect was observed (F(1,55) = .148, p = .702). A two-way ANOVA

was conducted to evaluate the effect of gender and cluster membership on response rate

(processing speed). Results showed no significant effect for cluster membership (F(1,55) = .772,

82

p = .384) or gender (F(1,55) = .643, p = .426). No interaction effect was observed (F(1,55) =

.023, p = .879).


membership on scores from the Trail Making Test (TMT) (Parts A & B). For Part A, no main

effect for gender was observed (F(1,28) = 1.638, p = .212). Similarly, no significant effect for

cluster membership was noted (F(1,28) = 2.04, p = .165). For Part B, no main effect for gender

(F(1,28) = 1.638, p = .212) or cluster membership was observed (F(1,28) = 1.638, p = .212) (see

Table 28 for mean scores).


membership on scores from the Letter Backwards task from the TOMAL. No main effect for

gender was observed (F(1,43) = .020, p = .889). Results showed a significant effect for cluster

membership (F(1,43) = 1.49, p = .228). No interaction effect for gender and cluster membership

was observed (F(1,43) = .023, p = .880).


membership on scores from the Oral and Written Language Scale (OWLS). Results showed a

significant effect for cluster membership (F(1,49) = 8.02, p = .007). A follow-up comparison of

the mean scores from the two clusters showed that children in cluster one received significantly

higher scores (M = 98.88, SD = 11.33) than children in cluster two (M = 89.31, SD = 12.68).


membership on scores from the BASC parent rating form (BASC). No significant effect for

gender (F(15,32) = 1.226, p = .303) and cluster membership (F(15,32) = .840, p = .630) was

observed. Similarly no significant interaction effect was observed between the two variables

(F(15,32) = .466, p = .941). Follow-up univariate analyses found a significant difference in

83

scores on the Atypical Behaviors scale, for which children in cluster two (M = 62.42, SD =

13.93) received significantly higher scores than children in cluster one (M = 53.75, SD = 8.61).

Bivariate correlations were calculated for the external validating variables by cluster

group. For cluster one, the strongest positive correlation for the validating measures was

observed between the CPT commission index and the Children's Category Test (r(26) = .45, p <

.05). The remaining correlations are presented in Table 29. For cluster two, a significant positive

correlation was observed between the commission index from the CPT and the perceptual

organization index from the WISC-III (r(30) = .41, p < .05) and the response rate index from the

CPT (r(30) = .48, p < .05). The remaining correlations are presented in Table 30.

84

CHAPTER 4

DISCUSSION

Confirmatory Factor Analyses

Results from the present study provide additional empirical support for the use of the

WISC-III four-factor model to measure intellectual functioning with children with head injuries.

Findings from the present study are among the first to establish the efficacy of this model with a

moderate sized sample of children with moderate and severe brain injuries. Findings from the

present study also found support for the use of the four-factor model with a moderate sample of

children with ADHD. This finding was generally consistent with previous findings within this

area.

Intellectual Functioning Post TBI

As expected, findings from the current study showed that children with moderate and

severe head injuries evidenced below average scores on each of the four WISC-III factors, with a

tendency for greater levels of sustained verbal functioning in comparison to non-verbal

functioning. Interestingly, in comparison, the ADHD clinical group evidenced the opposite

pattern in which higher levels of non-verbal intelligence (PIQ) were observed in relation to

verbal intelligence (VIQ). A review of the factor scores also showed that children within the

TBI group evidenced greater levels of sustained functioning for freedom from distractibility

(working memory) which was noted to be a deficit for the ADHD group. This latter finding

seems to provide support for the position that the simple and complex auditory attentional

deficits evidenced in ADHD are not simply produced by structural damage such as a traumatic

brain injury, but rather result from neurochemcial imbalances in the central nervous system.

Findings also evidenced a small correlation between age and the freedom from distractibility

85

scale for the TBI group. The absence of this same pattern within the ADHD group may support

speculation that age of injury is correlated with recovered functioning post-TBI; however, the

strength of these observations was limited and as such this finding should be considered

cautiously. A closer review of performance profiles for each of the samples showed a stronger

inter-relationship among the individual subtests and the higher-order factor indices for the TBI

group than the ADHD group. This differential pattern of correlations between the groups could

suggest that the cognitive processes which underlie performance on the WISC-III subtests are

too inter-related in the TBI group, or that the inter-relations among various cognitive processes

are too weakly associated in the ADHD group.

When intellectual functioning within the TBI group was considered, it was expected that

a significant relationship between processing speed and working memory, which had been

previously linked to Glasgow rating scores in the literature, would be observed. This finding

was not observed in the present study. Based on evidence from previous studies, it was

hypothesized that a link between the recovery of verbal and non-verbal intellectual functioning

and Glasgow rating scores would be observed. In general, support for this hypothesis was not

found, although a weak relationship between Glasgow rating score and processing speed was

noted, particularly for visual scanning in comparison to freedom from distractibility. While

significant, the strength of this relationship between processing speed and Glasgow rating scores

was weak and may have no particular clinical value, although this pattern should be studied

closer in the future. In the past, other variables such as length of coma had also been associated

with recovered intellectual functioning in TBI groups, particularly processing speed. However,

findings from the present study showed no evidence of this relationship.

86

Psychological & Behavioral Functioning

Parent perceptions about psychological functioning were also considered in the present

study in relation to each of the clinical samples. In the TBI group, a small but significant

relationship between verbal comprehension and depression, atypical behaviors, and low social

skills was observed. Interestingly, in both samples, freedom from distractibility was related to

hyperactivity and attentional problems although in the TBI group freedom from distractibility

showed a closer relationship to low social skills while in the ADHD group freedom from

distractibility was more closely related to conducted problems. As expected, within each clinical

group, parents reported higher levels of hyperactivity and behavioral problems for males than

females.

Identifying Cluster Profiles

The evaluation of variables such as a length of coma, age, and injury severity in

relationship to intellectual functioning has had limited utility and yielded ambiguous findings.

To address these limitations, researchers have attempted to create innovative ways to study the

within and between-group processes associated with intellectual functioning post-injury. Based

on a review of previous research it was hypothesized that the cluster analyses would identify

three or four distinct clusters within the TBI group. While admittedly there is some subjectivity

with regard to the identification of such as clusters, analyses from the present study found three

clusters in the TBI group, although it appeared that the clusters primarily differed quantitatively

(i.e., by level of performance) rather than qualitatively; with the exception of the third cluster

whose overall pattern of sustained verbal comprehension and freedom from distractibility scores

and severely impaired perceptual organization and processing speed scores had previously been

characterized as unique given its absence for prior cluster analysis studies conducted with

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healthy groups. Interestingly, there appeared to be greater empirical (and qualitative) support for

a three cluster solution identified in the present study than the previously proposed four cluster

solution (see Donders & Warchausky, 1997). Specifically, the current study only identified a

single below average cluster in comparison to the two below average clusters described by

Donders and Warchausky. However, given the limited clinical utility derived from the separation

of the two clusters, consideration was given to consolidation of the two groups. Interestingly,

when the two below average clusters from the Donders and Warchausky study are collapsed into

a single cluster (see Figure 5) the factor index profiles for each of the three clusters closely

resemble the cluster profiles presented in the current study. It should be noted, however, that the

index scores for the most impaired cluster in the present study were noticeably different than

those which had been described for a similar group in the past. One explanation for the

discrepancy in index scoring may be linked to the decision to exclude mildly impaired children

from the present study which created a more homogenous sample which ultimately prevented

mildly impaired participants from being seeded in the most impaired cluster.

While there were some clear similarities between the cluster profiles from the current

study and those identified in past research, the characterization of the third cluster as uniquely

TBI requires further discussion. First, the sample size in the third cluster from the present study

was very small and below the recommended levels for cluster analysis. Thus, one must question

the reliability and validity of the observed cluster profile. Second, the study did not replicate

prior findings which demonstrated a significant correlation between factor index scores and the

common injury severity indices of length of coma and Glasgow rating scores within the most

impaired cluster group. A small correlation within cluster one and cluster two was observed,

however, between processing speed and Glasgow rating scores and length of coma, respectively.

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While the indices of injury severity were not associated with intellectual functioning as

hypothesized, a review of the factor index profiles within each cluster revealed some interesting

patterns. Most notable, was the indication that the inter-relationship between the Verbal IQ scale

and the underlying Verbal Comprehension and Freedom from Distractibility indices was stronger

for cluster three than cluster one or cluster two. While this specific finding has not been

addressed in the cluster analysis and TBI literature, the pattern seems to be consistent with the

previously discussed findings from the current study which noted a stronger inter-relationship

among the individual subtests and higher-order factors for the TBI group than the ADHD group.

To some degree, this observation helps illustrate the complexity of the recovery process as a

whole by relating improved functioning to the brains ability to integrate multiple domains of

complex cognition simultaneously; an ability which seems to be absent among children who

continue to evidenced impaired intellectual functioning one year post-injury.

Validating the Clustering Process

The third aim to the present study was to validate any clusters which may be identified

within the TBI group with external neuropsychological variables that were not included in the

original cluster process. This is research which was noticeably absent from the pediatric TBI

literature although validation variables such as Glasgow rating scores, age, level of education,

and length of coma had been examined. In contrast, some research validating cluster profiles

with adult TBI groups has been conducted. Based in part on findings from that body of

literature, it was hypothesized that the three clusters would significantly differ on the Trail

Making Test (Part A & B) and card sorting test. While the study provided preliminary findings

from which to speculate about cluster validation with pediatric samples, the number of measures

used to validate the clusters was limited. For this reason, the present study sought to augment

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findings from previous studies with validating the observed clusters with additional

neuropsychological measures of working memory, information processing, attention,

concentration, and receptive language. As hypothesized, significant differences between all of

the clusters was observed for Part B of the Trail Making Test and the card sorting test.

Unexpectedly, Part A of the Trail Making Test was not significantly differentiated by cluster. A

review of the findings seemed to suggest that the between cluster differences reflected

quantitative differences rather than qualitative differences, with scores in cluster one noted to be

higher than scores in cluster two and cluster three. With the exception to the results for Part A,

this pattern was consistent with findings from the aforementioned TBI study with adults which

showed that mean scores were highest in the average cluster and lowest in the impaired cluster.

Significant cluster differences were also noted for processing speed and working memory,

although the differences were only evidenced between cluster one (average) and cluster three

(impaired cluster) for both measures. In contrast, no significant difference among the three

clusters was observed for the measures of attention, concentration, and response inhibition.

An effort was also made in the present study to expand beyond cognitive measures in an

attempt to validate the observed clusters with parent reports of psychological and behavioral

functioning. Findings from the present study showed that parents reported that children with

head injuries in cluster two exhibited higher levels of anxiety and somatic problems than children

in cluster one or two. Similarly, parents reported a higher level of educational problems for

children within cluster two in comparison to cluster one and cluster three. While the small

sample size in the third cluster limits the generalizability of these findings, an evaluation of the

overall pattern may provide some additional information about the relationship between

intellectual functioning and psychological functioning post-injury. In particular, the findings

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from the present study seem to reiterate the need for further studies to evaluate the relationship

between intrapersonal functions such as anxiety or depression and cognitive processes such as

verbal reasoning for children with head injuries.

Taken together, there seems to be minimal validation of the distinct or unique TBI

clusters in the present study. For the most part, when significant within-group differences were

observed among the clusters they were characterized as quantitative and not considered to be

unique aspects associated with a specific cluster. In fact, a visual representation illustrates how

cluster performances on neuropsychological measures tended to merely be differentiated by level

(see Figure 6). It was hypothesized that low processing speed, which had been related to injury

severity in previous research and was particularly evidenced in cluster three, would be uniquely

related to one of the higher-order cognitive tasks such as problem-solving and or cognitive

flexibility. However, no unique relationship between the higher-order cognitive tasks and

impairment in processing speed was identified. Findings also failed to find a unique relationship

between cluster membership and the validating measures. Qualitative support for the observed

clusters would have been evidenced had an external variable such as educational level, MRI

finding, history of rehabilitation, ethnicity, or genetic coding been identified in the present study.

While limited validation of the cluster profiles with neuropsychological instruments was

observed, it should be noted that some interesting patterns were observed. Most notably, was the

observation that three clusters only evidenced significant differences in terms of performance for

those measures that evaluated higher-order cognitively complex functioning such as problem-

solving, planning, and cognitive flexibility. In comparison, tasks which evaluated less

cognitively complex processes such as simple attention, response inhibition, information

processing, working memory and receptive language failed to differentiate the observed clusters.

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Taken together, this finding may suggest that it is disruption within the domain higher-order

cognitive functioning which contributes to the cluster differentiation observed in the present

study. Although the observed differences may only reflect quantitative differences among the

clusters, this finding seems to reflect a new finding within the area of cluster validation and

warrants further investigation.

Clinical Implications

While the examination of the WISC-III factor structure within this population was not

novel, further review of the proposed model structure was necessary to substantiate continued

use of the measure with this clinical population because previous factor analytic studies of the

WISC-III with TBI samples were conducted with large sub-groups of children with mild head

injuries which subsequently produced invalid and unreliable findings. There were a number of

other important reasons to address the prior limitations in this area. First, providing further

empirical support for the use of the four-factor model with children with moderate and severe

head injuries also creates a useful platform from which examinations of in and between group

differences can be conducted with confidence. Second, the findings from the present study may

be used to bolster conclusions drawn from previous studies which have studied intellectual

functioning in head injured children. Third, support for this study provides a continuum from

which comparative studies with the WAIS-III, for which greater empirical support for the four-

factor structure has already been generated, can be conducted. Finally, evidence for the WISC-

III four factor model can be extrapolated (due to similarities in factor structures) to the newer

WISC-IV four-factor model, thus providing support for the continued use of this measure in

future TBI studies.

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Findings from the present study were consistent with results from previous studies which

noted a significant discrepancy between the VIQ and PIQ index scores for children with

moderate to severe head injuries. The application of Sattler's (1988) theoretical

conceptualization of latent brain processes as "crystallized" and "fluid" may help explain the

ubiquity of the VIQ/PIQ discrepancy in the TBI literature. Several recent biological studies have

speculated that the long-term effects associated with cortical lesions (e.g., focal lesions) may

have less of an impact on cognitive functioning than those observed secondary to disruption from

processes such as "shearing" within the sub-cortical white matter regions; which ostensibly have

a strong influence on "fluid" tasks such as information processing and attention. Longitudinal

research on the VIQ-PIQ discrepancy will continue to be necessary to gain a more in depth

understanding of the dynamic nature of these processes, and to what degree discrepant

functioning contributes to problems at home and school. Although there is some question

regarding the ecological utility associated with this observation, it was none-the-less important to

demonstrate this pattern to establish some continuity between the participants in the present

study and those described in prior research. Moreover, the TBI literature is replete with

examples of the VIQ-PIQ split at one, three, and six-month intervals; yet, for numerous reasons

(e.g., improvements in cognitive rehabilitation therapies), evidence of a significant VIQ-PIQ

split one year is not as well documented (see Chadwick, Rutter, Shaffer, & Shrout, 1981). To

date, few studies have examined the nature of the VIQ/PIQ discrepancy at two, three, or fifteen

years post-injury. For this reason, longitudinal research on the VIQ-PIQ discrepancy would

remain important to fully understand the dynamic nature of these processes, and to what degree

discrepant functioning contributes to problems at home and in school.

93

When considering traumatic brain injuries in children as a whole, Glasgow Coma Scale

scores are an index of injury severity that is of interest for clinicians. Specifically, lower

Glasgow scores have been associated with higher levels of brain disruption immediately

following an acquired injury. Glasgow scores have a particular utility in that they provide

clinician's with a quick base-line understanding of a child's level of impairment, from which

future comparisons can be made. Previous studies have reported a relationship between the

recovery of verbal and non-verbal skills and Glasgow scores with higher Glasgow scores linked

with preserved functioning. It was hypothesized that this same pattern between Glasgow Rating

scores and IQ would be observed in the present study. Interestingly, findings failed to

completely support this hypothesis. While less reliable, when the subtests which comprise the

processing speed index were reviewed individually a significant relationship between the Coding

subtest and Glasgow Rating scores was observed. The important distinction between the Coding

and Symbol Search subtests is the fact that, by default, the Coding subtest requires greater

retention of working memory ability than does the Symbol Search subtest, which is inherently a

visual scanning task. This is an important finding, particularly for rehabilitation psychologists

who work with children recovering from brain injuries. Specifically, this seems to suggest that it

may be more important to incorporate rehabilitation practices which focus on the working

memory and cognitive flexibility aspects of visual processing speed rather than simple visual

scanning tasks when working with children with moderate and severe head injuries. This would

be consistent with the findings from the present study which noted significant cluster differences

in executive functioning. This may constitute a new view, focus, and theoretical construct for

evaluation neuropsychologists, as well as treatments in rehabilitation psychologies.

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Surprising, no significant relationship between Glasgow rating scores and the Verbal

Comprehension, Freedom from Distractibility, and the Perceptual Organization factors was

observed. Specifically, children with lower Glasgow scores at the time of hospital admission did

not exhibit lower IQ scores than children with higher Glasgow scores. What this may suggest, is

that the use of the Glasgow Rating system is not appropriate for predicting recovery patterns at

one year post-injury. As was previously noted, many of the studies on children with head injuries

have evaluated scores that were gathered at one, three, or six months post-injury. Less evidence

for a correlation between this rating system and IQ has been shown at twelve months post-injury.

Thus, it may be more appropriate, in-terms of providing longitudinal comparisons and predicting

recovery, to use of a system such as the Ranch Los Amigos scale which specifically addresses

coma levels, particularly given the known relationship between length of coma and disrupted

functions such as processes working memory or processing speed.

In addition to the noted cognitive differences between the two compared clinical groups,

clinicians who work with children with brain injuries and ADHD should also be cognizant of the

psychological differences which uniquely define these two groups. For example, parents of

children with head injuries reported greater difficulty with social skills and problems adapting to

new situations than children with ADHD. Although it is commonly known that both clinical

groups exhibit problems with social functioning, it is important to note that the TBI samples

showed particular difficulty adapting to new situation; a phenomenon not as widely reported in

ADHD samples. This findings was consistent at the overall sample level and again at the cluster

membership level. Therefore, in addition to noted cognitive therapies, findings from the present

study seem to suggest the need for rehabilitation therapies to include a social skills training

component to bolster important social skills such as self-assertiveness and self-worth.

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While studying patients who have sustained traumatic brain injuries as a whole has

produced some valuable clinical information, a number of researchers have argued that there

may be greater utility in identifying specific sub-groups children to further study the effects of

severe TBI. Examining children with head injuries from this perspective may allow clinicians

and researchers to conceptualize and treat brain damage more focally. However, more research

in this area is needed to clarify the parameters of such clusters. For example, although statistical

procedures identified three distinct TBI clusters, each of these appeared to differ quantitatively

rather than qualitatively, although the pattern of the factor indices in the most impaired group

appeared to represent a cluster which was not evident in prior studies with healthy samples.

However, the generalizability of these findings is limited given the small sample size in the

current study. For this reason future studies with large samples sizes would be necessary. If

individual qualitatively different clusters can be identified, it would be important to study those

clusters longitudinally to determine if the clusters remain independent, become collapsed, or

expand at two or three-years post-injury. While initiation of cognitive rehabilitation at three

years post-injury is less prevalent, it makes sense that this research could be expanded to include

this time frame, particularly given evidence which shows improvement through the third year.

The application of studying recovery through the third year may also be useful when considering

TBI corollaries such as anxiety, depression, and PTSD and how such corollaries relate to the

identified clusters. If unique clusters could be identified, knowledge of cluster membership may

also help service providers deliver more effective therapies. Additional information about the

different clusters may also prove useful for clinicians who work with families in a hospital or an

acute trauma facility immediately post-injury. Specifically, expanded research within this area

may contribute to the development of more individualized rehabilitation therapies for

96

psychological functioning, and thereby return more post-injury children into mainstream

programs. This would drive down costs associated with TBI and post-injury treatment in total.

As previously discussed, identifying distinct clusters of head injured children based on IQ

performance has a number of clinical and scientific benefits. In particular, it may be possible to

determine which of the specific demographic and clinical characteristics are most commonly

associated with sustained intellectual functioning post-injury. The literature on TBI is replete

with examples which show a strong correlation between recovered intellectual functioning and

injury severity as measured by length of coma and Glasgow rating scores. Other variables such

as age have been shown to be important in the study of TBI in pediatric and adolescent

populations and less relevant for adult samples. Although no relationship between age and

recovered functioning was observed in the present study, as expected, some effects for length of

coma and Glasgow Rating scores were noted by cluster. Specifically, length of coma and

Glasgow rating scores were more strongly correlated with the processing index in the third

cluster. Studying other variables which were hypothesized to have an influence on the cluster

process also provided important. In particular, findings from the present study seem to suggest

that cluster membership was related, in part, to executive functioning rather than other

neuropsychological processes such as attention, information processing, or receptive language.

This seemed to provide some support for the conclusion that it would be important for

neuropsychologists and rehabilitation psychologist who specialize in cognitive rehabilitation

treatments to conceptualize children with head injuries as a heterogeneous group in an effort to

reduce the observed cluster gaps.

Further evaluation of the role that executive functioning plays in differentiating cluster

membership may be important for a number of other reasons. In particular, structural damage to

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the frontal lobe has been empirically related to changes in emotional regulation, poor impulse

control, and diminished flexibility in thinking (Bigler, 1988). Disruption within this cortical

region has also been associated with impaired social developmental, poor social conduct,

difficulty with self-regulation, and depression (Riccio, Hall, Morgan, Hynd, Gonzalez, &

Marshall, 1994). Stuss and Alexander (2000) have argued that the vital role the frontal lobe

system plays in behavior and cognition includes the regulation of affective experiences, self-

awareness, and social development. In addition to academic problems, research has consistently

shown that children with moderate and severe head injuries experience significant problems

developing and maintaining peer relationships, are more susceptible to being taken advantage of

(because of impaired judgment), and exhibit greater problems with behavior and conduct (e.g.,

fighting, bullying, stealing). This group is also vulnerable to increased levels of depression and

anxiety as well as alcohol and substance abuse. Problems with executive dysregulation have also

been researched in parent-child studies in the TBI literature with increased problems with parent-

child and parent-parent communication observed.

Based on preliminary findings from the present study, it may be important for cognitive

rehabilitation programs to consider developing and implementing therapies that help children

develop important social judgment, self-awareness, and critical thinking skills. In addition to

developing special didactic programs, rehabilitation programs may include a mentoring system

in which mentors, more mature, or even children with more preserved levels of functioning are

coupled with children with greater impairment. It would be important that such programs be

offered at school as children re-integrate into the educational system. Such a program may be

particularly useful for children entering in middle school or junior high, for which independence

begins to play a central role.

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Study Limitations and Future Research

While this study provided a unique perspective from which to evaluate TBI and yielded a

number of interesting findings, several study limitations should be addressed. First, no

premorbid assessment of intellectual functioning (e.g., history of learning problems, grades at

school) was available for review. In the past, studies have examined how premorbid functioning

positively correlates with sustained functioning post-injury. In the future, controlling for level of

education may help researchers further explore the identified cluster solutions. Similarly,

previous research has also examined important variables such as family SES or parent education

level; again these variables were not evaluated in the present study. While the research within

this area is variable, clearly these data points are important and should be included in any future

studies looking at TBI clusters.

One of the unavoidable limitations of the present study was the relatively small sample

size within cluster three. In the past, researchers have argued that a minimum of thirty

participants per cluster would be necessary to ensure the integrity of the findings. In the present

study, the proportion of participants was less than recommended. Overall, individuals in the

cluster three made up approximately 10% of the total sample, which suggests that the overall

sample would have had to have been increased by three fold to meet this minimum requirement.

Although the observed power would be improved with the inclusion of a larger sample, findings

from the present study remain important for understanding the nature of intelligence within

individual clusters one year post-injury.

Additional research is needed to validate findings from the present study. This should

include a greater range of external neuropsychological and psychological measures as well as

breadth of external clinical and demographic variables such as MRI findings or genetic coding.

99

As previously noted, executive functioning is generally considered a multifaceted concept which

should be evaluated by several points. With findings suggesting that executive functioning plays

an important role in differentiating levels of IQ functioning post-injury, a more in-depth analysis

of the role that these functions play may be accomplished through the inclusion of more sensitive

measures (e.g., DKEFS). However, this process is complicated because many of the

neuropsychological instruments which are commonly used to evaluate skills such as executive

functioning are merely downward extensions of adult instruments and may not accurately reflect

or access these functions accurately. At the same time, the data from normative sample studies

of child-based neuropsychological tasks has not always been empirically validated.

It would also be useful for future research to focus on identification of variables which

might predict cluster membership at one year post-injury. The current findings suggest this

might include test scores from executive functioning measures such as the Part B from the Trail

Making Test or some other instrument sensitive to executive skills. One way to accomplish this

goal would be to use statistical procedures such as regression analysis to determine if scores

from an instrument such as Trails B, gathered acutely, could be used to predict cluster

membership at one, two, or three years post-injury. Moreover, if empirical support for the

predictive validity of such measures could be established, the application of such service might

be more widespread given the fact that the administration of instruments like the Trail Making

Test requires less technical training than a measure like the Wechsler Intelligence Scale for

Children. There would be cost saving to be realized if less technical measures were shown to be

able to reliably (and validly) identify the more impaired children relatively early in the process of

their recovery. It should be duly noted, that even given a relative small sample size of the more

impaired children, the larger cost associated with treating these children would be a point that

100

would encourage researchers and service providers alike to find some means to more quickly and

easily identify these children; and of course to seek cost-effect means to treating this population.

Similarly, some interesting patterns were observed among the three clusters with regard

to parent perceptions of psychological and behavioral functioning. It would be important to

augment these findings with self-report questionnaires and teacher evaluations to see if

consistency with the parent reports could be identified. Such comparisons may provide useful

clinical information which could be used to further advance rehabilitation programs, particularly

those aimed at enhancing social awareness and functioning.

While the technology remains in its infancy, contemporary neuropsychology must

embrace the use of neuroimaging practices to supplemental neuropsychological testing. In

particular, advancements in neuroimaging technology (e.g., positron emission tomography

(PET), magnetic resonance imaging (MRI), and functional magnetic resonance imaging (fMRI)

can be successfully utilized to augment the study of localized brain-behavior disorders and direct

treatment practices. Future research should attempt to combine these two areas of scientific study

in an effort to provide a more robust evaluation of brain-behavior functioning, particularly in

children with a history of head injury. Increased level of specificity with regard to functional

and structural relationships will necessarily improve diagnostic practices and facilitate important

treatment services.

Finally, additional research replicating findings from the present study must be conducted

using the newest version of the Wechsler intelligence scales (WISC-IV). Although the proposed

factor structure for the WISC-IV has remained the same (with the exception of the notable

change in nomenclature and the addition of a third subtest [Letter-Number Sequencing])

replicating the present finding would be important because many practicing neuropsychologists

101

have added this measure as part of a standardized neuropsychological battery. While the number

of clinicians using the WISC-IV continues to increase, the use of the WISC-III has not been

completely abandoned. For this reason, results from the present study remain important and

relevant for all neuropsychologists.

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Table 1

A Breakdown of Ethnicity for the Total, TBI, and ADHD Samples

Ethnicity Total Sample TBI Sample ADHD Sample

Caucasian n = 70 (59.8%) n = 54 (43.9%) n = 16 (22.9%)

African-American n = 27 (23.1%) n = 23 (18.7%) n = 04 (5.7%)

Mexican-American/Hispanic n = 18 (15.4%) n = 14 (11.4%) n = 04 (5.7%)

Asian n = 01 (00.5%) n = 01 (00.8%) n = 00

Other n = 01 (00.5%) n = 01 (00.8%) n = 00

Missing Data n = 76 (39.4%) n = 30 (24.4%) n = 46 (65.7%)

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Table 2

Mean and Standard Deviation BASC Scores for the TBI & ADHD Samples

Subtest ADHD TBI

Males Females Males Females

Externalizing Disorders M = 68.39 SD = 16.04 M = 60.29 SD = 10.43 M = 55.28 SD = 16.05 M = 50.19 SD = 13.49

Internalizing Disorders M = 55.21 SD = 13.59 M = 57.53 SD = 9.63 M = 52.00 SD = 11.95 M = 54.68 SD = 14.00

Hyperactivity M = 69.33 SD = 16.95 M = 63.00 SD = 10.43 M = 54.98 SD = 15.63 M = 52.84 SD = 14.15

Aggression M = 63.85 SD = 14.61 M = 56.65 SD = 10.67 M = 55.78 SD = 15.21 M = 50.34 SD = 15.44

Conduct Problems M = 64.24 SD = 16.06 M = 56.65 SD = 11.58 M = 53.03 SD = 15.54 M = 48.32 SD = 9.45

Anxiety M = 52.39 SD = 10.94 M = 55.24 SD = 9.11 M = 51.22 SD = 10.19 M = 52.39 SD = 13.23

Depression M = 60.52 SD = 14.26 M = 58.47 SD = 12.29 M = 53.51 SD = 15.14 M = 54.21 SD = 15.35

Somatization M = 49.27 SD = 15.27 M = 53.71 SD = 11.83 M = 50.31 SD = 11.06 M = 54.61 SD = 12.38

Atypical Behavior M = 58.39 SD = 11.87 M = 58.00 SD = 13.66 M = 52.17 SD = 11.78 M = 52.18 SD = 11.39

Withdrawn Behavior M = 50.94 SD = 14.19 M = 55.29 SD = 8.93 M = 49.82 SD = 11.80 M = 51.87 SD = 11.66

Attention Problems M = 68.88 SD = 9.26 M = 68.35 SD = 6.99 M = 60.00 SD = 11.46 M = 56.47 SD = 11.02

Adaptability M = 38.33 SD = 9.27 M = 37.76 SD = 7.24 M = 45.10 SD = 10.77 M = 43.48 SD = 14.11

Social Skills M = 40.06 SD = 9.86 M = 41.94 SD = 7.15 M = 46.71 SD = 11.05 M = 47.89 SD = 11.87

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Table 3

BASC, Demographic, and Clinical Variable Correlations for TBI Sample

Scale 1 2 3 4 5 6 7 8 9 10 11

1. Hyperactivity --

2. Aggression .66* --

3. Conduct Problems .59* .74* --

4. Anxiety .50* .44* .23* --

5. Depression .62* .58* .52* .68* --

6. Somatic Complaints .37* .14 .09 .40* .33 --

7. Atypical Behavior .62* .45* .39* .60* .47* .29* --

8. Withdrawn Behavior .25* .34* .14 .34* .48* .12 .13 --

9. Attention Problems .60* .54* .48* .49* .49* .11 .52* .21* --

10. Adaptability -.49* - .49* - .47* - .35* - .52* - .17 - .41* - .31* - .74* --

11. Social Skills - .35* - .51* - .44* - .12 - .41* - .20 - .15 - .37* - .53* .78* --

Variable

1. Length of Coma .20 .24* .18 .34* .18 .09 .20* .04 .23* - .22 - .11

2. Glasgow Coma Scale .06 .03 - .07 - .13 - .07 .09 - .04 .22 - .01 - .11 .00

3. Age - .08 .14 - .05 .03 - .13 - .08 - .18 .01 .05 .05 - .09

* = Statistically Significant at .05 level

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Table 4

BASC Scales & Age Correlations for ADHD Sample

Scale 1 2 3 4 5 6 7 8 9 10 11

1. Hyperactivity --

2. Aggression .61* --

3. Conduct Problems .44* .71* --

4. Anxiety .23 .08 .06 --

5. Depression .53* .59* .53* .49* --

6. Somatization .22 .24 .19 .31* .30* --

7. Atypical Behavior .45* .39* .30* .42* .39* .31* --

8. Withdrawn Behavior .13 .15 .01 .12 .15 .22 .30* --

9. Attention Problems .54* .16 .18 .29* .27 .16 .41* .17 --

10. Adaptability .45* .55* .36* .17 .54* .20 .36* .21 .17 --

11. Social Skills -.32* -.44* -.36* .18 -.28* -.08 -.24 -.38* -.33* .61* --

Variable

1. Age .08 -.28* -.22* .15 .18 .05 -.07 -.09 .29* .19 .09

* = Statistically Significant at .05 level

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Table 5

Goodness of Fit Indices for the Four and Three Factor Models

Model df x2

NFI CFI RMSEA

TBI Sample

WISC-III Four Factor Model

48 49.7 .93 .99 .02

WISC-III Three Factor Model 51 60.1 .92 .99 .04

ADHD Sample

WISC-III Four Factor Model 48 45.7 .89 1.00 .00

WISC-III Three Factor Model

51 63.5 .75 .93 .06

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Table 6

Factor Loadings for WISC-III Four Factor & Three Factor Models

Variable TBI Group ADHD Group

4 FM 3FM 4 FM 3FM

Information .87 .86 .66 .66

Vocabulary .86 .84 .84 .83

Similarities .75 .75 .83 .82

Comprehension .75 .75 .53 .52

Picture Completion .67 .67 .54 .56

Picture Arrangement .74 .73 .63 .64

Block Design .78 .78 .79 .76

Object Assembly .75 .76 .63 .64

Symbol Search .89 .90 .38 .34

Coding .76 .76 .40 .43

Arithmetic .77 .70 1.02 .50

Digit Span .56 .49 .44 .23

Note. FM = Factor Model

108

Table 7

TBI and ADHD WISC-III Factor Mean and Standard Deviation Scores

Factor TBI Group ADHD Group

Verbal Comprehension M = 86.84 SD = 13.70 M = 94.19 SD = 12.17

Perceptual Organization M = 84.78 SD = 15.93 M = 95.78 SD = 13.33

Freedom from Distractibility M = 91.26 SD = 13.27 M = 91.36 SD = 12.36

Processing Speed M = 85.59 SD = 15.89 M = 94.60 SD = 11.58

Verbal IQ M = 87.33 SD = 14.63 M = 93.51 SD = 12.64

Performance IQ M = 83.53 SD = 16.11 M = 96.39 SD = 14.14

Full Scale IQ M = 84.25 SD = 14.94 M = 94.24 SD = 12.51

Male Female Male Female

Verbal Comprehension M = 87.1 SD = 14.6 M = 85.1 SD = 12.7 M = 95.7 SD = 12.0 M = 91.4 SD = 12.2

Perceptual Organization M = 85.0 SD = 16.7 M = 83.9 SD = 15.0 M = 96.7 SD = 11.9 M = 94.0 SD = 15.8

Freedom from Distractibility M = 91.6 SD = 13.4 M = 90.7 SD = 13.8 M = 92.2 SD = 11.4 M = 89.7 SD = 14.4

Processing Speed M = 83.6 SD = 16.0 M = 88.2 SD = 15.8 M = 93.7 SD = 10.1 M = 96.4 SD = 14.0

Verbal IQ M = 88.1 SD = 15.2 M = 85.9 SD = 13.7 M = 94.1 SD = 12.7 M = 92.3 SD = 12.5

Performance IQ M = 83.0 SD = 16.2 M = 83.7 SD = 16.3 M = 96.6 SD = 13.8 M = 95.9 SD = 15.0

Full Scale IQ M = 84.4 SD = 15.2 M = 83.4 SD = 15.0 M = 94.7 SD = 11.8 M = 93.3 SD = 14.0

Note. Mean = 100, Standard Deviation = 15.

109

Table 8

WISC-III Factor Index Correlation Coefficients

Factor 1 2 3 4 5 6 7

Total Sample

1. Verbal IQ -

2. Performance IQ .62* -

3. Full Scale IQ .89* .91* -

4. Verbal Comprehension .94* .55* .81* -

5. Perceptual Organization .58* .91* .84* .59* -

6. Freedom from Distractibility .62* .45* .60* .50* .44* -

7. Processing Speed .39* .67* .57* .38* .54* .38* -

TBI Sample

1. Verbal IQ -


3. Full Scale IQ .90* .91* -




7. Processing Speed .41* .65* .59* .39* .52* .47* -

ADHD Sample

1. Verbal IQ -


3. Full Scale IQ .86* .86* -




7. Processing Speed .17 .44* .36* .16 .39* .23 -

* = Statistically Significant at the .05 level

110

Table 9

WISC-III Subtests and Indice Correlations

Variable VCI POI FDI PSI

TBI ADHD TBI ADHD TBI ADHD TBI ADHD

Information .86* .69* .57* .18 .56* .19 .35* .07

Similarities .79* .74* .51* .36* .52* .24* .31* .06

Arithmetic .62* .36* .52* .35* .87* .85* .41* .25*

Vocabulary .87* .80* .51* .35* .56* .24* .28* .17

Compreh .82* .67* .42* .28* .49* .13 .35* .08

Digit Span .42* .08 .37* .10 .79* .78* .35* .10

Picture Comp. .54* .34* .74* .60* .40* .20 .34* .14

Coding .31* .06 .40* .28* .42* .21 .81* .70*

Picture Arrng. .41* .26* .80* .61* .47* .30* .52* .26*

Block Design .52* .35* .79* .68* .48* .30* .36* .23

Object Assm. .47* .18 .79* .69* .38* .12 .41* .23

Symbol Srch. .39* .17 .53* .23 .40* .21 .89* .75*

Note = VCI = Verbal Comprehension Index, POI = Perceptual Organization Index, FDI =

Freedom from Distractibility, PSI = Processing Speed Index

* Correlation is significant at the .05 level

111

Table 10

WISC-III Factor Index Correlations with Glasgow Coma Scale, Age, & Length of Coma

Factor 1 2 3 4 5 6 7 8 9 10

TBI Sample

1. Age -

2. LC .06 -

3. Gl - .12 .07 -

4. VIQ .13 -.14 -.12 -

5. PIQ .00 -.04 .01 .64* -

6. FIQ .08 -.09 -.06 .90* .91* -

7. VC .12 -.13 -.19 .95* .58* .84* -

8. PO .09 -.03 -.08 .61* .93* .85* .60* -

9. FD .16* -.20* -.04 .72* .56* .70* .64* .54* -

10. PS - .09 -.32 .29* .41* .65* .59* .39* .52* .47* -

ADHD Sample

1. Age -

2. LC - -

3. Gl - -

4. VIQ .06 - - -

5. PIQ .08 - - .49* -

6. FIQ .08 - - .86* .86* -

7. VC .06 - - .88.* .34* .71* -

8. PO .06 - - .42* .82* .72* .43* -

9. FD .12 - - .43* .33* .44* .27* .29* -

10. PS .08 - - .17 .44* .36* .16 .39* .23 -

Note. LC = Length of Coma, Gl = Glasgow Coma Scale, VIQ = Verbal Intelligence, PIQ =

Performance Intelligence, FIQ = Full Scale Intelligence, VC = Verbal Comprehension, PO =

Perceptual Organization, FD = Freedom from Distractibility, PS = Processing Speed, - = not

applicable for this sample.

* = Statistically significant at the .05 level

112

Table 11

WISC-III Correlation Matrix for the TBI Sample1

Scale 1 2 3 4 5 6 7 8 9 10 11 12

1. Information --

2. Similarities .64 --

3. Arithmetic .60 .52 --

4. Vocabulary .75 .63 .55 --

5. Comprehension .63 .59 .49 .65 --

6. Digit Span .36 .38 .43 .41 .37 --

7. Picture Completion .50 .53 .42 .47 .41 .26 --

8. Coding .35 .33 .40 .28 .30 .37 .33 --

9. Picture Arrangement .41 .35 .43 .41 .24 .39 .45 .42 --

10. Block Design .53 .45 .49 .49 .40 .33 .48 .36 .56 ---

11. Object Assembly .48 .35 .39 .38 .41 .26 .49 .36 .55 .60 --

12. Symbol Search .38 .36 .38 .32 .41 .33 .36 .65 .52 .42 .46 --

Variable

1. Length of Coma - .08 - .06 - .18 - .13 - .12 - .14 .04 - .03 - .01 - .14 - .04 - .03

2. Glasgow Coma Scale - .21 - .09 - .10 - .12 .04 .08 .07 .30* - .06 - .10 - .08 .21

3. Age .06 .14 .20* .06 .06 .02 .18* - .16 .08 .09 - .14 - .08

1 All WISC-III subtests were significantly correlated at the .01 level (2-tailed)

* = Statistically significant at the .05 level

113

Table 12

WISC-III Reliability Coefficients for the TBI and ADHD Samples

Index TBI ADHD

Total Male Female Total Male Female

Verbal Comprehension .88 .88 .88 .80 .80 .80

Perceptual Organization .80 .82 .81 .75 .71 .81

Freedom from Distractibilty .59 .60 .60 .60 .82 .44

Processing Speed .80 .79 .78 .29 .02- .59

Verbal IQ .78 .83 .70 .42 .33 .51

Performance IQ .69 .69 .71 .55 .63 .51

Full Scale IQ .78 .75 .84 .66 .55 .81

114

Table 13

WISC-III Correlation Matrix for ADHD Sample

Scale 1 2 3 4 5 6 7 8 9 10 11 12

1. Information --

2. Similarities .59* --

3. Arithmetic .35* .41* --

4. Vocabulary .53* .69* .35* --

5. Comprehension .31* .37* .16 .54* --

6. Digit Span .02 .10 .44* .17 .12 --

7. Picture Completion .21 .64* .20 .30* .34* .13 --

8. Coding -.01 .06 .19 .14 .10 .17 .12 --

9. Picture Arrangement .28* .39* .37* .34* .15 .23 .46* .36* --

10. Block Design .39* .37* .47* .41* .27* .11 .40* .19 .44* --

11. Object Assembly .08 .27* .21 .32* .16 .10 .34* .29* .37* .58* --

12. Symbol Search .14 .07 .24* .17 .10 .08 .16 .17 .13 .34* .14 --

Variable

1. Age .10 .24* .15 -.10 -.17 .05 .18 -.09 .08 .12 -.01 .22

* Correlation is significant at the .01 level (2-tailed)

115

Table 14

WISC-III & BASC Scale Correlations (TBI Sample)

Scale 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Hyp --

Agg .66* --

Con .59* .74* --

Anx .50* .44* .23* --

Dep .62* .58* .52* .67* --

Som .37* .14* .09 .34* .33* --

Aty .61* .45* .39* .58* .47* .29* --

Wth .25* .34* .14 .34* .48* .12 .13 --

Att .60* .54* .48* .49* .49* .11 .52* .21* --

Adp - .49* - .48* - .47* - .35* - .52* - .17 - .41* - .31* - .74* --

Soc - .35* - .51* - .44* - .12 - .41* - .02 - .15 - .37* - .53* .78* --

Ext .85* .91* .87* .45* .56* .23* .55 .28 .61* - .54* - .49* --

Int .63* .51* .38* .83* .87* .67* .57* .41* .46* - .45* - .25* .58* --

VC - .25* - .09 - .07 - .05 - .22* .03 - .22* - .13 - .28* .15 .24* - .16 - .12 --

PO - .15 .02 - .05 .13 - .01 .10 - .21* .05 - .27* .17 .15 - .07 .08 .59* --

FD - .24* - .13 - .19 - .06 - .19 .18 - .19 .02 - .36* .22 .21* - .21* - .05 .62* .53* --

PS - .05 - .02 - .01 - .04 - .04 .19 - .09 - .03 - .23* .08 .02 - .03 .02 .38* .52* .47* --

Note. Hyp = Hyperactivity, Agg = Aggression, Con = Conduct Problems, Anx = Anxiety, Dep = Depression, Som = Somatic

Complaints, Aty = Atypical Behaviors, Wth = Withdrawn Behaviors, Adp = Adaptability, Soc = Social Skills, Ext = Externalizing

Problems, Int = Internalizing Problems, VC = Verbal Comprehension Index, PO = Perceptual Organization Index, FD = Freedom

from Distractibility Index, PS = Processing Speed Index.

* = Clinically Significant at .05 level.

116

Table 15

WISC-III & BASC Scale Correlations (ADHD Sample)

Scale 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Hyp --

Agg .61* --

Con .44* .71* --

Anx .23 .08 .06 --

Dep .53* .59* .53* .45* --

Som .22 .24 .19 .32* .30* --

Aty .45* .39* .29* .42* .39* .31* --

Wth .13 .15 .02 .12 .15 .22 .30 --

Att .54* .16 .18 .29* .27 .16 .41* .17 --

Adp - .45* - .55* - .36* - .17 - .55* - .20 - .36* - .21 - .17 --

Soc - .32* - .44* - .37* - .18 - .28* - .08 - .24 - .38* - .33* .61* --

Ext .82* .90* .83* .15 .65* .26 .44* .12 .36* - .53* - .44* --

Int .44* .43* .36* .73* .79* .74* .50* .23 .31* - .42* - .11 .49* --

VC .14 - .01 - .11 - .20 - .11 .02 - .20 - .13 - .02 .19 .16 - .01 - .12 --

PO .16 - .05 - .00 - .08 - .02 .16 - .20 - .16 .16* .31* .21 .05 - .12 .43* --

FD .34* .20 - .27* .01 .11 - .01 - .04 - .14 .16 .01 .02 .32* .05 .27* .29* --

PS .02 - .06 - .03 .04 .04 .11 - .27 - .05 .01 .02 .03 - .03 .09 .17 .39* .23 --

Note. Hyp = Hyperactivity, Agg = Aggression, Con = Conduct Problems, Anx = Anxiety, Dep = Depression, Som = Somatic

Complaints, Aty = Atypical Behaviors, Wth = Withdrawn Behaviors, Adp = Adaptability, Soc = Social Skills, Ext = Externalizing

Problems, Int = Internalizing Problems, VC = Verbal Comprehension Index, PO = Perceptual Organization Index, FD = Freedom

from Distractibility Index, PS = Processing Speed Index.

* = Clinically Significant at .05 level.

117

Table 16

WISC-III & External Validation Variable Correlation Matrix

Scale 1 2 3 4 5 6 7 8 9 10 11 12 13

Total TBI Sample

1. VCI --

2. POI .59* --

3. FDI .62* .53* -- 4. PSI .38* .52 .47* --

5. TLB .25* .38* .55* .40* --

6. CPTAtt -.14 -.17 -.10 .04 .16 -- 7. CPTrt -.27* -.27* -.27* -.11 -.24 .56* --

8. CCT .40* .56* .39* .37* .46* -.26* -.42 --

9. CPTcom .10 -.07 .02 .14 -.21 .69* .19 -.18 --

10. TrailsA .21 .29* .17 .33* .10 .09 -.03 .27* .06 --- 11. TrailsB .42* .53* .43* .49* .31* .07 -.04 .44* .07 .43 --

12. Length of Coma -.13 -.03 -.20* -.03 -.12 .21 .05 -.08 .24* .02 -.10 --

13. Glasgow Rating Score -.19 -.08* -.04 .29* -.03 .31* .15 -.03 .32 -.13 -.09 .07 --

Note. VCI = Verbal Comprehension Index, POI = Perceptual Organization Index, FDI = Freedom from Distractibility Index, PSI =

Processing Speed Index, TLB = TOMAL Letter Backwards, CPTAtt = Continuous Performance Test Attention Index, CPTrt =

Continuous Performance Test Response Rate, CCT = Children's Category Test, CPTcom = Continuous Performance Test Commission

Errors, TrailsA = Trail Making Test Part A, TrailsB = Trail Making Test Part B.

*Statistically Significant @ .05 level

118

Table 17

WISC-III & External Validation Variable Correlation Matrix

Scale 1 2 3 4 5 6 7 8 9 10 11

Total ADHD Sample

1. VCI --

2. POI .43* --

3. FDI .27* .29* -- 4. PSI .16 .39* .23 --

5. TLB .22 .09 .27 - .06 --

6. CPTAtt -.26 -.11 .24 .10 -.08 -- 7. CPTrt -.20 -.07 -.04 - .09 -.20 .62* --

8. CCT .36* .36* -.17 .04 .29 -.18 -.05 --

9. CPTcom .01 .20 -.02 .04 -.05 .59* .34* -.30* --

10. TrailsA .24 .04 .16 .30 .14 -.18 -.21 .08 -.05 --- 11. TrailsB -.04 .00 .18 .13 - .28 .01 .05 .05 .11 .22 --

Note. VCI = Verbal Comprehension Index, POI = Perceptual Organization Index, FDI = Freedom from Distractibility Index, PSI = Processing

Speed Index, TLB = TOMAL Letter Backwards, CPTAtt = Continuous Performance Test Attention Index, CPTrt = Continuous Performance Test

Response Rate, CCT = Children's Category Test, CPTcom = Continuous Performance Test Commission Errors, TrailsA = Trail Making Test Part A, TrailsB = Trail Making Test Part B.


119

Table 18

TBI and ADHD WISC-III Factor Mean and Standard Deviation Scores

Factor TBI Sample ADHD Sample

Male Female Male Female

Verbal Comprehension M = 87.1 SD = 14.6 M = 85.1 SD = 12.7 M = 95.7 SD = 12.0 M = 91.4 SD = 12.2

Perceptual Organization M = 85.0 SD = 16.7 M = 84.0 SD = 15.0 M = 96.7 SD = 11.9 M = 94.0 SD = 15.8

Freedom from Distractibility M = 91.2 SD = 13.4 M = 91.1 SD = 13.8 M = 92.2 SD = 11.4 M = 89.7 SD = 14.4

Processing Speed M = 83.2 SD = 16.0 M = 88.2 SD = 15.8 M = 93.7 SD = 10.1 M = 96.4 SD = 14.0

Verbal IQ M = 88.1 SD = 15.2 M = 85.9 SD = 13.7 M = 94.1 SD = 12.7 M = 92.3 SD = 12.5

Performance IQ M = 83.0 SD = 16.2 M = 83.7 SD = 16.3 M = 96.6 SD = 13.8 M = 95.9 SD = 15.0

Full Scale IQ M = 84.4 SD = 15.2 M = 83.4 SD = 15.0 M = 94.7 SD = 11.8 M = 93.3 SD = 14.0

Note. Mean = 100, Standard Deviation = 15

120

Table 19

Hierarchical Cluster Solutions for the TBI Sample

Cluster Solution VCI POI FDI PSI

M SD M SD M SD M SD

2 Cluster Solution

Cluster 1 N = 66 95.22 10.55 95.74 09.41 98.46 11.28 91.79 13.00

Cluster 2 N = 56 76.54 09.85 71.26 11.24 82.37 10.14 77.34 15.54

3 Cluster Solution

Cluster 1 N = 66 95.22 10.55 95.74 09.41 98.46 11.28 91.79 13.00

Cluster 2 N = 42 77.76 09.68 75.80 08.58 84.88 09.54 83.98 11.51

Cluster 3 N = 14 72.86 10.27 57.64 06.29 74.86 09.28 57.43 06.99

4 Cluster Solution

Cluster 1 N = 19 104.11 09.63 101.74 10.22 109.95 10.86 100.84 12.33

Cluster 2 N = 48 91.70 08.59 93.37 08.43 93.92 09.10 88.20 08.77

Cluster 3 N = 42 77.76 09.68 75.80 08.58 84.88 09.54 83.98 11.51

Cluster 4 N = 14 72.86 10.27 57.64 06.29 74.86 09.27 57.43 06.99

Note: VCI = Verbal Comprehension Index, POI = Perceptual Organization Index, FDI = Freedom from Distractibility Index,

PSI = Processing Speed Index.

121

Table 20

Male, Female, and Total Factor Index Scores and Standard Deviations of k-mean 3 Cluster Solution-TBI Sample

Variable Cluster 1 Cluster 2 Cluster 3

(n = 66) (n = 40) (n = 16)

Male Female Total Male Female Total Male Female Total

M SD M SD M SD M SD M SD M SD M SD M SD M SD

V 97.21 11.55 93.17 7.95 95.56 10.35 77.95 8.20 76.94 9.96 77.50 8.84 75.70 9.65 68.80 8.70 73.40 9.63

P 95.65 11.37 94.84 8.33 95.32 10.17 78.21 10.61 74.72 7.50 76.64 9.93 61.20 9.75 58.00 8.97 60.13 9.33

F 99.13 11.29 98.25 10.73 98.77 10.99 83.81 8.32 84.72 10.38 84.22 9.19 79.50 7.60 72.00 11.22 77.00 9.33

S 90.71 13.37 95.20 12.36 92.55 13.06 82.51 9.63 87.09 9.50 85.57 9.73 58.20 6.89 56.60 6.39 57.67 6.54

Note. V = Verbal Comprehension Index, P = Perceptual Organization Index, F = Freedom from Distractibility Index, S = Processing

Speed Index.

Mean = 100, Standard Deviation = 15

122

Table 21

WISC-III Factor Index Correlation Coefficients for the TBI Clusters

Factor 1 2 3 4 5 6 7

Cluster 1

1. Verbal IQ -


3. Full Scale IQ .83* .76* -

4. Verbal Comprehension .88* .13 .66* -

5. Perceptual Organization .18 .78* .56 .21 -

6. Freedom from Distractibility .50* -.04 .33* .39* .04 -

7. Processing Speed .14 .32* .28* .14 .12 .17 -

Cluster 2

1. Verbal IQ -

2. Performance IQ .00 -

3. Full Scale IQ .66* .71* -

4. Verbal Comprehension .95* -.08 .57* -

5. Perceptual Organization .00 .87* .62* -.02 -

6. Freedom from Distractibility .27 .29 .35* .03 .06 -

7. Processing Speed -.32* .17 .11* -.30 -.18 .13 -

Cluster 3

1. Verbal IQ -

2. Performance IQ .14 -

3. Full Scale IQ .85* .64* -

4. Verbal Comprehension .98*. .10 .81* -

5. Perceptual Organization .14 .97* .63* .12 -

6. Freedom from Distractibility .72 .30 .71* .60* .22 -

7. Processing Speed -.22 .94 -.17 -.30 -.10 .84 -

* Statistically Significant Correlation at the .05 level.

123

Table 22

WISC-III & External Validation Variables by Clusters Correlation Matrix (TBI Sample)

Scale 1 2 3 4 5 6 7 8 9 10 11 12 13

Cluster One

1. VCI -- 2. POI .21 --

3. FDI .39* .04 --

4. PSI .14 .12 .17 -- 5. TLB -.04 .09 .42* .16 --

6. CPTAtt -.04 .13 -.09 .32* -.13 --

7. CPTrt -.27 -.08 -.28 .05 -.13 .45 -- 8. CCT .18 .24 .03 .07 .17 .14 -.03 --

9. CPTcom .24 -.04 .03 .30* -.48 .77* .20 -.02 --

10. TrailsA -.01 .15 .08 .30* .11 .09 -.01 .47 .07 --

11. TrailsB .22 .25 .31* .29* .19 .04* -.13 .39 -.17 .50* -- 12. Length of Coma .08 .11 -.02 .20 -.03 .40 .01 .05 .34* .02 -.05 --

13. Glasgow Rating Score -.22 -.24 -.21 .29* -.26 .39* .06 -.19 .49* -.18 -.39 .18 --



Continuous Performance Test Response Rate, CCT = Children's Category Test, CPTcom = Continuous Performance Test Commission Errors, TrailsA = Trail Making Test Part A, TrailsB = Trail Making Test Part B.


124

Table 23


Scale 1 2 3 4 5 6 7 8 9 10 11 12 13

Cluster Two

1. VCI -- 2. POI -.02 --

3. FDI .03 .06 --

4. PSI -.30 -.18 .13 -- 5. TLB -.02 -.09 .56* .31 --

6. CPTAtt -.12 .04 .20 .40* .27 --

7. CPTrt .04 -.07 .22 .41 -.03 .65* -- 8. CCT -.07 .13 .06 -.11 .41 -.36 -.44* --

9. CPTcom -.07 -.06 .03 .15 -.65 .71 .16 -.29 --

10. TrailsA .06 .21 -.12 .29 -.08 .40 .31 -.32 .17 --

11. TrailsB .16 .30 .04 .24 .19 .36 .25 .19 .18 .58* -- 12. Length of Coma -.08 .35* -.28 .06 -.07 .11 -.05 .07 .22 .21 .08 --

13. Glasgow Rating Score -.28 -.24 .20 .32 .50* .27 .54 .26 -.17 -.02 .07 -.18 --





125

Table 24


Scale 1 2 3 4 5 6 7 8 9 10 11 12 13

Cluster Three

1. VCI -- 2. POI .12 --

3. FDI .60* .22 --

4. PSI -.30 -.20 .06 -- 5. TLB .65 .36 .31 -.53 --

6. CPTAtt .65* -.48 .57 -.05 -.15 --

7. CPTrt .14 -.30 -.03 -.04 -.29 .62 -- 8. CCT .24 .74* .27 .14 .24 -.68 -.89* --

9. CPTcom .29 -.20 .58 .52 -.18 .48 .08 -.00 --

10. TrailsA .39 . 14 -.01 -.03 -.25 .13 .03 -.06 .02 --

11. TrailsB .32 .33 .41 .40 -.16 .57 .60 -.16 .63 .07 -- 12. Length of Coma -.04 -.15 -.09 -.36 -.14 -.23 -.09 .13 -.08 -.10 -.35 --

13. Glasgow Rating Score -.08 -.39 -.06 .15 -.24 .20 .25 -.09 -.46 -.32 .15 -.01 --

Note. VCI = Verbal Comprehension Index, POI = Perceptual Organization Index, FDI = Freedom from Distractibility Index, PSI = Processing

Speed Index, TLB = TOMAL Letter Backwards, CPTAtt = Continuous Performance Test Attention Index, CPTrt = Continuous Performance Test

Response Rate, CCT = Children's Category Test, CPTcom = Continuous Performance Test Commission Errors, TrailsA = Trail Making Test Part A, TrailsB = Trail Making Test Part B.


126

Table 25

Hierarchical Cluster Solutions for the ADHD Sample

2 Cluster Solution VCI POI FDI PSI

M SD M SD M SD M SD

Cluster 1 N = 30 102.50 12.87 104.49 11.85 97.91 13.03 101.91 10.83

Cluster 2 N = 40 87.96 6.76 89.25 10.39 86.46 9.59 89.12 8.83

3 Cluster Solution

Cluster 1 N = 19 101.63 13.98 100.72 10.31 104.54 8.68 97.54 9.53

Cluster 2 N = 40 87.96 6.76 89.24 10.39 86.46 9.59 89.12 8.83

Cluster 3 N = 11 104.00 11.17 111.00 11.92 86.45 11.38 109.45 8.81

Note: VCI = Verbal Comprehension Index, POI = Perceptual Organization Index, FDI = Freedom from Distractibility Index, PSI =

Processing Speed Index.

127

Table 26

Factor Index Scores and Standard Deviations of Two WISC-III Cluster Subtypes for the ADHD

Sample (k-means)

Cluster 1 Cluster 2

(n = 29) (n = 41)

M SD M SD

VC 103.03 12.79 87.94 6.64

PO 105.44 11.39 88.95 10.01

FD 98.52 12.16 86.30 10.10

PS 101.45 11.26 89.75 9.18

Note. VC = Verbal Comprehension Index, PO = Perceptual Organization Index, FD = Freedom

from Distractibility Index, PS = Processing Speed Index

128

Table 27

Mean and Standard Deviation Scores for the External Validation Variables (TBI 3 Cluster Solution)

Variable Cluster 1 Cluster 2 Cluster 3

M SD M SD M SD

Length of Coma 6.56 4.81 8.25 7.10 9.33 3.18

Glasgow Rating Score 7.31 3.47 7.33 3.47 6.20 1.13

Trails Aa 104.89 16.07 99.07 12.17 94.33 20.44

Trails Ba 107.13 12.40 94.72 21.55 73.83 34.79

OWLSa 98.10 13.81 86.90 13.01 79.30 13.58

CCTb 49.64 10.00 40.49 10.00 29.45 08.46

CPTComb 48.82 11.00 48.90 09.21 51.60 09.70

CPTAttb 52.12 08.79 54.90 09.58 59.01 10.82

CPTrtb 60.35 22.76 66.17 18.39 78.91 23.11

TLBc 08.60 02.17 07.84 01.68 05.00 01.87

Note. Trails A = Trail Making Test Part A, Trails B = Trail Making Test Part B, OWLS = Oral and Written Language Scales, CCT = Children's

Category Test, CPTCom = Continuous Performance Test Commissions, CPTAtt = Continuous Performance Test Attention Index, CPTrt = Continuous

Performance Test Response Rate, TLB = TOMAL Letter Backwards. a Mean = 100, Standard Deviation = 15 bMean = 50, Standard Deviation = 10 cMean = 10, Standard Deviation = 3

129

Table 28

Mean and Standard Deviation Scores for the External Validation Variables (ADHD 2 Cluster Solution)

Variable Cluster 1 Cluster 2

M SD M SD

Trails Aa 112.85 05.13 107.39 10.55

Trails Ba 110.82 07.48 110.17 09.39

OWLSa 98.88 11.33 89.31 12.68

CCTb 51.89 07.70 45.06 08.17

CPTComb 50.42 09.54 49.78 10.07

CPTAttb 54.67 08.54 61.20 14.74

CPTrtb 68.51 24.30 69.99 19.69

TLBc 09.65 02.01 08.71 02.27

Note. Trails A = Trail Making Test Part A, Trails B = Trail Making Test Part B, OWLS = Oral and Written Language Scales, CCT = Children's

Category Test, CPTCom = Continuous Performance Test Commissions, CPTAtt = Continuous Performance Test Attention Index, CPTrt = Continuous

Performance Test Response Rate, TLB = TOMAL Letter Backwards. a Mean = 100, Standard Deviation = 15 bMean = 50, Standard Deviation = 10 cMean = 10, Standard Deviation = 3

130

Table 29

WISC-III & External Validation Variable by Cluster Correlation Matrix (ADHD Sample)

Scale 1 2 3 4 5 6 7 8 9 10 11

Cluster One

1. VCI -- 2. POI -.09 --

3. FDI -.23 -.24 --

4. PSI -.28 .40 -.04 --

5. TLB .16 -.01 .29 -.14 -- 6. CPTAtt -.27 -.11 .07 .15 -.30 --

7. CPTrt -.24 -.01 -.07 -.16 -.19 .72* --

8. CCT .22 .11 -.33 -.40 .07 -.06 .05 -- 9. CPTcom .08 .02 -.10 -.10 -.23 .41 .20 .45* --

10. TrailsA .19 .47 .30 .20 .63 -.26 .00 -.14 -24 --

11. TrailsB -.23 .56 .14 .58 .32 .28 .35 .06 .37 .50 --


Processing Speed Index, TLB = TOMAL Letter Backwards, CPTAtt = Continuous Performance Test Attention Index, CPTrt = Continuous Performance Test Response Rate, CCT = Children's Category Test, CPTcom = Continuous Performance Test Commission

Errors, TrailsA = Trail Making Test Part A, TrailsB = Trail Making Test Part B.


131

Table 30

WISC-III & External Validation Variable by Cluster Correlation Matrix (ADHD Sample)

Scale 1 2 3 4 5 6 7 8 9 10 11

Cluster Two

1. VCI --

2. POI .38 --

3. FDI .26 .21 -- 4. PSI -.10 -.15 .00 --

5. TLB .09 -.60 .14 -.15 --

6. CPTAtt -.05 .14 -.27 -.05 .10 -- 7. CPTrt -.20 .25 .04 -.00 -.24 .65* --

8. CCT .10 .21 -.21 -.01 .28 .02 .10 --

9. CPTcom .12 .41* -.08 .14 -.01 .74 .48* .21 --

10. TrailsA .12 -.35 -.15 .19 -.62* -.15 -.30 -.38 -.16 -- 11. TrailsB -.16 -.28 .25 -.07 .27 -.05 -.10 -.20 -.01 .16 --




*Statistically Significant @ .05 level.

132

Figure 1

WISC-III Four Factor Model (TBI Sample)

.76 .56

.67

.88 .77

.52 .77

.86 .70

.87 .78

.75

.75

.72 .67

.86

.75

.74

Note. PS = Processing Speed Index, FD = Freedom from Distractibility Index, Verbal Proc. = Verbal Comprehension Index,

Vis. Proc. = Perceptual Organization Index, Arrang = Arrangement, Assembl = Assembly, Compl = Completion, Coding =

Digit Coding.

Coding Digit Span

Symbol Search Arithmetic

Information

Similarities

Vocabulary

Comprehension

Block Design

Object Assembl

Picture Compl

Picture Arrang

PS FD

Verbal

Proc.

Vis.

Proc.

133

Figure 2

WISC-III Four Factor Model (ADHD Sample)

.38 .44

.57

.40 1.02

.37 .51

.45 .78

.66 .54

.83 .63

.61 .79

.84

.63 .63

Note. PS = Processing Speed Index, FD = Freedom from Distractibility Index, Verbal Proc. = Verbal Comprehension Index,

Vis. Proc. = Perceptual Organization Index, Arrang = Arrangement, Assembl = Assembly, Compl = Completion, Coding =

Digit Coding.

Coding Digit Span

Symbol Search Arithmetic

Information

Similarities

Vocabulary

Comprehension

Block Design

Object Assembl

Picture Compl

Picture Arrang

PS WM

Verbal

Proc

Vis

Proc

134

Figure 3

WISC-III k-means Cluster Analysis for the TBI Sample

Cluster Analysis TBI Group

55

60

65

70

75

80

85

90

95

100

105

110

VCI POI FDI PSI

WISC-III Factor Indices

WIS

C-I

II S

cale

d S

core

*

Cluster 1

Cluster 2

Cluster 3

Note. VCI = Verbal Comprehension Index, POI = Perceptual Organization Index, FDI =

Freedom from Distractibility Index, PSI = Processing Speed Index.

* Mean = 100, Standard Deviation = 15.

135

Figure 4

K-means Cluster Analysis for the ADHD Group-2 Cluster Solution

Cluster Analysis ADHD Group

75

80

85

90

95

100

105

110

VCI POI FDI PSI


WIS

C-I

II S

cale

Sco

re*

Cluster 1

Cluster 2




136

Figure 5

Clusters Comparisons from the Current and the Donders & Warschausky Studies

Cluster Analysis TBI Group

55

60

65

70

75

80

85

90

95

100

105

110

115

120

VCI POI FDI PSI


WIS

C-I

II S

cale

d S

core

*

Cluster 1a

Cluster 2a

Cluster 3a

Cluster 2b

Cluster 3b

Cluster 1b




a= Cluster from present study.

b= Modified cluster from Donders & Warschausky, 1997.

137

Figure 6

Comparison of the Neuropsychological Instruments by Cluster Group

0

20

40

60

80

100

120

Trails

A1

Trails

B1

OWLS2 CCT2

CPTCom2

CPTAtt2 CPTrt2 TLB3

c1

c2

c3

Notes. Trails A = Trail Making Test Part A, Trails B = Trail Making Test B, Owls = Oral and

Written Language Scale, CCT = Children's Card Sorting Test, CPTComb = Continuous

Performance Test Combined, CPTAtt = Continuous Performance Test Attention Index, CPTrt =

Continuous Performance Test Response Rate, TLBc = Tomal Letter Backwards, C1 = Cluster 1,

C2 = Cluster 2, C3 = Cluster 3. 1 Mean = 100, SD = 15

2 Mean = 50, SD = 10

3 Mean = 10, SD = 3

138

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