Published: van Iterson, L & A.S Kaufman (2009) Intra-individual Subtest Variability on the Dutch Wechsler

Intelligence Scales for Children--Revised (WISC-RNL) for children with Learning Disabilities, Psychiatric

Disorders, and Epilepsy. Psychological Reports, 2009, 105, 995-1008.

Running head: SUBTEST VARIABILITY ON THE WISC-RNL

Intra-individual Subtest Variability on the Dutch Wechsler Intelligence Scales for Children--

Revised (WISC-RNL) for children with Learning Disabilities, Psychiatric Disorders, and

Epilepsy

Loretta van Iterson

SEIN Stichting Epilepsie Instellingen Nederland &

School De Waterlelie, Cruquius

Alan S. Kaufman

Yale University School of Medicine

Author note

Please address correspondence to Loretta van Iterson, SEIN, Stichting Epilepsie Instellingen

Nederland. Afdeling psychologie. Postbus 21 2100 AA Heemstede, The Netherlands. E-mail:

lviterson@sein.nl

Abstract

It is common practice to look at disparities among subtest scores (“scatter”) on an intelligence

test to establish if a score is deviant. However, it remains unclear whether subtest scatter

reflects primarily normal variation within individuals or is clinically meaningful. The present

study explored this issue based on data from 467 children with developmental disabilities

tested on the Dutch WISC-RNL. Of these children, 132 had learning disabilities, 178 had

psychiatric disorders, and 157 had epilepsy. Subtest scatter was defined as scaled-score range

(highest minus lowest scaled score). When contrasted with “normal scatter,” the overall

sample revealed higher ranges on the Performance Scale and Full Scale, although effect sizes

were small. Analysis of the data for the three separate clinical samples revealed unusual

scatter only for the sample of children with psychiatric disorders. When comparing the

clinical samples, scaled-score range was larger for the sample of children with psychiatric

disorders than for those with epilepsy. Two distinct subsamples revealed elevated ranges with

moderate effect sizes: children with autistic spectrum disorders and children with left

hemisphere seizures. These results suggest that elevated subtest scaled-score range might

characterize specific clinical samples rather than denoting an overall sign of pathology.

(199 words)

Key-words: Wechsler Intelligence Scale for Children, subtest scaled-score range, scatter,

intra-individual variability, inter-subtest variability, learning disabilities; psychiatric

disorders; childhood epilepsy.

In child neuropsychology, the clinician frequently looks for strengths and weaknesses

in the cognitive profile, often operationalized as a positive or negative difference of 1 or 2

standard deviations, in order to make a diagnosis of a developmental disorder (Sattler, 2001).

This approach is based on the assumption that a child’s profile should be uniform, and that

undue inter-subtest or intra-individual variability (scatter) can be interpreted as pointing

toward a specific strength or deficit. Two common indexes of scatter are subtest scaled-score

range (the simple difference between the highest and the lowest score in a profile) and

univariate scatter (the number of subtests deviating 1 SD from an individual’s own mean).

Kaufman (1976, 1979) showed that large intra-individual variability on these indexes, far

from being unusual, was seen frequently in the standardization sample of the WISC-R. Later,

Silverstein (1987) demonstrated that the empirically-derived moments (mean and SD) from

Kaufman's data were a function of the psychometric qualities of the test and could be

estimated from the average intercorrelations among the subtests comprising the scales. Both

subtest scaled-score range and univariate scatter make use only of the extreme values in a

profile. As a more sensitive measure of intra-individual variability, the Profile Variability

Index was proposed which, like a standard deviation, uses information derived from all

subtests (Matarazzo, Daniel, Prifitera, & Herman, 1988; McLean, Reynolds, & Kaufman,

1990). Interestingly, subtest scaled-score range was shown to correlate highly with Profile

Variability Index (Matarazzo, Daniel, Prifitera, & Herman, 1988; Boone, 1993).

The question of whether elevated intra-individual variability is a sign of pathology, or

only a reflection of the psychometric properties of the test, remains unsettled. Some

researchers have provided evidence for elevated variability in pathology (Zimet, Goodman

Zimet, Farley, Shapiro Adler, & Zimmerman, 1994; Mayes, Calhoun, & Crowell, 1998;

Greenway & Milne, 1999; Ryan, Tree, Morris, & Gontkovsky, 2006), while others ardently

advocate against any use of measures based on inter-subtest variability (Watkins & Glutting,

2000; Watkins, Glutting, & Youngstrom, 2005). In the studies by Watkins and his colleagues,

inter-subtest variability did not have a significant incremental validity in predicting academic

achievement over and above Full-Scale IQ in samples of exceptional children, mainly

children with learning disabilities. The authors argue that inter-subtest variability is of no use

as a diagnostic indicator, and its use can be considered “prescientific” (Watkins, Glutting, &

Youngstrom, 2005, p.263).

In spite of this controversy, recent Wechsler test manuals have incorporated subtest

scaled-score range in the form of base rate tables (e.g. for the WISC-III, Wechsler, 1992, and

the WISC-IV, Wechsler, 2004b). For the Dutch, the adult test version includes subtest scaled-

score range (Wechsler, 2004a), while the children’s versions do not (van Haasen, de Bruyn,

Pijl, Poortinga, lutje Spelberg, Vandersteene, et al., 1986; Wechsler, 2005).

In order to be a sign of pathology, the intra-individual variability should be

significantly different in clinical samples when compared to the standardization sample.

Significant scatter should not only be interpreted as reliable scatter – that is, genuine and not

the effect of measurement error – but also as uncommon, in the sense that the magnitude of

occurrence within the normal population is small, e.g., 5% (Crawford & Allan, 1996).

Because the Wechsler scales are well-standardized and well-researched, they are used,

in the present paper, as a model to further evaluate whether intra-individual variability is a

clinically meaningful measure of pathology. This study focused on subtest scaled-score range

because it is practical and easily computed by clinicians and is included in most recent test

manuals. Furthermore, it correlates highly with Profile Variability Index. Univariate scatter,

on the other hand, was not included because its distribution is skewed, preventing

parametrical analyses. The data refer to the Dutch WISC-R (for this purpose: WISC-RNL; van

Haasen et al., 1986) and were collected up to 2007. This version was in use in the Netherlands

for a prolonged time, thus allowing the collection of larger samples. For the newer test

version, it will take some time before sufficiently large samples are accrued, but underlying

notions about subtest scatter can be understood independent of test version. The expected

mean values of subtest scaled-score range and the cut-off values for uncommonly large ranges

for the WISC-RNL were estimated according to Silverstein (1987, 1989), aided by Owen’s

(1962) range statistics. These estimates draw on the averaged intercorrelations between the

subtests, which came from the technical manual of the WISC-RNL (de Bruyn, Vandersteene, &

van Haasen, 1986, p 139; from N = 1961 children).

Based on WISC-RNL data on 467 children from three clinical samples, the aims of this

study were (1) to study whether subtest scaled-score range in children with developmental

disabilities shows differences compared to expected (normal) values; (2) to study whether

there are differences among the clinical samples, and, if so, (3) to explore whether specific

subsamples account for these differences; and (4), to report rates of individuals with

uncommonly large subtest scaled-score ranges found in clinical samples.

Methods

Participants were N = 467 children, aged six to 16 years, with FS-IQs > 75. Overall,

353 (76%) were male. The children were entitled to benefit from distinct special school

services in The Netherlands, according to national regulations (e.g., Resing, Evers, Koomen,

Pameijer, Bleichrodt, & van Boxtel, 2002). These regulations describe the criteria for

placement in different settings specialized in, respectively, (specific) learning disabilities,

childhood psychiatric disorders, or childhood epilepsy. Generally, information from four

sources is weighted by an independent committee. These sources are the family of the child,

the present school, a psychologist who did the assessment (including the intelligence testing),

and an educational, psychiatric or medical specialist. For learning disabilities, specified

criteria in terms of academic failure must be met; for psychiatric disorders, a DSM-IV diagnosis

from a psychiatrist or qualified mental health psychologist is required; for epilepsy, a

diagnosis from a neurologist is required. In all cases, the difficulties caused by the diagnosed

condition must exceed the competencies of the regular school. Normal intellectual abilities

were a further criterion for the schools of the first two samples, but not the third (epilepsy). As

indicated, in this study FS-IQ was set to be above 75 for all. Co-morbidity is a common

phenomenon in childhood developmental disabilities and the samples are diagnostically

heterogeneous; the primary diagnosis as reflected by special school placement was the

criterion for inclusion in a sample. Diagnostic group membership – type of special school –

was the independent variable in this study. Demographic data are presented in Table 1 and

data on the Wechsler scales are shown in Table 2.

The first sample consisted of N = 132 children with (specific) learning disabilities and

the second sample consisted of N= 178 children with psychiatric disorders. The latter group

included children with neurodevelopmental disorders as well as children with behavioral and

emotional disorders related to major life events (e.g. traumas). The main diagnoses of this

sample were autism spectrum disorders (ASD), conduct disorders or oppositional defiant

disorders, reactive attachment disorders, attention deficit and hyperactivity disorders, tic

disorders, and depression. The subsamples are listed in Table 1. The percentages add up to

over 100% due to psychiatric co-morbidity.

The third sample consisted of N = 157 children with seizure disorders. Mean age at

epilepsy onset was 5.6 years (ranging from the first day of life to age 15 years with SD = 3.2).

Mean duration of epilepsy was 4.0 years (SD = 3.2). Seizure type classification, side of

epilepsy onset, and information on medication and neuroimaging are presented in Table 1.

For each participant, subtest scaled-score range was calculated for five Verbal, five

Performance, and ten Full-Scale subtests. Verbal, Performance, and Full-Scale subtest scaled-

score range was the dependent variable in the study. Mean scores are given in Table 3; z-

converted means are depicted in Figure 1. Levene’s testing for homogeneity of variances

showed no significant values for ANOVA or ANCOVA. ANOVA revealed differences in mean age

(age was higher in learning disabilities compared to epilepsy) and mean P-IQ (but not V-IQ or

FS-IQ), indicating higher P-IQ in the samples of children with learning disabilities and

psychiatric disorders compared to the sample of children with epilepsy. Also, chi-square

showed that boys and girls were unevenly distributed among the samples; significant

differences were found, indicating that the rate of boys was higher in the sample with

psychiatric disorders than the sample with epilepsy. Three separate ANCOVA’s were

undertaken (for Verbal, Performance and Full-Scale subtest scaled-score range), controlling

for the pre-existing differences in P-IQ, age, and sex. With multiple, one-sided, one-sample t-

tests, the observed clinical values were compared to the estimated expected values

(Silverstein, 1987), and effect sizes were calculated accordingly (Cohen, 1988, p.45). Overall,

alpha was set at .05 and Bonferroni corrections were used to control for family-wise errors.

With chi-square, rates of children with uncommonly high subtest scaled-score range (Verbal

Scale: ≥ 8 points; Performance Scale ≥10, and Full-Scale ≥11), expected in ~5% of the

normal population (Silverstein, 1989), were compared to this value. As this value was seen as

an approximation only, alpha was set to .001. Rates of uncommonly high subtest scaled-score

range were also compared between the clinical samples.

Results

Verbal Scale.

Comparison of means. Table 3 presents the expected mean subtest scaled-score range

for the Verbal Scale (mean = 4.7, SD = 1.7) and the observed valued for the distinct samples,

together with the results of the one sample t-tests, and Figure 1 depicts the z-converted values

of subtest scaled-score range. No differences were found between the mean expected values

and the observed values of the total sample or any of the distinct clinical samples (Table 3).

No differences were found between the means of the clinical samples (ANCOVA: F(462,2) =

0.138, p = .871, n.s).

Rates of uncommonly large ranges. Large ranges (≥ 8 points) were found,

respectively, in 8.3%, 12.9% and 15.3% of the children with learning disabilities, psychiatric

disorders, and epilepsy. Compared to the expected rates, these values reached significance for

psychiatric disorders (Χ2 = 23.51, p < .001) and epilepsy (Χ2 = 34.97, p < .001). Chi-square

revealed no difference in the distributions of uncommonly large ranges between the clinical

samples. There was an almost twofold rate (likelihood ratio 1.8, 95% Confidence Interval

[CI]: 0.93 – 3.6) of children with epilepsy versus children with learning disabilities.

Performance Scale.

Comparison of means. The estimated expected mean subtest scaled-score range was

5.8 (SD = 2.4). The total sample and the children with psychiatric disorders differed from the

expected value (Table 3). Significant differences were also suggested among the clinical

samples (ANCOVA: F(461,2) = 3.024, p = .050, partial η2 = .01). However, pair-wise

comparisons between the clinical samples did not yield significant results.

Rates of uncommonly large ranges. Large ranges (≥ 10 points) were found in 7.6%,

12.9%, and 7.0% of the children with, respectively, learning disabilities, psychiatric disorders,

and epilepsy. Compared to expected values, these percentages were elevated for psychiatric

disorders only (Χ2 = 23.51, p < .001). Chi-square revealed no significantly different rates

among the samples. Likelihood ratios were 1.7 (95% CI: 0.8 – 3.5) and 1.8 (95% CI: 0.93 –

3.7) for children with psychiatric disorders versus, respectively, children with learning

disabilities and children with epilepsy.

Full Scale.

Comparison of means. The expected mean subtest scaled score range was 7.3 (SD =

2.1). Values differing significantly from expected were found for the total sample and the

sample with psychiatric disorders (Table 3). Significant differences were found between

clinical samples (ANCOVA: F(462,2) = 4.130; p = .017, partial η2 = .02); specifically, subtest

scaled-score range was higher in children with psychiatric disorders than in children with

epilepsy.

Rates of uncommonly large ranges. Larges ranges (≥ 11 points) were found,

respectively, in 6.8%, 12.9%, and 10.8 % of the children with learning disabilities, psychiatric

disorders and epilepsy. Compared to expected values, significant differences were found for

psychiatric disorders (Χ2 = 23.51, p < .001) and epilepsy (Χ2 = 11.23, p = .001). Again,

differences of the large ranges between samples showed a trend that did not reach statistical

significance. Notably, however, there was almost a twofold rate (likelihood ratio 1.9, 95% CI

0.9 - 4.0) for children with psychiatric disorders compared to children with learning

disabilities.

Subsamples.

Although it is beyond the scope of this paper to enter into detail on all subsamples,

two subsamples were identified as showing conspicuously elevated subtest scaled-score

ranges relative to expected values: (a) from the sample with psychiatric disorders, the

subsample with autistic spectrum disorders (ASD, N = 58) was identified; and (b) from the

epilepsy sample, children with left focal onset seizures (LH; N = 33) were identified. Data on

these subsamples are also included in Table 2, Table 3, and Figure 1.

Table 3 shows that the ASD sample had a significantly larger subtest scaled-score

range than the expected values on the Verbal Scale, Performance Scale and Full Scale, all

with moderate effect sizes. When the ASD sample was compared to the other children with

diagnoses of psychiatric disorders (N = 120) in the psychiatric sample, mean subtest scaled-

score range was elevated for the ASD sample on the Verbal Scale (t(94.7,1) = 2.49, p = .014,

ES = 0.5) and the Full Scale (t(176,1) = 2.44, p = .016, ES = 0.4). Uncommonly large ranges

were found for the Verbal Scale in 20.7% of these children, for the Performance Scale in

15.5%, and for the Full Scale in 17.2%. The percentages were significantly elevated when

compared to expected values for the Verbal Scale (Χ2 = 30.06, p < .001), Performance Scale

(Χ2 = 13.51, p < .001), and Full Scale (Χ2 = 18.3, p < .001). Classificatory statistics revealed

that when the ASD group was contrasted to the others children with psychiatric disorders,

uncommonly large ranges had classificatory utility for the Verbal Scale: sensitivity was 21%,

specificity was 91%, Positive Predictive Power (PPP) was 52%, Negative Predictive Power

(NPP) was 70%, and likelihood ratio was 2.26 (95% CI 1.06 - 4.81). These values indicated

that when a child with psychiatric disorders is found to have a subtest scaled-score range of 8

or more points on the Verbal Scale, it will more likely belong to the group with autistic

spectrum disorders.

Within the sample of children with psychiatric disorders, none of the other subsamples

showed elevated subtest scaled-score range consistently on all scales. However, two

subsamples of children with neurocognitive developmental disorders showed elevations on

one scale—specifically, the subsample with conduct disorders had substantial scatter on the

Performance Scale and the subsample with tic disorders had elevated scatter on the Verbal

Scale. These data merely suggest hypotheses for future study, but are not presented here

because many children had multiple diagnoses and the sample sizes were too small to permit

meaningful analyses.

Table 3 shows that mean scaled-score range was higher than expected in the sample of

children with left hemisphere seizures. Significantly elevated values were seen for the Verbal

Scale (small effect size) and the Full Scale (moderate effect size). Such elevations were not

seen in the other epilepsy subsamples; planned comparisons indicated that values were

significantly different compared to the right hemisphere seizure-group for the Verbal Scale

(t(137,1) = 2.05, p = .042, ES = 0.3). Large range was found for the Verbal Scale in 21.2% of

these LH children, for the Performance Scale in 6.1%, and for the Full Scale in 15.2%. The

percentages were significant when compared to expected values for the Verbal Scale only (Χ2

= 18.26, p < .001). Classificatory statistics revealed that when the left hemisphere seizure-

group was contrasted to the right hemisphere seizure-group, there was a clear trend to find

more children with uncommonly large ranges in the Verbal Scale in the left hemisphere

seizure-group. The valued failed to reach significance due to lack of statistical power:

sensitivity was 21%, specificity was 96%, PPP was 88%, NPP was 47%, and likelihood ratio

was 6.19 (95% CI 0.67 - 38.7).

Conclusions and Discussion

The assertion that intra-individual variability is elevated in pathology, taken for

granted by some researchers and opposed by others, was the subject of analysis in this study,

which focused on subtest scaled-score range for clinical samples of children with learning

disabilities, psychiatric disorders, and epilepsy. Analyses were conducted at three levels.

At the broadest level, 467 children from three categories of developmental disabilities

(learning disabilities, psychiatric disorders, and epilepsy) were compared to the expected

(“normal”) values. Significant elevations were found in the Performance and Full Scales –

with conspicuously small effect sizes. This finding suggested that the study was profiting

from the effects of a relatively large sample size and also that possible meaningful

information was being masked by focusing on the heterogeneous total group. At the second

level of analysis, each of the clinical samples was compared to the expected values and to

each other. The sample with psychiatric disorders showed significantly more than normal

intra-individual variability on both the Performance and Full Scales. Also, the sample with

psychiatric disorders showed more variability than the sample with epilepsy on the Full Scale.

Effect sizes were larger than for the total clinical sample, but were still small. At the third and

most specific level of analysis, two homogeneous subsamples were subjected to further

scrutiny. It appeared that the sample with ASD (within psychiatric disorders) and the sample

with focal LH seizures (within epilepsy) showed elevated scatter, compared both to the

expected values and to the other children in their respective clinical original samples. For

ASD, this was true for all three scales (moderate effect sizes). For left hemisphere epilepsy,

this was true for the Verbal Scale (small effect size) and the Full Scale (moderate effect size).

When evaluating the percents of children with uncommonly large ranges, children

with (specific) learning disabilities did not display any unusual elevations relative to groups of

normal children. However, rates were increased in children with psychiatric disorders on all

scales, specifically for the ADS subsample. Rates were also increased for the sample of

children with epilepsy on the Verbal and Full Scales, more clearly so in the subsample of LH

seizures.

The fact that the Performance Scale and not the Verbal Scale yielded the significant

differences in the primary samples of this study is consistent with a diverse body of

neuropsychological literature that has shown Wechsler’s Performance subtests to be more

sensitive to brain injury and brain dysfunction than Verbal subtests (Kaufman &

Lichtenberger, 2006, chapters 8 and 9). Nonetheless, the present study suggests that elevated

subtest scaled-score range can also be seen on the Verbal Scale in specific samples.

For children with (specific) learning disabilities, no elevations were found on any

measure. These results are in line of earlier studies (Watkins, Glutting, & Youngstrom, 2005;

Flanagan & Kaufman, 2009). For children with ASD, elevated intra-individual variability has

been reported earlier (Joseph, Tager-Flusberg, & Lord, 2002). For children with epilepsy, to

the authors’ knowledge, no such studies have been reported, but the results are in line with the

large V-IQ > P-IQ discrepancies reported for children with unilateral focal onset epilepsy (van

Iterson & Augustijn, 2006) regardless of side of seizure onset. The elevations in subtest

scaled-score range on the Verbal Scale in left hemisphere epilepsy may be the result of

plasticity in the developing brain (Vicari, Albertoni, Chilosi, Cipiriani, Coni, & Bates, 2000).

The results found for children with ASD and children with epilepsy are interesting in the light

of recent research on the commonalities underlying both conditions and the findings of high

rates of subclinical EEG abnormalities in children with ASDs even in the absence of manifest

clinical seizures (Spence & Schneider, 2009).

Effect sizes increased when the selected samples were more homogeneous, suggesting

that specific samples of children with developmental disabilities may show elevated intra-

individual variability while others may not, or may even show decreased variability. Thus,

studies of subtest scaled-score range and their interpretation should take into account type of

pathology.

Scaled-score range was not found suitable for classification purposes between the

large samples; 95% confidence intervals for likelihood ratios were non-significant, though

some trends could be found. This is not surprising as scatter is a non-specific measure which

does not provide an answer as to where the variability is coming from, or if it follows some

specific pattern.

Classificatory statistics applied on selected samples indicated that an uncommonly

large range in the Verbal Scale was more likely to belong to a child with ASD and not a child

with “another diagnosis” within psychiatric disorders. Also the data suggest that uncommonly

large variability on the Verbal Scale may be characteristic of children with left, but not right,

hemisphere onset seizures; likelihood ratio was not significant, probably due to small sample

sizes.

In line with the results of this study, and pertinent to the discussion of the

interpretability of scores beyond the summed scores of a scale (Watkins, Glutting, &

Youngstrom, 2005; Flanagan & Kaufman, 2009), it is worthy noting Saling’s (2009)

perspective. Based on the results of research within a highly specific area of research in

neuropathology – epilepsy surgery – Saling (2009), advocates against the use of scales of

summed scores in neuropsychological assessment of memory functions and argues in favor of

task specific measurement.

Intra-individual variability was studied with the WISC-RNL-version – which has now

been replaced by the WISC-IIINL, and by the WISC-IV

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subtest variability within WISC-IV Factor Indexes. The appreciation of a true difference

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Table 1

Samples and Diagnoses

Sample n % n %

Learning Disabilities 132 32.3

Psychiatric Disorders 178 38.1

Autism Spectrum Disorders 58 32.6

Conduct Disorders / Oppositional Defiant Disorders 36 20.2

Attachment Disorders 28 15.7

Attention Deficit and Hyperactivity Disorders 27 15.2

Tic Disorders 19 10.7

Depression 14 7.9

Other 66 37.1

Epilepsy 157 33.6

Seizure Type:

Focal Onset / Localisation Related Seizures 87 55.4

Left Hemisphere 33

Right Hemisphere 24

Bilateral / Multifocal 32

Generalized Seizures 32 20.4

Uncertain whether Focal or Generalized 21 13.4

Unknown 17 10.8

Anti-epileptic Drug:

0 9 5.7

1 59 37.6

>1 63 40.1

n a 26 16.6

MRI positive data 29 18.5

Total 467 100

Table 2

Mean Age, Mean WISC-RNL IQs and Sex for Three Clinical Samples and Two Subsamples

(Autism Spectrum Disorders and Left Hemisphere Onset Seizures)

age (yrs) V-IQ P-IQ FS-IQ

boys M SD M SD M SD M SD

n n % & range & range & range & range

Learning Disabilities 132 91 68.9 12.8a 1.3 93.3 10.8 97.3 12.3 94.6 10.8

7.6 to 15.6 72 to 119 68 to 125 77 to 124

Psychiatric Disorders 178 159 89.3a 10.9 2.7 93.8 11.5 95.7 13.5 93.9 10.7

6.0 to 16.7 70 to 132 61 to 130 76 to 127

Autism Spectrum Disorders 58 56 96.6 9.9 2.3 96.2 12.2 96.6 14.2 95.6 11.2

6.2 to 15.1 70 to 132 61 to 124 76 to 127

Epilepsy 157 103 65.6 9.7 2.7 95.3 12.1 91.0b 11.9 92.5 10.7

6.2 to 16.7 71 to 134 66 to 135 76 to 125

Left Hemisphere Seizures 33 26 78.8 9.5 2.5 96.6 11.1 89.1 11.7 92.3 8.7

6.3 to 16.1 71 to 120 66 to 120 78 to 108

Total 467 353 75.6 11.1 2.7 94.2 11.5 94.6 12.9 93.6 10.8

6.0 to 16.7 70 to 134 61 to 135 76 to 127 a significantly higher than sample with epilepsy b significantly lower than learning disabilities and psychiatric disorders

Fig 1

Mean z-Converted Uncorrected Subtest Scaled-score Range Values and SEM-bars for Verbal

(black), Performance (white) and Full Scales (patterned), for Three Clinical Samples and Two

Subsamples

0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

Total LearningDisabilities

PsychiatricDisorders

AutismSpectrumDisorders

Epilepsy LeftHemisphere

Seizures

z-sc

ore

Verbal range

Performance range

Full Scale range

SUBTEST VARIABILITY ON THE WISC-RNL

21

Table 3

Subtest Scaled-score Range: Means, SDs, t-Values, Probabilities and Effect Sizes for Three Samples and Two Subsamples contrasted to

Expected Values.

Verbal Scale Performance Scale Full Scale

Sample n M SD ta p ES M SD ta p ES M SD ta p ES

Expected Valueb 4.7 1.7 5.8 2.1 7.3 1.9

Learning Disabilities 132 4.8 2.0 0.52 ns - 5.8 2.3 0.37 ns - 7.4 2.1 0.28 ns -

Psychiatric Disorders 178 5.0 2.0 1.84 ns - 6.5 2.6 3.77 < .001 0.3 8.1 2.2 4.83 < .001 0.4

Autism Spectrum Disorders 58 5.6 2.3 2.85 .003 0.5 6.8 2.8 2.74 .004 0.5 8.7 2.3 4.48 < .001 0.7

Epilepsy 157 5.0 2.0 1.76 ns - 6.0 2.4 1.10 ns - 7.7 2.3 1.81 ns -

Left Hemisphere Seizures 33 5.3 2.2 1.64 .050 0.4 6.5 2.4 1.63 ns - 8.4 2.0 3.00 .003 0.6

Total 467 4.9 2.0 2.45 ns - 6.1 2.4 3.28 .001 0.2 7.8 2.2 4.19 < .001 0.2a one-sample t-tests for comparisons against estimated (expected) value, one-tailed tests, d.f. = N - 1 in all cases. b estimated expected values according to Silverstein (1987)