204 A.G. Volpe et al. / Archives of Clinical Neuropsychology 21 (2006) 203–210
Several successful studies have been conducted on the ability of comprehensive neuropsychological batteries to
differentiate neurological impairment from normal functioning. For example, the Halstead–Reitan Neuropsychologi-
cal Battery (HRNB, Reitan & Wolfson, 1993) and the Luria Nebraska Neuropsychological Battery (LNNB, Golden,
Hammeke, & Purisch, 1978) have been respectively found to predict brain damage at rates of up to 90% (Golden,
1976) and 100% accuracy (Golden et al., 1978). However, systematic evaluation of sensory-motor measures to pre-
dict neurological damage has been sparse. The lack of standardized administration and scoring (Reitan & Wolfson,
2003), differences in speciﬁc task selection (Adams & Victor, 1993; Glick, 1993), reliance on patients’ self-reporting,
and the clinician’s experience in interpreting the results have been mentioned as reasons for the paucity of research
demonstrating the predictive ability of sensory-motor tests (Dean & Woodcock, 1999; Malloy & Nadeau, 1986). Fur-
thermore, research shows that normal subjects can display sensory-motor impairment (false positives) whereas those
with diagnosed pathology may present with no sensory-motor impairment at all (false negatives), further complicating
the role of sensory-motor assessment (Dean & Woodcock, 1999). Finally, many neuropsychological studies of brain
functions have elected to investigate the effects of brain dysfunction on higher levels of brain functioning (such as
cognitive abilities) rather than on lower level brain functions (sensory-perceptual and motor skills) (Reitan & Wolfson,
The Dean–Woodcock Sensory-Motor Battery (DWSMB, Dean & Woodcock, 2003) is a comprehensive measure of
cortical and subcortical motor and sensory skills. While some studies have established the reliability and validity of this
new sensory-motor battery (i.e., Davis, Finch, Dean, & Woodcock, 2005; Woodward, Ridenour, Dean, & Woodcock,
2002), the effectiveness of the DWSMB to discriminate between neurologically impaired and normal individuals
based on the performance of basic sensory and motor functions has not been fully explored. Additionally, few studies
have been conducted to assess the ability of a sensory-motor battery to identify brain dysfunction without the aid of
cognitive or other neuropsychological information. The present study used a discriminant analysis to investigate the
effectiveness of the DWSMB in differentiating patients with neurological damage from normals without the aid of
other neuropsychological measures.
Participants in this study included 250 patients who had originally been referred for neuropsychological evaluation
to a large midwestern neurological practice and were selected because of diagnosed neurological disorders. The second
group consisted of 250 “normal” volunteers who had denied being diagnosed or treated for neurologic, psychiatric, or
orthopedic disorders. The clinical sample included 125 males and 125 females ranging in age between 4 and 93 years
(mean = 51 years, 7 months) with a level of education ranging from pre-kindergarten to a graduate degree (mean = 11.22
years of education). Among the patients, 88% (N = 220) were right handed, 10.8% (N = 27) were left handed, and 1.2%
ambidextrous (N = 3). The group of 250 participants who denied a history of neurological and psychiatric disorders
(“normals”) was composed of 94 males and 156 females ranging in age from 3 to 95 years (mean = 48 years, 9
months), with a level of education ranging from pre-kindergarten to graduate degree (mean = 11.9 years of education).
Among the normal participants, 90.4% were right handed (N = 226), 6% were left handed (N = 15), and 3.6% were
ambidextrous (N = 9). The patients in the neurologic group were diagnosed as having a neurological disorder by a
neurologist and classiﬁed according to the International Classiﬁcation of Diseases, Ninth Revision (ICD-9) (American
Medical Association, 2000). Diagnoses included cardio-vascular accidents (CVA), traumatic head injuries (TBI),
seizure disorders, multiple sclerosis, dementia, and Parkinson’s disease.
The DWSMB is composed of 8 sensory tests and 10 motor tests that takes approximately 1 hour to administer. These
tests were drawn from long established neurological measures which were improved upon by providing standardized
procedures for administration and scoring. The DWSMB is a standardized measure that combines sensory and motor
tests with qualitative features (such as qualifying and describing a patient’s gait) as well as quantitative scoring of
performance driven tests (i.e., assessing a patient’s strength of grip). Eight of its tests assess sensory functions such
as visual, auditory, and tactile perception and discrimination. The remaining 10 tests assess motor functioning such as
A.G. Volpe et al. / Archives of Clinical Neuropsychology 21 (2006) 203–210 205
upper extremity motor strength, movement, balance, and ﬁne motor skills. Three of the motor tests (Gait and Station,
Romberg Testing, and Coordination) are believed to assess sensory and motor strip functions at the subcortical level
(Davis et al., 2005; Dean & Woodcock, 1999). The sensory portion of the DWSMB yields 21 scores, while the motor
portion yields 15 scores. From a quantitative view, the DWSMB provides standardized procedures for test administration
and uses norms for the interpretation of individual performance. The DWSMB covers a broader range of sensory and
motor functions than either the HRNB or LNNB because it provides additional information necessary to differentiate
between signs of subcortical dysfunction and those related to right hemispheric damage (Dean & Woodcock, 1999).
Furthermore, left–right differences and pathological signs are incorporated with information regarding the level of
1.3. Description of procedures
Each participant was administered the tests of the DWSMB using the standardized administration procedures
described in the Dean–Woodcock Sensory-Motor Battery’s manual (2003). All examiners had been previously trained
in the use of neuropsychological assessment instruments as well as standardized procedures. All participants were
treated in accordance with the Ethical Principles of Psychologists and Code of Conduct (American Psychological
1.4. Data analysis
The Statistical Package for the Social Sciences Base 11.0 for Windows (2001) (SPSS) was utilized to analyze all
demographic information and provide descriptive statistics. For each group, 125 participants were randomly chosen by
the statistical package (SPSS) for an analysis group and the other 125 in each group served as the cross-validation groups.
This was necessary because discriminant analysis creates a regression equation that maximally discriminates between
two groups (neurologically impaired versus normals), and the extent to which it can be used to predict group membership
in future instances is often unclear. Therefore, a discriminant function was obtained on one group and then compared
to the other to establish the generalizability of the prediction. Discriminant analysis is applied to situations in which the
dependent variable is nominal in nature and is used predominately to predict group membership in two or more groups
(in this case group membership: 1 = neurologically impaired and 2 = normal subject). Discriminant function analysis
is used to identify the best set of independent variables,which maximizes the correct classiﬁcation of participants
(Martella, Nelson, & Merchand-Martella, 1999). The resulting discriminant function yields standardized discriminant
coefﬁcients to maximize differentiation between groups (Martella et al., 1999). The cross-validation sample was used
to compare the effectiveness of the discriminant function in terms of diagnosing between neurologically impaired and
normal subjects of the DWSMB.
Three separate discriminant analyses were performed on the data. The ﬁrst two discriminant analyses were conducted
to examine the ability of the DWSMB to differentiate between normal subjects and neurologically impaired patients. The
ﬁnal discriminant analysis was performed to investigate how accurately scores on the DWSMB would identify different
types of neurological impairment diagnoses. The 36 scores yielded by the 18 subtests of the DWSMB were entered as
predictors of subjects’ membership (normal versus neurologically impaired) into a stepwise discriminant analysis. The
result of the discriminant analysis produced one signiﬁcant function: Wilks’ lambda = .405, Chi-square transformation
(10, N = 250) = 219.867, p < .001. The obtained discriminant function accounted for 100% of the explained variance.
The results indicated that 94.4% of the normal population and 91.2% of the neurologically impaired subjects were
correctly classiﬁed by the DWSMB, with a total of 92.8% of identiﬁed cases. Table 1 displays the means and standard
deviation for each of the predictor variables.
The relative magnitude of the standardized discriminant coefﬁcients noted in Table 2 provided information regarding
the relative importance of each predictor variable in discriminating between groups. The stepwise discriminant analysis
procedure maximizes the prediction of the variables and the ability to separate the groups. The ﬁrst variable chosen by
the statistical program is the one that maximizes separation among the groups.
206 A.G. Volpe et al. / Archives of Clinical Neuropsychology 21 (2006) 203–210
First group discriminant analysis-descriptive statistic for each variable (in W-scores)
Variable name Mean S.D.
Normal Patients Normal Patients
Right visual acuity 431.02 425.95 26.27 25.95
Left visual acuity 430.35 424.39 25.45 26.38
Right visual confrontation 484.67 480.77 11.67 16.21
Left visual confrontation 487.27 481.30 10.35 17.20
Both visual confrontation 487.96 481.57 10.72 16.21
Naming pictures of objects 534.72 445.37 9.21 110.89
Right auditory perception 475.52 461.34 14.69 24.05
Left auditory perception 482.83 463.29 14.01 26.21
Both auditory perception 485.42 473.60 13.19 18.73
Right palm writing 500.52 487.68 10.44 21.54
Left palm writing 501.10 486.73 10.66 20.94
Right object identiﬁcation 493.51 486.26 8.79 14.54
Left object identiﬁcation 497.58 490.05 9.69 13.38
Right ﬁnger identiﬁcation 489.09 481.40 5.80 14.81
Left ﬁnger identiﬁcation 489.85 482.61 6.55 15.74
Right hand sim. loc. 510.51 507.32 1.43 11.07
Left hand sim. loc. 510.92 507.86 1.82 10.81
Both hands sim. loc. 514.52 512.04 2.15 8.71
R hands/cheeks sim. loc. 522.06 512.56 8.62 19.73
L hands/cheeks sim. loc. 521.75 513.90 9.00 18.92
Both hands/cheeks sim. loc. 515.03 506.90 8.11 16.82
Gait and station 479.82 460.58 15.56 25.28
Romberg testing 484.30 465.14 23.14 27.63
Cross-construction 494.19 478.51 13.60 18.94
Clock construction 494.91 484.27 10.96 18.90
Right ﬁnger/nose coord. 490.28 468.16 15.36 31.82
Left ﬁnger/nose coord. 490.46 466.48 12.37 30.96
Right hand/thigh coord. 477.05 465.78 18.82 19.43
Left hand/thigh coord. 477.50 465.83 21.34 18.56
Mime movements 499.04 490.26 7.27 14.53
Left–right movements 499.32 493.16 3.22 16.00
Dominant ﬁnger tapping 503.41 498.15 7.18 7.40
Non-dom. ﬁnger tapping 503.72 497.91 9.18 10.13
Expressive speech 495.12 486.39 10.76 14.68
Dominant strength of grip 526.96 521.33 13.33 23.58
Non dom. strength of grip 524.94 519.88 14.59 22.64
sim.: simultaneous; coord.: coordination; dom.: dominant; loc.: localization.
DWSMB variables involved in the discriminant function predicting group membership for the neurologically impaired and normal subjects
Variable Standardized discriminant coefﬁcients
Naming pictures of objects .978
Left hand sim. localization .791
Both hands sim. localization −.556
Left ﬁnger to nose coordination .548
Left hands and cheek sim. loc. −.445
Mime movements .436
Dominant palm writing .239
Right near point visual acuity −.228
Left auditory perception .183
A.G. Volpe et al. / Archives of Clinical Neuropsychology 21 (2006) 203–210 207
Differential diagnoses classiﬁcations resultsa
Diagnosis CVA (%) MS (%) Seizure (%) TBI (%) Dementia (%) Parkinsons (%) Normals (%)
CVA 44.9 8.7 5.8 14.5 11.6 11.6 2.9
MS .0 66.7 .0 16.7 .0 .0 16.7
Seizures 16.0 4.0 40.0 24.0 .0 8.0 8.0
TBI 10.2 12.5 10.2 42.0 4.5 10.2 10.2
Dementia 7.8 3.9 2.0 3.9 62.7 15.7 3.9
Parkinsons 9.1 9.1 9.1 .0 18.2 54.5 .0
Normals .0 3.2 2.4 2.4 1.6 .4 90.0
a 69.0% of original cases correctly classiﬁed.
The group centroids (the discriminant scores for each group when the variable means are entered into the discriminant
equation) were 1.208 for the normals and −1.208 for the neurologically impaired patients.
Among the available subjects, 250 were included in the initial discriminant analysis, with a randomly selected 50%
(N = 250) of the sample set aside for cross-validation. The purpose of cross-validation was to establish the extent to which
the discriminant function obtained in the initial analysis was successful in predicting group membership in another
sample population. When the discriminant function resulting from the ﬁrst group was applied to the cross-validation
group, the difference between the neurologically impaired and normal subjects was again statistically signiﬁcant, with
91.2% of the normal subjects and 83.2% of neurologically impaired patients correctly identiﬁed for a combined total
of 87.2% cases. Although in this analysis the results indicated a somewhat lower percentage of identiﬁcation for the
normal population and equal percentages for the neurologically impaired group than the ﬁrst analysis, these disparities
were not statistically signiﬁcantly different (p > .05).
The above discriminant analyses examined the 36 scores of the DWSMB as a total instrument. To inspect the degree
of contribution of individual tests within the resulting discriminative function, the 10 variables which had demonstrated
a statistically signiﬁcant (p < .001) contribution to the discriminant function were entered into a discriminant analysis.
The result of the analysis was signiﬁcant, Wilks’ lambda = .446, Chi-square transformation (10, N = 500) = 398.025,
p < .001, and indicated that the 10 variables alone predicted 93.0% of the total cases, with 94.8% of the normal subjects
and 91.2% of the neurologically impaired cases correctly classiﬁed.
The canonical correlation was .744 and the group centroids were 1.112 for the normal and −1.112 for the neuro-
logically impaired subjects. Therefore, these 10 predictive variables can successfully predict neurological impairment
and discriminate among neurologically impaired and normal subjects, suggesting the possibility that a shorter version
of the battery could be used for quick screening in a variety of settings.
A stepwise discriminant analysis was conducted with the initial 36 scores from the sensory-motor battery for 250
normal subjects and 250 patients (N = 500) to investigate the ability of the DWSMB to predict individual neurological
diagnoses. The Box’ M statistic was not signiﬁcant (F = 12.5, p = .001), indicating that the homogeneity of variance
assumption was met and a discriminant was allowable. Of the 250 neurologically impaired subjects, 88 were diagnosed
with TBI, 69 with CVA, 51 with dementia, 25 with seizure disorders, 11 with Parkinson’s disease, and 6 with multiple
sclerosis. Six signiﬁcant discriminant functions resulted from this analysis. The ﬁrst function had a canonical correlation
of .798 and accounted for 74.1% of the variance within the data. The second function accounted for 12.3%, the third
for 7.1%, the fourth for 3.1%, and the ﬁfth for 2.6% of variance. Canonical correlations were .474 for the second,
.380 for the third, .262 for the fourth, and .131 for the ﬁfth. The result of the analysis indicated that the sensory-motor
battery correctly classiﬁed 69% of total cases (Table 3) and more speciﬁcally identiﬁed 45% of CVA (31 cases), 67%
of multiple sclerosis diagnosis (4 cases), 40% of seizure disorders (10), 42% of TBI (37 cases), 63% dementia cases
(32), and 55% of Parkinson’s disease patients (6 cases). A total of 90% or 225 of the normal cases were also identiﬁed
correctly within the discriminant analysis.
The current investigation examined the differences between scores of neurologically impaired patients and those
of normal subjects on the tests of the DWSMB. To examine the predictive utility of the DWSMB in distinguishing
normals and neurologically impaired subjects, a total of 36 scores for 125 normal and 125 neurologically impaired
208 A.G. Volpe et al. / Archives of Clinical Neuropsychology 21 (2006) 203–210
subjects were entered into a stepwise discriminant analysis and scores for 125 more normal and 125 neurologically
impaired subjects were analyzed in a cross-validation. The results indicated similar percentages of correct classiﬁcation
as the initial discriminant analysis and cross-validation analysis (correct placement = 92.8%; cross-validation = 87.2%).
These results supported studies that indicated that measures of sensory-motor functioning can be a powerful tool in
classifying a variety of cerebral dysfunctions (Reitan & Wolfson, 2003), and more speciﬁcally, that the DWSMB has
the potential to serve as an effective instrument for predicting group membership among neurologically impaired and
normal individuals. Furthermore, using only 10 of the 36 predictive variables revealed a similar ability to predict
neurologically impaired and normal subjects.
The third discriminant analysis investigated the ability of the DWSMB to predict among diagnoses. Although 90% of
the normal subjects were identiﬁed, the correct classiﬁcation for each of the diagnosis in this study ranged from 40% to
67%. Considering that this outcome occurred by administering only the sensory-motor portion of a neuropsychological
battery, the results are remarkable when considering that only 14% of group classiﬁcation would be expected by chance.
In terms of speciﬁc diagnoses, 67% of multiple sclerosis cases were identiﬁed with the DWSMB. A study by Reitan
and Wolfson (2001) reported identiﬁcation of 15 out of 16 multiple sclerosis patients out of a sample of 112 patients
(93%). However, the entire HRNB was used for the study. In the present study, patients with dementia were identiﬁed
63% of the time. When one takes into consideration the great deal of inter-individual variability on all measures of
neuropsychological functions among patients with dementia, as well as older normal subjects, these results indicate
that the evaluation of sensory-motor abilities can be helpful in conﬁrming such a diagnosis. Parkinson’s disease cases
were limited to only 11 subjects, with the discriminant analysis identifying 54.5% of the cases. The patients within
the CVA group were correctly identiﬁed 45% of the time, while traumatic brain injuries and seizure disorder patients
were correctly classiﬁed, respectively, 42% and 40% of the time. Once again, general impairment and speciﬁc deﬁcits
differ greatly among patients who have sustained a stroke or a traumatic brain injury, and knowledge of the extent and
degree of sensory-motor functioning adds much to the understanding of these patients’ deﬁcits.
The present results add evidence of validity by demonstrating the ability of the DWSMB to differentiate between
individuals with and without neurological impairment. It should be expected that the DWSMB would have high
predictive validity, based not only on the rich history of validation that many of these classic neuropsychological tests
have undergone, but because of the existence of age-based normative data that provides a wider spectrum from which
to examine individual differences than do most sensory-motor batteries that use a cutoff, or dichotomous approach to
scoring. However, as with any new test, the continued analysis of reliability and validity information should progress
with a series of different studies.
Although the results of this study were statistically signiﬁcant, it is important to point out a few minor limitations.
Over 96% of the clinical group was Caucasian in comparison to a reported 75.1% by the 2002 Census and a smaller
representation of African–American and Hispanic individuals participated in this study than appears in the normal
population. Although differences among neurologically impaired and normal individuals are usually not inﬂuenced
by race, generalizability would be enhanced if the sample of participants represented a broader ethnic range. Another
limitation of the current study is that the groups of neurologically impaired individuals consist of a homogeneous
range of disease severity, and diagnosis was not subject to interrater reliability. For example, an individual who had
experienced a profound CVA was included with individuals who had experienced a more minor CVA. A way to address
this in future studies would be to include analyses of additional neuropsychological measures, such as cognitive or
memory measures. Finally, the ratio of subjects in the impaired group to the potential predictive variables offered
a sample small for generalization. Discriminant analysis is greatly inﬂuenced by unequal sample size as well as by
small sample size. Indeed, within the diagnostic discrimination analysis, the small number of cases available for
all of the diagnoses, and especially in the Parkinson’s disease and multiple sclerosis cases, placed some constraint
on the way the analysis was conducted and therefore introduced limitations in the generalizability of the results. The
DWSMB was better able to discriminate CVA, traumatic brain injury, and seizure cases from normal subjects. However,
the administration of a full neuropsychological battery would improve these predictions and provide the additional
information necessary to fully determine the nature and degree of the impairment (Reitan & Wolfson 2001).
The goals of neuropsychological assessment are much more complex than simply establishing the presence or
absence of neurological impairment, but this study is a stepping stone for further investigations. One area of future
research should focus on establishing how speciﬁc sensory-motor deﬁcits as measured by the DWSMB relate to
different academic difﬁculties later in life. Measurements of sensory-motor skills have already been found to be a
predictor of later cognitive performance (Snow, Blondis, Accardo, & Cunningham, 1993). When looking at the source
A.G. Volpe et al. / Archives of Clinical Neuropsychology 21 (2006) 203–210 209
of academic deﬁciencies in children, the combination of measures of sensory-motor functions and of intellectual
and cognitive functions has been found to differentiate between diagnoses of brain damage and behavioral disorders
(Reitan & Wolfson, 2003). Furthermore, longitudinal studies on the development of sensory and motor abilities of
academically disabled students suggest that neurological damage may exhibit itself as a persistent delay in the sensory
areas even when motor abilities have improved (Snow et al., 1993). Further investigation of the relationship between
the two could lead to more effective instructional and intervention strategies early in life (Reitan & Wolfson, 2003).
In fact, below-normal sensory and motor performance has been found to be indicative not only of frank neurological
impairment but also of more subtle learning disabilities (Gaddes & Edgell, 1994). Although attempted in this study,
identiﬁcation of speciﬁc diagnosis will be more generalizable with a larger data set, and possibly with the administration
of the entire DWNB. In addition, many of the patients whose performance was examined in this study were referred
for neuropsychological testing to conﬁrm their neurological diagnoses. Future studies should include patients with
psychiatric disorders so as to examine the ability to predict patients with neurological and/or psychiatric diagnoses
from normal subjects.
This investigation assessed the ability of a new sensory-motor battery, the DWSMB, to identify brain dysfunction.
The DWSMB was found to be successful in differentiating neurologically impaired patients from normal individuals,
conﬁrming that sensory-motor functioning is of utility in the prediction of neurological integrity. Additionally, the
DWSMB was able to identify different neurological disorders at an adequate rate, especially considering this study only
assessed sensory-motor functioning. The advantages of this sensory-motor battery in comparison with other measures
of neurological damage are that it provides a broader neurological range, addressing not only a variety of pathognomonic
measures but also the evaluation of subcortical functions. In addition, the DWSMB’s use of standardized administration
procedures and consideration of behavioral information in its evaluation has resulted in the accurate discrimination
between normal and neurologically damaged individuals at rates similar to those resulting from the administration
of full neuropsychological batteries. Furthermore, testing time is much shorter than most full neuropsychological
assessments, and, from the results of this investigation, a shorter version of the DWSMB might even have the potential
to be as effective as the whole battery for certain diagnoses.
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