A Comprehensive Profile of Decoding and Comprehension in Autism Spectrum Disorders

Abstract

The present study examined intake data from 384 participants with autism spectrum disorders (ASD) and a comparison group of 100 participants with dyslexia on nine standardized measures of decoding and comprehension. Although diagnostic groups were based on parental reports and could not be verified independently, we were able to observe significant distinctions between subject groups. Overall findings confirm previous results of a disassociation between decoding and comprehension in ASD. Using a larger sample than previous studies and a greater variety of measures, a pattern of relatively intact decoding skills paired with low comprehension was found in autism, PDD-NOS, and Asperger's. In contrast, the dyslexic group showed the opposite pattern of stronger comprehension and weaker decoding.

Full text

ORIGINAL PAPER
A Comprehensive Profile of Decoding and Comprehension
in Autism Spectrum Disorders
Sabine V. Huemer •Virginia Mann
Published online: 14 November 2009
The Author(s) 2009. This article is published with open access at Springerlink.com
Abstract The present study examined intake data from
384 participants with autism spectrum disorders (ASD) and
a comparison group of 100 participants with dyslexia on
nine standardized measures of decoding and comprehen-
sion. Although diagnostic groups were based on parental
reports and could not be verified independently, we were
able to observe significant distinctions between subject
groups. Overall findings confirm previous results of a dis-
association between decoding and comprehension in ASD.
Using a larger sample than previous studies and a greater
variety of measures, a pattern of relatively intact decoding
skills paired with low comprehension was found in autism,
PDD-NOS, and Asperger’s. In contrast, the dyslexic group
showed the opposite pattern of stronger comprehension and
weaker decoding.
Keywords Autism spectrum disorders Decoding 
Comprehension Reading Dyslexia
A successful reader has the ability to accurately and flu-
ently decode words so as to comprehend their meaning in
isolation and in context. For many children, decoding skills
and reading comprehension develop hand in hand (Mirenda
2003; Nation and Norbury 2005). Children with ASD,
however, have been reported to show a disassociation
between decoding and comprehension: while decoding
skills in high-functioning children with autism and As-
perger’s syndrome may be intact (Frith and Snowling 1983;
Griswold et al. 2002; O’Connor and Klein 2004), their
reading comprehension is often lower than expected for
their level of reading ability (Minshew et al. 1994;
O’Connor and Hermelin 1994). Several studies have
examined the connection between poor reading compre-
hension and autism (Nation et al. 2006; Nation and Nor-
bury 2005; Wahlberg and Magliano 2004). Comprehension
problems in ASD may be due to difficulties integrating
information in context (Frith 2003), deficits in verbal skills
and oral language ability (Mirenda and Erickson 2000, pp.
349–351), impairments in communication (Nation and
Norbury 2005), and/or general language impairment
(Tager-Flusberg and Joseph 2003).
Early on, Kanner (1943), and also Ricks and Wing
(1975) wrote about reading comprehension problems in the
context of other language deficits in autism, such as literal
speech dominated by the use of concrete words. Later
studies of single-word comprehension suggested that ASD
individuals can mentally represent at least some single-
word meanings: Colors (Bryson 1983) as well as concrete
and abstract words (Eskes et al. 1990) all appear to be
processed normally. It is the comprehension of linguistic
units beyond the word level that present increased diffi-
culty for ASD individuals (O’Connor and Klein 2004).
Wahlberg and Magliano (2004) reported that high-func-
tioning readers with autism had difficulty understanding
written text, likely because they were not able to draw in
relevant background information to interpret ambiguities in
discourse. Other comprehension tasks related to under-
standing a sequence of words, such as following complex
This study was originally prepared as the Sabine V.Huemer’s master
thesis at Antioch University Los Angeles.
S. V. Huemer V. Mann
Department of Cognitive Sciences, University of California,
Irvine, 2201 Social & Behavioral Sciences Gateway Building
(SBS), Irvine, CA 92697-5100, USA
S. V. Huemer (&)
1215 S. Gertruda Ave., Redondo Beach, CA 90277, USA
e-mail: shuemer@uci.edu
123
J Autism Dev Disord (2010) 40:485–493
DOI 10.1007/s10803-009-0892-3

oral instructions, have been shown to be impaired even in
high-functioning individuals with autism (Goldstein et al.
1994). These findings underscore the disassociation of
form (language structure) and function (language use)
present in autism (Tager-Flusberg 1981; Tager-Flusberg
and Joseph 2003).
While reading comprehension is impaired, the compre-
hension deficits in ASD are not likely to be the result of
poor decoding skills. Previous research found that ASD
students were as skilled in nonword reading as typically
developing controls (Frith and Snowling 1983) and showed
the expected advantage for reading phonetically regular
words more easily than phonetically irregular words dem-
onstrating a phonetic decoding strategy alongside intact
lexical skills for familiar words (O’Connor and Klein
2004). Minshew et al. (1994) found general patterns of
stronger word-reading skills in the presence of deficits in
language comprehension and abstract reasoning in children
with high-functioning autism and Asperger’s. Thus, at least
one aspect of structure, the phonetic structure of words,
appears relatively intact among some of the diagnostic
categories in ASD.
Higher level elements of decoding, however, have been
shown to be impaired or delayed in autism (Tager-Flusberg
et al. 1990), especially when it comes to more complex
grammatical structures. These higher-level skills impact
comprehension but they are also influenced by compre-
hension, which facilitates contextual decoding. Therefore,
fluent reading of meaningful text can pose a challenge to
the ASD population, since this task requires complex
multi-dimensional cognitive abilities and relies more
heavily on general linguistic and semantic skills than on
word-level measures of decoding (Nation and Snowling
1997). Fluent reading involves decoding with concurrent
processes activated, such as the processing of syntax and
semantics (Katzir et al. 2006), which would predict that
ASD children will be less efficient text-readers than single-
word readers.
In one of the few systematic studies of reading skills in
autism, Nation et al. (2006) assessed 41 children with ASD
in four components of reading skills: word recognition,
nonword decoding, text-reading accuracy, and reading
comprehension. Nation reported that 65% of the partici-
pants showed poor reading comprehension with standard
scores at least one SD below population norms. Of the 32
children with measurable reading ability (nine of the
youngest children, or 22%, were completely unable to
read), 10.3% achieved reading comprehension scores at
least two SDs below their reading accuracy scores. All but
one of the participants showed reading comprehension
skills below decoding skills, clearly demonstrating a dis-
crepancy between the two elements. Contrary to Frith and
Snowling (1983), Nation’s group of participants also
showed problems in decoding nonwords: 42% were at least
one SD below population norms and 22% at least two SDs
below population norms which may be linked to poor
phonological processing. Nation concludes that many
children with ASD have low levels of reading accuracy and
she implicates low decoding skills as one of the factors in
reading comprehension deficits in ASD.
In contrast to the ASD population, children with dys-
lexia tend to be poor decoders despite adequate linguistic
comprehension and intellectual functioning. Where visual
reversals were once thought to be responsible, the present
consensus offered by the International Dyslexia Associa-
tion (2002) and the National Institute of Health (2002)is
that dyslexia is a specific learning disability that is neuro-
logical in origin and characterized by difficulties with
accurate and/or fluent word recognition and by poor
spelling and decoding abilities. These difficulties typically
result from a deficit in the phonological component of
language that is often unexpected in relation to other
cognitive abilities and the provision of effective classroom
instruction. Secondary consequences may include prob-
lems in reading comprehension and reduced reading
experience that can impede the growth of vocabulary and
background knowledge (International Dyslexia Association
2002; National Institute of Health 2002). But in dyslexia
the problem starts with inadequate decoding whereas in
ASD the problem appears to lie in recovering the linguistic
and semantic structure and in relating the meaning of a text
to background information.
According to the two above mentioned sources and
confirmed by a wealth of research, phonological deficits are
the underlying reason for the decoding errors in dyslexic
individuals (Beaton 2004; Bishop and Snowling 2004;
Catts and Kamhi 2005; Mann 2002). Dyslexics show some
comprehension problems with both spoken and written
sentences (e.g., Mann et al. 1984; Shankweiler et al. 1984;
Smith et al. 1986; see Mann 2002, for a review) but these
are regarded as the consequences of poor working memory
for phonological information and not as a problem with
syntactic or semantic structure.
Phonological decoding is typically measured by an
individual’s performance on nonword reading tasks,
which is widely considered one of the most critical pre-
dictors of successful reading acquisition (Snowling 2000;
Ziegler and Goswami 2006). In nonword reading tasks,
readers are required to connect novel letter strings to
sequences of phonemes that are not words but could exist
in their phonological lexicon. Dyslexics show difficulty in
reading unfamiliar words and nonwords compared to
known words (Harm and Seidenberg 2000). They tend to
be less fluent readers, achieving fewer words per minute
in timed oral reading tests (Bowers 1993; Catts and
Kamhi 2005). Thus, where dyslexics are inordinately poor
486 J Autism Dev Disord (2010) 40:485–493
123

decoders, people with ASD tend to be inordinately poor
comprehenders.
The present study takes advantage of a large-scale
intervention program that seeks to ameliorate reading and
reading comprehension problems among poor readers
including those with a diagnosis of ASD and dyslexia. The
large sample available to this study, the variety of stan-
dardized tests of decoding, the inclusion of written and oral
comprehension measures, and the reporting of subgroup
data (autism, PDD-NOS, Asperger’s) were clear advanta-
ges that made the current study informative even though all
grouping was based on parental report and not clinical
assessment records. Lastly, the present study holds the
ASD findings up against findings from a comparison group
with dyslexia which was expected to show the reverse
pattern of the decoding-comprehension disassociation.
Intake data was the concern of the analysis, the outcome of
treatment was not. Extrapolating from the literature, we
hypothesized that, at intake:
1. Both oral and written comprehension tasks will show
greater impairment in the ASD groups than in the
dyslexia group supporting evidence of inordinately
poor comprehension in ASD.
2. Decoding tasks will show greater impairment in the
dyslexia group than in all ASD groups. The latter are
expected to have scores for nonword and sight word
decoding above their text reading scores.
3. The Asperger’s group should consistently outscore the
two other ASD groups in all measures, with the autism
group trailing the PDD-NOS group by a small margin
based on a slightly higher level of general cognitive
functioning in PDD-NOS, as described in the Diag-
nostic and Statistic Manual of Mental Disorders, Text-
Revision, DSM-IV-TR (American Psychiatric Associ-
ation 2000).
Method
Participants
The current study utilized 2001–2006 clinical intake data
from Lindamood-Bell Learning Processes (LBLP), a net-
work of private, nationwide learning centers specializing in
one-on-one reading and reading comprehension instruction
for individuals with learning disabilities and developmental
disorders, such as dyslexia and ASD. Two of the programs
administered at LBLP specifically address issues of
decoding and comprehension, another one stimulates
phoneme awareness to optimize decoding. The 42 LBLP
centers and additional summer sites are located in pre-
dominantly affluent parts of major U.S. cities; one learning
center is located in London, England. Data for the present
study were collected at all 42 LBLP center locations and
five summer sites.
The study data included intake test results from nine
measures of decoding and comprehension from 171 indi-
viduals with autism (26 females and 145 males with an
average age of 10.41 years), 94 individuals with Asper-
ger’s (14 females and 80 males with an average age of
11.37 years), and 119 individuals with PDD-NOS (28
females and 91 males with an average age of 10.08 years).
The comparison group of dyslexic individuals included 100
children and adolescents (45 females and 55 males with an
average age of 11.21 years). See Table 1for more demo-
graphic information by diagnostic group.
The primary diagnosis, which was reported upon intake
by the student’s parent or caretaker, was the main selection
criteria for the data analyzed in the present study. Despite
the limitation of a diagnosis based on parental report, we
did not want to dismiss the valuable subset data made
available by LBLP distinguishing between autism, PDD-
NOS, and Asperger’s. To our knowledge, no systematic
ASD reading and/or comprehension study with this kind of
subset data exists. Information on secondary diagnoses or
co-morbidities was not part of the data set and is often less
reported and underdiagnosed.
While nearly half of the general ASD population has
little or no speech, functions within the mentally retarded
range, and, consequently, does not have measurable read-
ing skills (Nation and Norbury 2005) all subjects in the
present study were verbal and had measurable reading
abilities. Another bias of the research data set lies in the
fact that LBLP clients are typically school-aged children
with at least some hope of academic progress. Therefore,
the ASD sample of this study represents a very specific
slice of the general ASD population.
Lindamood-Bell’s 2006 clinical statistics show that 45%
of LBLP students received prior speech therapy, 37%
received prior special education services, and 33% reported
prior remedial reading instruction. The data also show that
18% of clients had previously repeated a grade and 11%
Table 1 Participants by primary diagnosis and ethnicity
Ethnicity Autism Asperger’s PDD-
NOS
Dyslexia Total
African–American 2 4 1 5 12
Asian 27 10 14 3 54
Caucasian 103 73 76 68 320
Hispanic 16 1 7 9 33
Other 6 3 9 4 22
Not reported 17 3 12 11 43
Total N171 94 119 100 484
J Autism Dev Disord (2010) 40:485–493 487
123

were diagnosed as gifted. Most LBLP students reported a
prior diagnosis. Twenty-eight percent of the students who
received instruction at LBLP in 2006 had a primary diag-
nosis of dyslexia, 13% were diagnosed with ADD, 16%
with ADHD, 13% with ASD, 5% with Central Auditory
Processing Disorder, and 25% with another or no diagno-
sis. The average referral age for children with ASD was
10.7 years with 18% females and 82% males. The average
age for referrals diagnosed with dyslexia was 11.0 years
with 42% females and 58% males (LBLP 2007).
Materials
The ASD data for this study were collected between 2001
and 2006 and represent all ASD data collected by LBLP
during that time period. The investigators excluded data
sets of three participants with autism (1 female and 2 male)
due to a lack of test results. The dyslexia data were a
simple random sample of 100 participants out of a com-
plete set of 372 dyslexia data collected between 2005 and
2006. The sample was drawn by a representative of the
LBLP Research & Development Department using SAS
(Statistical Analysis Software). Participants’ names and
birth dates were omitted to protect the students’ identity.
Only de-identified data were seen and used by the inves-
tigators. Data for each participant included information on
age (years and months), grade, gender, ethnicity, pretest
date and site, diagnosis, and pre-treatment test data (raw
scores and standard scores) of the measures discussed in
this section from the years indicated above.
Before a prospective student begins instruction at LBLP,
an experienced clinician trained in test taking administers a
standard psychometric test battery to determine specific
weaknesses and strengths of decoding and comprehension.
The core test battery includes the tests analyzed in the
present study. The variety of measures therefore carries an
inevitable bias of having been selected by LBLP. While
results from nine measures were analyzed in this study,
some of the measures are not the most current versions
used in clinical practice today.
The children tested typically undergo the full test battery
in one block of about 4 h with breaks between tests, or, in
the case of younger children, in two blocks of 2 h admin-
istered on two consecutive days. Data sets for each par-
ticipant showed that, with the exception of the three
excluded subjects, most children took most of the tests.
After testing, raw test scores are calculated and verified by
at least one more LBLP employee trained in testing. Scores
are then analyzed by a software program developed by
LBLP and accessible in all LBLP locations. Should a
scoring error be detected, at least one more LBLP
employee is assigned to provide another layer of test score
verification.
What follows is a list of all measures included in this
study beginning with four decoding measures followed by
five comprehension measures
1
:
Woodcock reading mastery test—revised (WRMT-R)
word attack. Individuals are asked to read isolated
nonwords or pseudo words (Woodcock 1987).
Slosson oral reading test-revised (SORT-R). Individuals
decode from lists of isolated real words, assessing word
recognition abilities (Slosson 1990).
Gray oral reading test-revised, 4th edition (GORT-4).
The subtests for rate and accuracy were analyzed in the
present study (Wiederholt 1991).
Lindamood auditory conceptualization test (LAC-3).
This test measures an individual’s ability to perceive
and conceptualize speech sounds using a visual medium.
Subtests include sound tracking with blocks and sylla-
ble-tracking with felts from given auditory stimuli
(Lindamood and Lindamood 2004).
Peabody picture vocabulary test third edition (PPVT-
III). This test is a wide-range measure for receptive oral
vocabulary of Standard English and a screening test for
verbal ability. Individuals are asked to look at a choice
of four simple black-ink drawings per page and select
the picture that best matches an auditory stimulus (Dunn
and Dunn 1997).
Detroit tests of learning aptitude-4th edition (DTLA-4)
word opposites. Individuals have to verbally express one
word that is exactly the opposite of an auditorily
presented stimulus word. For example, ‘‘What is the
opposite of ‘day’?’’ This measure assesses verbal
expression skills and general intelligence (Hammill
1991).
Detroit tests of learning aptitude-2nd edition (DTLA-2)
oral directions. This test assesses the ability to mark
visual material after oral directions have been given. For
example, the examiner gives an oral direction such as:
‘‘Draw a line from one circle to the other circle that does
not touch the square’’ (Banas 1989). This test is no
longer available for clinical practice but LBLP has
special permission from the publisher to use this tool.
GORT-4 comprehension. Individuals read passages
aloud and answer five multiple choice comprehension
questions after completing each passage (Wiederholt
1991).
1
Three additional tests were available for a subset of participants: the
WRAT-3 for sight word reading which, at times, substitutes for the
SORT-R, the TOPS-R, and the TOPS-A, both measures of critical
thinking normed for children and adolescents/adults. The data from
these measures were insufficient in order to permit inclusion in the
analyses.
488 J Autism Dev Disord (2010) 40:485–493
123

Procedure
All data were provided as Excel files by the Lindamood-
Bell Research & Development Department in San Luis
Obispo, California, and included diagnosis, age, grade,
gender, ethnicity, center location, and test results. The
investigators analyzed the data in SPSS. Normed standard
scores were used whenever possible to analyze all data.
Z-transforms of the data were used to standardize the range
of scores, and factor analysis was used for data-reduction
procedures. Missing scores were replaced by the popula-
tion mean.
Results
To test our predictions regarding the relative asymmetry
between decoding and comprehension, data from all par-
ticipants were subjected to a factor analysis of Z-scores.
This revealed two factors that accounted for 70% of all
score variances. Varimax rotation with Kaiser Normaliza-
tion revealed the loads that appear in Table 2.
In general, the tests clustered as we had expected: Factor
1, accounting for 36% of variance, includes four of the
decoding measures at 0.8 or higher. Of the decoding
measures, only the LAC-3 test failed to load heavily on this
factor. That test loaded relatively higher on factor 2, which
accounts for 34% of the variance; its primary load was the
four comprehension measures that loaded at 0.7 or higher.
The two factor scores were then computed for individual
subjects and treated to a repeated-measures GLM with
diagnostic group as a between-subject factor. Table 3
shows each mean factor score for the four diagnostic
groups and their average.
There is a small main effect of factor score, F(1,
480) =5.70, MSE =4.06, p\.02, reflecting a trend for
factor scores on decoding to be lower than those on com-
prehension. There is also a more substantial main effect of
diagnostic group, F(3, 480) =18.09, MSE =16.36,
p\.01, reflecting a trend for participants with autism to
have the lowest factor scores, on average, followed by the
PDD-NOS group, the dyslexia group, and the Asperger’s
group, in that order.
More central to our concern was the very significant
interaction between factor and diagnostic group, F(3,
480) =66.08, MSE =47.06, p\.01. Post-hoc Tukey
HSD comparisons of the effect of diagnostic group indi-
cated that group differences between the dyslexia group and
the autism group were significant at p\.01 but that those
between autism and PDD-NOS and Asperger’s and dyslexia
were not, p[.85. However, consideration of individual
factor scores revealed that, on the decoding factor (factor 1),
the three ASD groups scored at or above the population
mean whereas the dyslexia group scored below the mean.
On the comprehension factor (factor 2), the autism and the
PDD-NOS groups scored below the population mean, and
the Asperger’s and the dyslexia groups scored above. The
disassociation between decoding and comprehension skills
was therefore most evident between the two ‘‘extreme’’
diagnostic groups, autism and dyslexia. The PDD-NOS
group resembles the autism group and the Asperger’s group
falls in between, with decoding scores like the autism group
but comprehension scores closer to the dyslexia group.
In an additional GLM with repeated measures of the two
factor scores we controlled for age, gender, and center
location as co-variants and found that neither gender nor
center location had any effects between subjects, p[.05.
While age showed an effect on factor scores,
F(3,477) =9.94, p\.01, there was no interaction with the
diagnosis. A MANCOVA of standard scores was used to
examine the effects of age across all measures. The anal-
ysis confirmed that older children tended to do worse than
younger ones: age had effects in the PPVT-III, DTLA-2
Oral Directions, and the GORT-4 Comprehension as well
as in the WRMT-R Word Attack, the SORT-R, and the
GORT-4 Rate, p\.01.
Table 2 Factor analysis of standard scores (Z-transforms)
Test Factor 1 Factor 2
Comprehension
PPVT-III .09 .87
DTLA-4 word opposites .30 .76
DTLA-2 oral directions .05 .86
GORT-4 comprehension .25 .74
Decoding
WRMT-R word attack .84 .14
SORT-R .90 .21
GORT-4 rate .83 .20
GORT-4 accuracy .87 .22
LAC-3 .33 .50
Factor 1 and factor 2 account for 70% of total scores. Z-scores were
derived from standard scores across all diagnostic groups. Rotation:
Varimax with Kaiser Normalization
Table 3 Factor scores: means and SD as a function of diagnosis
Diagnosis NDecoding Comprehension Average
MSD MSD MSD
Autism 171 .13 .07 -.54 .06 -.21 .05
PDD-NOS 119 -.01 .09 -.28 .08 -.14 .06
Asperger’s 94 .28 .10 .35 .09 .31 .07
Dyslexia 100 -.47 .10 .93 .08 .23 .07
All subjects 484 -.02 .05 .12 .04
J Autism Dev Disord (2010) 40:485–493 489
123

Table 4gives a summary of mean standard scores
achieved by each diagnostic group on each measure.
As predicted, all decoding measures showed lower
scores among the dyslexia group as compared to the ASD
groups, with the exception of the LAC-3 test, and com-
prehension measures show the opposite trend of higher
comprehension scores for participants with dyslexia than
participants with ASD.
Discussion
The present study examined decoding and comprehension
in individuals with ASD with the goal of confirming pre-
vious studies using a bigger sample with a greater variety
of measures and presenting rare subset data for autism,
PDD-NOS, and Asperger’s. The ASD groups’ performance
on nine standardized psychometric tests was further com-
pared to the performance of a dyslexia comparison group.
Given the number of tests available for analysis, factor
analysis was used to reduce the data.
As predicted, participants with ASD achieved lower
scores on all comprehension measures compared to par-
ticipants with ASD whereas the dyslexia comparison group
showed lower scores on all decoding measures than the
ASD groups. The expected asymmetrical disassociation
between decoding and comprehension became especially
apparent when the autism and the PDD-NOS were com-
pared to dyslexia. The exception to the asymmetry between
the ASD groups and the dyslexia group occurred on the
LAC-3, a test of phoneme awareness that behaved partly
like a comprehension test and partly like a decoding test.
The results from the ASD groups support findings of
comprehension skills that lie below decoding skills in high-
functioning autism (Minshew et al. 1994; Mirenda and
Erickson 2000; Nation et al. 2006; O’Connor and Klein
2004; O’Connor and Hermelin 1994). The results from the
dyslexia group are also consistent with a definition of
dyslexia as decoding that is unexpectedly below the child’s
other cognitive abilities (International Dyslexia Associa-
tion 2002; National Institute of Health 2002). They further
show that the reading skills associated with PDD-NOS
closely resemble autism whereas Asperger’s associates
with a relatively high level of reading skills across both
decoding and comprehension.
Gender and clinic location had no effect on our results
regarding the disassociation between decoding and com-
prehension and its relation to diagnostic category. Age co-
varied with some measures. Interestingly, the Asperger’s
group was the only group that showed an improvement
with increased age while the other groups fell further
behind the population norm. Previous studies reported that
children with Asperger’s have higher verbal and oral
Table 4 Means and SDs of all measures of decoding and compre-
hension
Test/diagnosis NM SD
WRMT-R word attack
a
Autism 164 95.98 16.82
PDD-NOS 110 93.42 16.23
Asperger’s 92 99.70 15.49
Dyslexia 99 91.75 11.38
SORT-R
a
Autism 167 90.31 21.55
PDD-NOS 112 88.72 21.14
Asperger’s 92 95.37 18.75
Dyslexia 98 84.97 16.60
GORT-4 rate
b
Autism 145 6.85 3.46
PDD-NOS 83 6.60 3.26
Asperger’s 78 8.09 3.85
Dyslexia 96 6.11 2.76
GORT-4 accuracy
b
Autism 145 6.67 3.50
PDD-NOS 83 6.64 3.41
Asperger’s 78 8.36 4.11
Dyslexia 98 6.45 3.02
LAC-3
a
Autism 69 80.94 18.63
PDD-NOS 43 83.09 17.56
Asperger’s 44 91.64 18.79
Dyslexia 97 90.26 11.13
PPVT-III
a
Autism 170 76.83 17.87
PDD-NOS 118 82.62 15.15
Asperger’s 92 95.15 19.38
Dyslexia 100 101.67 13.68
DTLA-4 word opposites
b
Autism 159 5.28 3.39
PDD-NOS 111 6.09 3.78
Asperger’s 89 8.30 3.77
Dyslexia 94 8.28 3.07
DTLA-2 oral directions
b
Autism 156 3.85 3.01
PDD-NOS 110 4.00 2.81
Asperger’s 90 6.32 3.29
Dyslexia 99 8.19 2.89
GORT-4 comprehension
b
Autism 145 4.48 3.05
PDD-NOS 84 5.21 3.01
Asperger’s 78 7.10 3.63
Dyslexia 96 8.37 3.12
95% confidence interval
a
Mean =100, SD =15
b
Mean =10, SD =3
490 J Autism Dev Disord (2010) 40:485–493
123

language skills than children with high-functioning autism
(Iwanga et al. 2000; Klin et al. 1995; Ozonoff et al. 1991,
2000) which is commensurate with the strong overall
performance by the Asperger’s group in our study.
The relatively high achievement of all ASD groups in
isolated word reading (WRMT-R Word Attack and SORT-
R) is possibly the single most remarkable finding of this
study. In the general population, nonword reading is the
best predictor for reading success (Snowling 2000; Ziegler
and Goswami 2006) among children seeking intervention
for reading problems. Apparently, the comprehension
problems in children with ASD stem from difficulties
above and beyond the ability to recover the phonological
structures transcribed by the English alphabet. In this
regard, our finding of relatively intact decoding in Asper-
ger’s and autism differs from Nation et al. (2006) who
showed that 46% of their 41 ASD participants were at least
one SD below norm and 22% were at least two SD below
norm in decoding. The present study included a much
larger sample but did not include non-readers like those
who had participated in Nation’s study.
Against predictions, results from the phonological
awareness measure (LAC-3) loaded on both the decoding
and comprehension factors, and not particularly well on
either. The LAC-3 placed the dyslexia group second after
the Asperger’s group and showed the autism group to be
clearly at the bottom. Since the LAC-3 was the sole pho-
nological measure in this study, a variety of explanations
might filter into the interpretation of the results. The dys-
lexia group could have received and benefited from prior
intervention in phonological awareness at school, a likely
scenario considering their diagnosis and the fact that LBLP
is often pursued after years of public education have failed.
But we suspect a more likely reason is that the participants
with autism and PDD-NOS found the LAC-3 difficult
because it places demands on the ability to follow arbitrary
oral instructions. Oral commands within the LAC-3, such
as asking participants to track nonword sounds and sylla-
bles with color-coded blocks and felts, may challenge these
groups with their known problems with oral instructions
(Goldstein et al. 1994) and receptive language compre-
hension (Mirenda and Erickson 2000). Nation (1999)
considers the LAC an excellent predictor of first-grade
reading skills, and that does appear to be the case for the
general population. However, three hyperlexic children
performed well below age-expected levels on the LAC in a
study conducted by Sparks (1995). For the present study,
which involves ASD children with known oral instruction
deficits, we are inclined to consider the LAC-3 as a con-
founded test that measured both oral comprehension and
phonological awareness.
The primary limitation of this study was the very nature
of archival research which confines the parameters of the
study to a given set of data. The positive aspect of this type
of research is that large sample sizes tend to show clearer
patterns in subpopulations. However, limited by the exist-
ing LBLP dataset, we had to work with a self-reported
diagnosis by parents which did not include secondary
diagnoses or co-morbidities. Therefore, the subset data has
to be regarded as a secondary analysis that possibly hints at
trends but cannot be conclusive. Self-reports of diagnosis
have been used in previous ASD research. Reichenberg
et al. (2006) used reports from Israeli draft-board assess-
ments in a population-based cohort study to determine a
significant association between advancing paternal age and
risk of ASD. Reichenberg et al. used draft board assess-
ment data from 132,271 17-year old Israeli-born Jewish
persons. The Israeli draft board assigns a diagnosis based
on reviews of records from various sources, including
government agencies, medical professionals, and parental
interviews rather than-face-to-face assessments.
Despite the lack of a formal medical diagnosis, we
obtained results that were significant and consistent with
prior research that had used clinical assessment. Our con-
fidence in the accuracy of the parental reporting is further
confirmed by the fact that eligibility for specialized health
services, including treatment at LBLP, or for any other
form of federal support, including tax credits, depends on a
proper diagnosis from a health specialist. With a typical
recommendation of 6–10 weeks of 20 h per week of one-
on-one intervention, treatment at LBLP comes at a high
price and parents almost always seek partial or full reim-
bursement from their school district. Proper diagnosis prior
to treatment also plays a role when filing for educational
loans, an option parents often choose to cover LBLP
treatment expenses, since school district reimbursements
are often delayed and never guaranteed. As the parents’
ability to meet these financial requirements depends upon
having a clinical diagnosis prior to treatment, it would
seem that children seen at LBLP would have received a
clinical assessment that confirmed their diagnosis at some
point in their history.
We also caution that the Lindamood-Bell clientele and
database consist of a specific slice of the population: ASD
and dyslexic children with problems in reading and reading
comprehension but with some hope for academic
improvement and with access to remedial treatment. Due to
the limitations of the data stored in the LBLP archive, we
could not directly compare the ASD group to a typically
developing control group, nor did we have a choice in the
measures used to gather the data. Working with standard
scores was our way to compare the data to population
norms. Clearly, more research examining the disassocia-
tion between decoding and comprehension in ASD is
needed to understand the origin of this asymmetry and to
develop more effective treatment.
J Autism Dev Disord (2010) 40:485–493 491
123

Acknowledgments The authors acknowledge Erica Meyer and
Andrea Richards, thesis reviewers, at Antioch University Los Ange-
les, Nancy Bell, Dave Hungerford, and Robert Tifler at Lindamood-
Bell Learning Processes in San Luis Obispo, California, USA, and
Bill Meis.
Open Access This article is distributed under the terms of the
Creative Commons Attribution Noncommercial License which per-
mits any noncommercial use, distribution, and reproduction in any
medium, provided the original author(s) and source are credited.
References
American Psychiatric Association. (2000). Diagnostic and statistical
manual of mental disorder (text revision). Washington, DC:
Author.
Banas, N. (1989). Detroit tests of learning aptitude (2nd ed.). Novato,
CA: Academic Therapy Publications.
Beaton, A. (2004). Dyslexia, reading and the brain. New York:
Psychology Press.
Bishop, D., & Snowling, M. (2004). Developmental dyslexia and
specific language impairment: Same or different? Psychological
Bulletin, 130, 858–886.
Bowers, P. (1993). Text reading and rereading: Predictors of fluency
beyond word recognition. Journal of Reading Behavior, 25,
133–153.
Bryson, S. (1983). Interference effects in autistic children: Evidence
for the comprehension of single stimuli. Journal of Abnormal
Psychology, 92, 250–254.
Catts, H., & Kamhi, A. (2005). Language and reading disabilities
(2nd ed.). Needham Heights, MA: Allyn & Bacon.
Dunn, L., & Dunn, L. (1997). The peabody picture vocabulary test
(3rd ed.). Circle Pines, MN: American Guidance Service.
Eskes, G., Bryson, S., & McCormick, T. (1990). Comprehension of
concrete and abstract words in autistic children. Journal of
Autism and Developmental Disorders, 20(1), 61–73.
Frith, U. (2003). Autism (2nd ed.). Oxford, UK: Blackwell.
Frith, U., & Snowling, M. (1983). Reading for meaning and reading
for sound in autistic and dyslexic children. British Journal of
Developmental Psychology, 1, 329–342.
Goldstein, G., Minshew, N., & Siegel, D. (1994). Age differences in
academic achievement in high-functioning autistic individuals.
Journal of Clinical and Experimental Neuropsychology, 16,
671–680.
Griswold, D., Barnhill, G., Myles, B., Hagiwara, D., & Simpson, R.
(2002). Asperger syndrome and academic achievement. Focus
on Autism and Other Developmental Disabilities, 17, 94–102.
Hammill, D. (1991). Detroit Tests of Learning Aptitude (4th ed.).
Austin, TX: PRO-ED.
Harm, M., & Seidenberg, M. (2000). Are there orthographic
impairments in phonological dyslexia? Cognitive Neuropsychol-
ogy, 18, 71–92.
International Dyslexia Association (2002). Revised definition from the
International Dyslexia Association. Retrieved electronically on
August 12, 2009 from http://www.dys-add.com/define.html#
revised.
Iwanga, R., Kawasaki, C., & Tsuchida, R. (2000). Comparison of
sensory-motor and cognitive function between autism and
Asperger syndrome in preschool children. Journal of Autism
and Developmental Disorders, 30, 16–174.
Kanner, L. (1943). Autistic disturbances of affective contact. The
Nervous Child, 2, 217–250.
Katzir, T., Yongsuk, K., Wolf, M., O’Brien, B., Kennedy, B., Lovett,
M., et al. (2006). Reading fluency: The whole is more than the
parts. Annals of Dyslexia, 53(1), 51–82.
Klin, A., Volkmar, F., Sparrow, S., Cicchetti, D., & Rourke, B.
(1995). Validity and neuropsychological characterization of
Asperger syndrome: Convergence with nonverbal learning
disabilities syndrome. Journal of Child Psychology and Psychi-
atry, 36, 1127–1140.
Lindamood, C., & Lindamood, P. (2004). Lindamood auditory
conceptualization test. Austin, TX: PRO-ED.
Lindamood-Bell Learning Processes (2007) 2006 Learning Centers’
Results. Retrieved September 18, 2007, from http://www.linda
moodbell.com/downloads/pdf/research/clinical%20stats%202005.
pdf.
Mann, V. (2002). Reading disorders, developmental. In V. Rama-
chandran (Ed.), The encyclopedia of the human brain (pp. 141–
154). San Diego, CA: Academic Press.
Mann, V., Shankweiler, D., & Smith, S. (1984). The association
between comprehension of spoken sentences and early reading
ability: The role of phonetic representation. Journal of Child
Language, 11, 627–643.
Minshew, N., Goldstein, G., Taylor, H., & Siegel, D. (1994).
Academic achievement in high functioning autistic individuals.
Journal of Clinical and Experimental Neuropsychology, 16(2),
261–270.
Mirenda, P. (2003). ‘‘He’s not really a reader…’’: Perspectives on
supporting literacy development in individuals with autism.
Topics in Language Disorders, 23(4), 271–282.
Mirenda, P., & Erickson, K. (2000). Augmentative communication
and literacy. In A. M. Wetherby & B. M. Prizant (Eds.), Autism
spectrum disorders: A transactional developmental perspective.
Baltimore, MD: Paul H. Brooks.
Nation, K. (1999). Reading skills in hyperlexia: A developmental
perspective. Psychological Bulletin, 125(3), 338–355.
Nation, K., Clarke, P., Wright, B., & Williams, C. (2006). Patterns of
reading ability in children with autism spectrum disorder.
Journal of Autism and Developmental Disorders, 36(7), 911–
919.
Nation, K., & Norbury, C. (2005). Why reading comprehension fails.
Topics in Language Disorders, 25(1), 21–32.
Nation, K., & Snowling, M. (1997). Assessing reading difficulties:
The validity and utility of current measures of reading skill.
British Journal of Educational Psychology, 67, 359–370.
National Institute of Health (2002). Dyslexia definition. Retrieved
electronically on August 12, 2009 from http://www2.ednet10.
net/dyslexia/Dyslexia-Definition.html.
O’Connor, N., & Hermelin, B. (1994). Two autistic savant readers.
Journal of Autism and Developmental Disorders, 24(4), 501–515.
O’Connor, I., & Klein, P. (2004). Explorations of strategies for
facilitating the reading comprehension of high-functioning
students with autism spectrum disorders. Journal of Autism
and Developmental Disorders, Vol., 34(2), 115–127.
Ozonoff, S., Rogers, S., & Pennington, B. (1991). Asperger’s
syndrome: Evidence of an empirical distinction from high
functioning autism. Journal of Child Psychology and Psychiatry,
32, 1107–1122.
Ozonoff, S., South, M., & Miller, J. (2000). DSM-IV defined
Asperger syndrome: Cognitive behavioral and early history
differentiation from high-functioning autism. Autism: Interna-
tional Journal of Research and Practice, 4, 29–46.
Reichenberg, A., Gross, R., Weiser, M., Bresnahan, M., Silverman, J.,
Harlap, S., et al. (2006). Advancing paternal age and autism.
Archives of General Psychiatry, 63, 1026–1032.
Ricks, D., & Wing, L. (1975). Language, communication, and the use
of symbols in normal and autistic children. Journal of Autism
and Childhood Schizophrenia, 5, 191–221.
492 J Autism Dev Disord (2010) 40:485–493
123

Shankweiler, D., Smith, S., & Mann, V. (1984). Repetition and
comprehension of spoken sentences by reading disabled chil-
dren. Brain and Language, 23, 241–257.
Slosson, R. (1990). Slosson oral reading test-revised. East Aurora,
NY: Slosson Educational Publishing.
Smith, S., Mann, V., & Shankweiler, D. (1986). Spoken sentence
comprehension by good and poor readers: A study with the token
test. Cortex, 22, 627–632.
Snowling, M. (2000). Dyslexia. Oxford, UK: Blackwell.
Sparks, R. (1995). Phonemic awareness in hyperlexic children.
Reading and Writing, 7, 217–235.
Tager-Flusberg, H. (1981). On the nature of linguistic functioning in
early infantile autism. Journal of Autism and Developmental
Disorders, 11, 45–56.
Tager-Flusberg, H., Calkins, S., Nolin, T., Baumberger, T., Anderson,
M., & Chadwick-Denis, A. (1990). A longitudinal study of
language acquisition in autistic and Down syndrome children.
Journal of Autism and Developmental Disorders, 14, 281–289.
Tager-Flusberg, H., & Joseph, R. (2003). Identifying neurocognitive
phenotypes in autism. Philosophical Transactions of the Royal
Society of London Series B-Biological Sciences, 358(1430), 303–
314.
Wahlberg, T., & Magliano, J. (2004). The ability of high function
individuals with autism to comprehend written discourse.
Discourse Processes, 38, 119–144.
Wiederholt, J. (1991). Gray oral reading tests—diagnostic. Austin,
TX: PRO-ED.
Woodcock, H. (1987). Woodcock reading mastery tests—revised.
Circle Pines, MN: American Guidance Service.
Ziegler, J., & Goswami, U. (2006). Reading acquisition, develop-
mental dyslexia, and skilled reading across languages: A
psycholinguistic grain size theory. Psychological Bulletin,
131(1), 3–29.
J Autism Dev Disord (2010) 40:485–493 493
123