traumatic brain injury in children and adolescents: an evaluation of the wisc-iii...
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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
Confirmatory Factor Analysis
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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23. WISC-III and External Validation Variables by Clusters Correlation Matrix (TBI Sample) (Cluster 2) ....................................................................................................... 124
24. WISC-III and External Validation Variables by Clusters Correlation Matrix (TBI Sample) (Cluster 3) ....................................................................................................... 125
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
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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
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(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
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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
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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,
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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
13
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)
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
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.
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).
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
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.
A one-way repeated measures ANOVA was conducted to evaluate within-group
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)
A two-way ANOVA was conducted to evaluate the effect of diagnosis and gender on
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
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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.
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Oral and Written Language Scales (OWLS)
A two-way ANOVA was conducted to evaluate the effect of diagnosis and gender on
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
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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
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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).
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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
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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).
A one-way repeated measures ANOVA was conducted to evaluate within-group
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.
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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.
A two-way ANOVA was conducted to evaluate the effect of gender and cluster
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).
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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.
A two-way ANOVA was conducted to evaluate the effect of gender and cluster
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).
A two-way ANOVA was conducted to evaluate the effect of gender and cluster
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.
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(1,81) = .087, p = .917) or gender (F(2,81) = 1.306, p = .257). No
interaction effect was observed (F(2,81) = .383, p = .683).
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A two-way ANOVA was conducted to evaluate the effect of gender and cluster
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.
A two-way ANOVA was conducted to evaluate the effect of gender and cluster
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).
A two-way ANOVA was conducted to evaluate the effect of gender and cluster
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
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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).
A two-way ANOVA was conducted to evaluate the effect of gender and cluster
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).
A two-way ANOVA was conducted to evaluate the effect of gender and cluster
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)
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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)
A two-way ANOVA was conducted to evaluate the effect of gender and cluster
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).
A two-way ANOVA was conducted to evaluate the effect of gender and cluster
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).
A two-way ANOVA was conducted to evaluate the effect of gender and cluster
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).
A two-way ANOVA was conducted to evaluate the effect of gender and cluster
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).
A two-way ANOVA was conducted to evaluate the effect of gender and cluster
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).
A two-way ANOVA was conducted to evaluate the effect of gender and cluster
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.
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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
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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.
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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.
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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.
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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
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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
106
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
107
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 -
2. Performance IQ .64* -
3. Full Scale IQ .90* .91* -
4. Verbal Comprehension .95* .58* .84* -
5. Perceptual Organization .61* .93* .85* .60* -
6. Freedom from Distractibility .72* .56* .70* .64* .54* -
7. Processing Speed .41* .65* .59* .39* .52* .47* -
ADHD Sample
1. Verbal IQ -
2. Performance IQ .49* -
3. Full Scale IQ .86* .86* -
4. Verbal Comprehension .88* .34* .71* -
5. Perceptual Organization .42* .82* .72* .43* -
6. Freedom from Distractibility .43* .33* .44* .27* .29* -
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.
*Statistically Significant @ .05 level
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 -
2. Performance IQ .28* -
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 --
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
124
Table 23
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 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 --
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
125
Table 24
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 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.
*Statistically Significant @ .05 level
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 --
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
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 --
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.
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
WISC-III Factor Indices
WIS
C-I
II S
cale
Sco
re*
Cluster 1
Cluster 2
Note. VCI = Verbal Comprehension Index, POI = Perceptual Organization Index, FDI =
Freedom from Distractibility Index, PSI = Processing Speed Index.
* Mean = 100, Standard Deviation = 15.
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
WISC-III Factor Indices
WIS
C-I
II S
cale
d S
core
*
Cluster 1a
Cluster 2a
Cluster 3a
Cluster 2b
Cluster 3b
Cluster 1b
Note. VCI = Verbal Comprehension Index, POI = Perceptual Organization Index, FDI =
Freedom from Distractibility Index, PSI = Processing Speed Index.
* Mean = 100, Standard Deviation = 15.
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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