Journal of Child Psychology and Psychiatry 43:5 (2002), pp 635–645
Young adult academic outcomes in a longitudinal
sample of early identified language impaired
and control children
Arlene R. Young, Joseph H. Beitchman, Carla Johnson, Lori Douglas,
Leslie Atkinson, Michael Escobar, and Beth Wilson
The Centre for Addiction and Mental Health, Toronto, Canada
Background: The long-term academic consequences of childhood language impairment are both
theoretically and clinically important. An unbiased appraisal of these outcomes, however, requires
carefully designed, longitudinal research. Method: A group of children first identified as having speech
and/or language impairment in a community-based, longitudinal study at 5 years of age and matched
controls were re-examined during young adulthood (age 19). A comprehensive battery of speech and
language, cognitive and achievement tests, psychiatric interviews, and questionnaires were completed
by subjects, their parents and teachers. Results: While children with early speech problems showed
only a few academic differences from controls in young adulthood, early language impaired (LI) young
adults lagged significantly behind controls in all areas of academic achievement, even after controlling
for intelligence. Further, rates of learning disabilities (LD) were significantly higher in the LI group than
both the controls and community base rates. Concurrent individual difference variables, including
phonological awareness, naming speed for digits, non-verbal IQ, verbal working memory, and executive
function, all contributed unique variance to achievement in specific areas. Conclusion: Early LI rather
than speech impairment is clearly associated with continued academic difficulties into adulthood.
These results speak to the need for intensive, early intervention for LI youngsters. Keywords:
Adulthood, educational attainment, language disorder, learning difficulties, longitudinal studies,
outcome.
A history of early language impairment (LI) has been
consistently shown to negatively affect academic
performance later in life (Bashir & Scavuzzo, 1992;
Beitchman, Wilson, Brownlie, Walters, & Lancee,
1996a). Early studies of outcome among clinically
referred speech and language disordered children,
for example, report poorer academic performance
than that of the general population (e.g., Hall &
Tomblin, 1978; King, Jones, & Lasky, 1982; Aram &
Nation, 1980). Prospective studies which do not rely
on clinical samples confirm that children with
speech and language impairment at a young age
have lower levels of academic achievement throughout childhood (e.g., Beitchman et al., 1996a; Catts,
1993; Rissman, Curtiss, & Tallal, 1990; Scarborough & Dobrich, 1990; Walker, Greenwood, Hart, &
Carta, 1994) and into adolescence (e.g., Aram, Ekelman, & Nation, 1984). The association between
early language limitations and later academic problems is further strengthened when combined with
socioeconomic factors, such as family SES, which
contribute to the stability of performance on both
language and academic measures over time (Walker
et al., 1994).
In contrast to the well-established literature on
academics and language competence in childhood,
comparatively little is known about the academic
outcome of children with a history of LI once they
have entered adulthood. In part, this lack of information reflects the methodological limitations of
retrospective or clinically based studies in which the
early language development of subjects selected for
current academic difficulties is examined (e.g., Badian, Duffy, Als, & McAnulty, 1991). Other studies
employ a prospective design but do not report on
outcome beyond early elementary school (e.g., Fazio,
1996; Silva, Williams, & McGee, 1987) or follow
samples of only very severely impaired individuals
(Mawhood, Howlin, & Rutter, 2000) or do not include
an appropriate control group (Aram et al., 1984).
Despite these limitations, a portrait of children with
a history of speech and language impairment during
late adolescence or early adulthood has emerged and
warrants review.
Much of our knowledge about the association between language deficits and academic achievement
focuses on reading and spelling problems. In particular, deficits in phonological awareness, a specific
aspect of language development, are widely accepted
as playing a major, likely causal, role in reading
problems (e.g., Liberman & Shankweiler, 1985; Siegel, 1988; Stanovich 1988a, b; Wagner & Torgesen,
1987). Longitudinal studies using batteries of linguistic measures at an early age and predicting later
reading performance (e.g., Jorm, Share, Maclean, &
Matthews, 1986; Mann, 1986; Badian, McNulty,
Duffy, & Als, 1990; Badian et al., 1991) point primarily to phonological awareness and rapid naming
tasks as particularly good predictors of reading
outcome. Yet, as pointed out by Bashir & Scavuzzo
Association for Child Psychology and Psychiatry, 2002.
Published by Blackwell Publishers, 108 Cowley Road, Oxford OX4 1JF, UK and 350 Main Street, Malden, MA 02148, USA
636
Arlene Young et al.
(1992), children with language impairments appear
to encounter an array of reading problems not necessarily explained by phonological skill deficits
alone. For example, limitations in vocabulary
knowledge and ability to comprehend syntactic
structures associated with language disorders are
also likely to contribute to reading deficits (Bashir &
Scavuzzo, 1992). Further, reciprocal relationships
demonstrated between language and reading skills
(Share & Silva, 1987; Stanovich, 1986) speak to the
importance of examining both reading performance
and language development over an extended period
of time. A long-term, longitudinal study of language
impaired children offers such a unique opportunity.
In addition to literacy problems, individuals with a
history of LI may also encounter difficulties with
mathematics achievement. These problems are not
unexpected given that many aspects of mathematics,
such as mastery of domain-specific vocabulary,
number reading and writing, and counting, are linguistic activities (Lyytinen, Ahonen, & Rasanen,
1994) and children who have poor arithmetic
achievement also tend to be poor readers (e.g.,
Share, Moffitt, & Silva, 1988). Difficulties with vocabulary used in mathematics (McLeod & Armstrong, 1982) and the relatively high reading level
used in mathematics problems (Woodward & Peters,
1983) are particularly apparent at the secondary and
post-secondary level. Studies of learning disabled
(LD) children (many of whom are also language impaired) reveal particular inability to solve simple
arithmetic problems with both speed and accuracy
(Torgesen, 1988; Connor, 1983). Problems with
number fact retrieval, a likely contributor to the
speed and accuracy difficulties, have also been
identified in elementary aged LD children (Connor,
1983; Kulak, 1993; Russell & Ginsburg, 1984).
Studies of mathematical abilities in language impaired children (Fazio, 1994, 1996) reveal particular
difficulties in the automatisation of basic units of
information, such as number facts. These studies
typically focus on mathematical abilities at the preschool and elementary level, however, thus it is unclear whether problems in automatisation continue
and contribute to mathematical difficulties in older
individuals.
While lower-level processing skills (such as phonological awareness) can continue to play a role in
academic performance during adulthood (e.g., Bell &
Perfetti, 1994; Pennington, Van Orden, Smith,
Green, & Haith, 1990), acquisition of secondary- and
post-secondary-level academic skills also requires
higher-level cognitive skills. Working memory, for
example, is an essential contributor to activities
such as reading comprehension and mental arithmetic. Executive skills, including planning, flexibility, and self-monitoring, become increasingly
important as academic demands increase. Denckla
(1993) proposed that executive function may be a
key factor in the ability for learning disabled (and one
might also assume LI) adults to compensate for
deficits in lower-level processing skills (such as linguistic deficits). Thus, individuals with a history of LI
but at least adequate self-monitoring and planning
skills may be better able to make use of remedial
instruction or therapy offered to address their processing difficulties. Deficits in these higher-order
skills may also speak to the relationship between
language competence and other areas that influence
academic success, including social functioning and
the ability to self-regulate behaviour.
Although various studies outlined above provide
evidence for enduring academic difficulties amongst
early identified LI children, no single study provides
information on outcome in adulthood that can be
generalised to the general population. Without such
information, the association between variables that
may differentially predict outcome in different academic areas cannot be addressed. The current study
is unique in its examination of these issues. This
study examines the impact of early identified LI on
academic outcome during young adulthood. We report on the third wave of a 14-year, communitybased, longitudinal prospective study of speech
and/or language impaired children and matched
controls identified at 5 years of age and followed
until the age of 19 years. Further, the contributions
of individual difference characteristics known to
predict academic progress generally and deemed
specifically relevant to outcome in LI youngsters are
reported. These variables include: intellectual ability, phonological awareness ability, and rapid
naming as a measure of capacity to automatically
process and retrieve small units of information critical to the development of skills in both reading and
mathematics. Finally, we report on the role of higherlevel processes such as working memory and executive skills in academic outcome among young
adults with a history of language impairment.
Method
Initial (1982) study – subjects and design
and first follow-up study
A one-in-three random sample of all English-speaking
5-year-old Kindergarten children in the Ottawa-Carleton region of Ontario, Canada (N ¼ 1655; 794 girls
and 861 boys) participated in the initial screening
procedure. Screening measures included: The Bankson
Language Screening Test (BLST; Bankson, 1977), The
Screening Test for Auditory Comprehension of Language (STACL; Carrow, 1973), The Photo Articulation
Test (PAT; Pendergast, Dickey, Selmar, & Soder, 1969)
and examiner ratings of voice and fluency performance
during testing. Children performing below specific cut
points on the original speech and language screening
measures participated in comprehensive speech and
language assessment. Measures included: the Test of
Language Development (TOLD; Newcomer & Hamill,
1977), the Peabody Picture Vocabulary Test-Revised
Young adult academic outcomes
(PPVT-R; Dunn & Dunn, 1981), and the Goldman–
Fristoe–Woodcock auditory Memory Tests (GFW; Goldman, Fristoe, & Woodcock, 1974). In all, 142 children
identified as speech and/or language impaired (SLI)
agreed to participate in the longitudinal study. Children
who passed the original screening (N ¼ 1340) were
eligible to participate as control subjects. For each
subject identified as SLI, a same-sexed member of their
classroom who passed the original speech and language
screening was assigned to the control group (N ¼ 142
controls). Both control and SLI groups had 90 boys and
52 girls. Detailed information was obtained through
direct assessment, teacher, parent, child self-report,
and psychiatric interview regarding the children’s developmental and health history, intelligence, behavioural adjustment, and parent/family characteristics
(Beitchman, Nair, Clegg, Ferguson, & Patel, 1986a;
Beitchman, Nair, Clegg, & Patel, 1986b).
Subjects from this first study were recontacted in
1989–90 (when they were 12 to 13 years old) for a first
follow-up study of their functioning across domains,
including speech and language, academic, cognitive,
behavioural, and psychiatric outcomes. Results of this
follow-up study are summarised in Beitchman et al.
(1996a, b).
Current study – subjects in second follow-up
In 1995–97 a second follow-up study was initiated in
which 264 (93%) of the original study participants (now
aged 18–19 years old) were successfully re-contacted
and 258 (90.8%) agreed to participate. For a detailed
description of the second follow-up longitudinal study
procedures see Beitchman et al. (1999) and Johnson
et al. (1999). Complete academic and cognitive outcome
measures were obtained for most of these subjects (N ¼
229; 80.6%) represented in the following groups based
on their initial (age 5) study performances: speech
impaired only group (n ¼ 35), language impaired only
(n ¼ 49), speech and language impaired (n ¼ 25), and
control (n ¼ 120).
While attrition was generally small, 55 original sample subjects did not complete the academic portion of
the current follow-up study. Examination of original
study performance differences between participants
and non-participants at follow-up reveals that nonparticipants had a significantly lower mean score on
one of the language screening tests (BLST), and lower
age 5 intelligence test scores.
Measures and procedures
A comprehensive battery of speech and language
measures, cognitive and achievement tests, psychiatric interviews, and questionnaires were completed
by subjects, tested individually for a 6-hour session.
Participants also completed a structured, diagnostic
interview (the Composite International Diagnostic Interview; Kessler et al., 1994) and provided background,
developmental,
family,
and
medical
histories. Both parents and teachers were asked to
complete questionnaires describing the participants’
behaviour and relevant background information. Par-
637
ticipants were paid $75 at the completion of the
testing session.
Intellectual ability was assessed using a seven-subtest short form of the Wechsler Adult Intelligence Scale
– Revised (WAIS-R) (Wechsler, 1981), described by Ward
(1990). This short form yields prorated Verbal, Performance, and Full Scale IQ scores and includes the
Information, Digit Span, Arithmetic, and Similarities
subtests of the Verbal scale and the Picture Completion,
Block Design, and Digit Symbol subtests of the Performance scale. Excellent concurrent validity and IQ
scores that are as reliable as the complete WAIS-R make
this short form an appropriate substitution for the
complete WAIS-R (Schretlen, Benedict, & Bobholz,
1994).
Academic achievement was assessed using the
Woodcock Johnson Psycho-Educational BatteryRevised, Part II (WJ-R; Woodcock & Johnson, 1989):
Letter-Word Identification, Passage Comprehension,
Calculation, and Word Attack subtests; and the Wide
Range Achievement Test – Third Edition (WRAT-3;
Wilkinson, 1993) Spelling subtest.
Executive function was assessed using the Wisconsin
Card Sorting Test (WCST; Heaton, 1981), computer
version. This instrument, which is the most widely
accepted measure of executive function in adults, has
been shown to be sensitive to group differences in a
number of populations purported to have frontal lobe
dysfunction, including individuals who have suffered
frontal head injury, schizophrenics, and children with
Attention Deficit Disorder. Subjects were asked to
match each of 128 response cards to an array of four
key cards differing in colour, shape, and number which
remained at the top of the computer screen. Feedback
regarding accuracy of a response was provided after
each card was placed. After 10 correct responses, the
sorting criterion shifts to another feature of the key
cards without directly informing the subject. Thus, they
must shift to another sorting principle based on accuracy feedback alone. The WCST yields a variety of
scores, including total errors, perseverative errors (i.e.,
repetition of an incorrect sorting principle), nonperseverative errors, categories completed, failure to maintain
set, and percent conceptual level responses.
Working memory refers to the temporary holding of
information while it is processed or manipulated in
some way. We assessed working memory using the
digits backwards portion of the Digit Span subtest from
the WAIS-R.
Phonological awareness was assessed using the Pig
Latin test (Pennington et al., 1990) in which subjects
were asked to produce Pig Latin versions (i.e., drop the
initial phoneme and add it to the end of a word followed
by the sound of a long letter ‘a’) for 48 target words. This
test measures phonological awareness and manipulation abilities in adults.
Finally, the Rapid Automatised Naming test for digits
(RAN; Denckla & Rudel, 1976) was used to measure
naming speed. The speed in naming simple, familiar
stimuli, such as digits, letters, and objects, has been
shown to contribute both shared and unique variance
to the prediction of reading skill. It has been convincingly argued that naming speed reflects the ability to
automatise retrieval of the identity associated with the
stimuli, such as the names of the letters or numbers
638
Arlene Young et al.
(e.g., Bowers, 1995; Bowers & Wolf, 1993). The RAN
digits task consists of 5 digits repeated 10 times in
random order displayed in continuous format on a
chart (i.e., 10 digits per line for 5 lines) and subjects
were instructed to read the digits as quickly as possible
without making mistakes. Two trials of naming were
given and the naming time in seconds was measured
using a stopwatch. The mean time to name the series
was the score used in analyses.
Criteria for learning disability
The current report examines academic outcome both as
a continuous variable and in terms of the prevalence of
learning disability among early language impaired and
control subjects. For this purpose, learning disability
was determined to be present if achievement in a
particular academic area was below the 25th percentile
according to the test norms. This definition of LD is in
keeping with compelling arguments that inclusion of IQ
information and reliance on a discrepancy between IQ
and achievement to define LD is neither clinically nor
empirically warranted (e.g., Shaywitz, Fletcher, & Shaywitz, 1996; Siegel, 1988, 1990). Nevertheless, in order
to allow for comparison between the present results and
those reported in other studies which frequently use an
IQ cut-off score, we examined the prevalence of LD
when an IQ cut-off was employed. Under this definition,
an individual was classified as LD if achievement was
below the 25th percentile and IQ was within the broad
Average range (Verbal or Performance IQ was greater
than or equal to 80). A participant was classified as
reading disabled (RD) if the above criteria were met
using either the Basic Reading composite score (combining the Word Attack and Word Identification subtests) or the Broad Reading composite score (combining
the Word Identification and the Comprehension subtests). Similarly, they were classified as arithmetic or
spelling disabled (AD and SD, respectively) if the
Calculation or Spelling test performance was below
the 25th percentile.
Statistical analyses
Prior to any analyses, the distributions of all variables
were checked for normalcy and outliers. Problems with
skew were resolved by cube transformation for WRAT-R
Spelling scores, log transformations for RAN time
scores, and rank transformations for Pig Latin, and
the WCST measures.
First, we present descriptive statistics of the study
participants, followed by intercorrelations among the
variables included in this report. A multivariate analysis of variance design (MANOVA) was used and
significant results were followed up with univariate
tests. In each case, we compared participants at age 19
grouped according to the four original (i.e., age 5)
language groups. Given that the emphasis of this report
is on the impact of early language rather than speech
impairment, the comparison between the original controls and speech-only participants versus both original
language impaired groups is of particular interest. To
assess effects of early language impairment independent of general intellectual ability, Performance IQ was
removed as a covariate. Finally, a series of hierarchical
multiple regression analyses are reported that examined the relationship between individual difference
characteristics (e.g., phonological awareness, naming
speed) and academic outcome for each of the language
groups.
Results
Table 1 presents demographic characteristics of the
subjects grouped according to their age 5 speech and
language function. Univariate tests revealed no differences between the speech and control group on
any demographic variable, nor did the two language
disorder groups differ. The combined speech and
control groups did differ from the language impaired
groups on SES; t(225) ¼ 4.57, p < .0005, Performance IQ; t(225) ¼ 6.32, p < .0005, and Verbal IQ;
t(225) ¼ 9.49, p < .0005. The proportion of males
compared to females did not differ across groups.
Given that the speech-only and control groups did
not differ on these demographic variables, these
groups were combined and are referred to subsequently as non-LI. Similarly, the two language impaired groups were also combined and are referred
to simply as LI. This combination of groups allows for
a more direct examination of the effects of early
language impairment on later academic functioning.
Preliminary analyses revealed no significant differences between the speech-only and control groups
on all outcome variables except for one reading
variable. Similarly, the two language groups did not
differ in outcome. In the interest of clarity, only results for the recombined groups will be reported,
Table 1 Characteristics of subjects grouped according to age 5 speech and language status
Control (n ¼ 120)
Age in years
SESa
Verbal IQ
Performance IQ
Full Scale IQ
Male (%)
a
Speech only (n ¼ 35)
Lang. only (n ¼ 49)
Speech + Lang. (n ¼ 25)
M
SD
M
SD
M
SD
M
SD
18.8
53.4
103.9
109.7
107.3
64.2
(.34)
(13.6)
(12.2)
(16.1)
(13.4)
18.9
52.5
100.2
105.8
103.0
74.3
(.36)
(16.7)
(10.3)
(15.1)
(11.8)
19.0
44.1
86.2
93.9
88.6
65.3
(.44)
(13.4)
(11.1)
(16.2)
(12.3)
19.0
41.8
84.3
90.8
86.1
44.0
(.47)
(12.6)
(9.1)
(12.4)
(9.5)
Based on Blishen, Carroll, & Moore (1987) rating.
Young adult academic outcomes
with the exception of the one variable on which the
original speech-only and control groups differed.
Correlations among variables
All achievement and individual difference variables
were highly and significantly correlated with each
other. All correlations were positive, with the exception of the negative correlations between RAN and
WCST total errors and other variables. In both cases,
this reflects the nature of the variables in that good
performance results in a lower score (e.g., less
naming time needed, fewer errors) while a poorer performance is reflected in a higher score. Correlations
among the academic achievement measures were all
above .50 in magnitude, except for the relation between Calculation and Word Attack (r ¼ .46). We
tested whether any correlations differed significantly
by language group and found a few differences. In
particular, the correlations between reading and
spelling variables were significantly stronger among
participants with LI compared with non-LI participants. For example, real word (Word Identification)
and nonword decoding (Word Attack) for LI participants was .68 compared to non-LI (.49); correlations
for LI were stronger than non-LI for reading comprehension with Word Identification (.69 versus .42)
and WRAT spelling (.69 versus .49).
Correlations among individual difference variables
known to predict reading were moderate and ranged
from ).33 (RAN – Pig Latin) to ).36 (RAN – PIQ). The
correlations of these variables and the executive
function measure (WCST, Total Errors) were low to
moderate in magnitude, ranging from ).16 (WCST –
Digits Backward) to ).38 (WCST – PIQ).
Finally, relations between the individual difference
variables and the five achievement measures were
examined. Most correlations were moderate. For example, RAN and Pig Latin were correlated with Calculation ().43, .46), Word Identification ().43, .56),
Passage Comprehension ().34, .45), Word Attack
()41, .50), and WRAT-3 Spelling ().49, .55). RAN and
639
word decoding were well correlated among LI participants ().64 for nonword and ).55 for real words),
but significantly less correlated among non-LI participants ().17, ).30). Digits Backwards score correlated most strongly with Spelling (.47) and Word
Identification (.42). WCST total errors correlated
most strongly with Calculation and Passage Comprehension ().47, ).44) and least with Word Attack
().25).
Group comparisons
Group differences were analysed with a series of
multivariate analyses of variance (MANOVAs). This
multivariate procedure was chosen because of the
interdependence of the measures and to protect
against Type I error. Significant MANOVAs were followed up with univariate tests. Homogeneity of
variance assumptions was tested using Cochran’s C,
Bartlett-Box F, and Box’s M tests. In each case, we
first compared participants in the four original language groupings using three planned multivariate
contrasts: 1. Controls versus SI only, 2. Languageonly versus speech & language, and 3. LI versus
non-LI. If the multivariate contrast was significant,
univariate analyses of variance (ANOVAs) were conducted for each dependent measure. To assess language effects independent of general intellectual
ability, PIQ was removed as a covariate and the three
contrasts were recomputed using MANCOVA procedures. Groups were then collapsed and the final
comparisons to be reported are between LI participants versus non-LI participants.
Do early language groups differ in academic
achievement at age 19?
Mean scores and standard deviations for both the
academic achievement and the individual difference
variables are presented in Table 2. Given that data
transformations did not change significance levels in
Table 2 Means and standard deviations on academic and individual difference variables at age 19: subjects grouped according to
age 5 language functioning
Variables
Non-lang. impaired
Sign.a
Language impaired
Achievement
Spelling
Reading comp.
Word identification
Word attack
Calculation
105.5
110.0
111.8
109.9
107.2
(10.7)
(15.0)
(16.3)
(13.9)
(16.5)
92.5
89.7
93.6
99.5
87.7
(15.2)
(13.9)
(13.8)
(16.3)
(14.8)
P
p
p
p
p
Individual difference
RAN mean time
Pig Latin
Digits backward
WCST total errors
18.2
38.5
7.0
23.8
(3.6)
(9.7)
(2.0)
(17.5)
19.9
33.0
5.6
36.0
(4.8)
(10.1)
(1.7)
(21.3)
p < .05
p < .0005
p < .0005
p < .0005
a
b
Significance of univariate test without controlling for PIQ.
Significance of univariate test after controlling for PIQ.
<
<
<
<
<
.0005
.0005
.0005
.0005
.0005
Sign.b
p < .0005
p < .0005
p < .0005
p < .005
p < .0005
ns
p < .05
p < .01
p < .05
640
Arlene Young et al.
the analyses, the data are reported in terms of
untransformed means and standard deviations.
The multivariate LI versus non-LI contrast showed
significance, F(5, 221) ¼ 19.55, p < .0005, as did
the control versus SI-only contrast, F(5, 221) ¼
2.36, p < .05. Examination of the univariate F tests
indicated that LI participants performed significantly
lower than non-LI participants on all five academic
outcome variables (all ps < .0005). One significant
univariate difference drove the multivariate finding
for the control versus SI-only contrast. Specifically,
SI-only participants scored significantly lower than
controls on real word decoding (Word Identification;
p < .01). This is the only significant difference between the speech-only and control groups on any
outcome measure. The univariate effects remained
significant after controlling for PIQ.
Prevalence of learning disabilities
The proportion of subjects meeting criteria for
learning disabilities in both the early LI and non-LI
groups is presented in Table 3. When no IQ cut-off is
employed, 36.8% of the LI sample met the criterion
for a Reading Disability, 40.3% for a Spelling Disability, and 53.9% for an Arithmetic Disability. When
an IQ cut-off of 80 was employed, these figures
dropped to 26.7% for Reading Disability, 29.3% for
Spelling Disability, and 42.7% for Arithmetic Disability. The proportion of individuals diagnosed with
learning disorders within the non-LI group was
significantly smaller both with and without the IQ
cut-off criterion. In particular, when IQ was not
included, 6.4% met criteria for disabilities in reading, 7.1% for spelling, and 12.2% for arithmetic. In
contrast to the LI group, the inclusion of the IQ cutoff resulted in only modest changes, with rates of
5.8% for reading, 7.1% for spelling, and 11.5% for
arithmetic. Chi-square tests revealed consistently
higher prevalence of LD among the LI group for
reading disability (RD), spelling disability (SD), and
arithmetic disability (AD), (all ps < .0005). The inclusion of an IQ cut-off did not change significance
levels. For example, when the IQ cut-off was employed, 21.3% of early LI participants had both RD
and SD at age 19 while only 4.5% of the non-LI
participants evidenced both of these disorders.
Similarly, 20.0% of LI versus 2.6% of non-LI participants met criteria for both literacy-based (RD and
SD) and arithmetic disorders. Finally, Table 3 lists
the relative risk or risk ratio (RR) of LD in the LI
group when compared to the non-LI group. The RR
analysis poses the question: Relative to a control
child, how many times more likely is a child in the LI
group to have a learning disability at age 19? For
example, after controlling for IQ, an LI child is 4.6
times more likely to be LD in reading than a control
child. While risk for LD was elevated in all areas,
early LI participants were particularly more likely
than control subjects to have learning disabilities
across academic domains. When not controlling for
IQ, for example, they were 11.7 times more likely to
have LD in all areas and 10.7 times more likely to
have combined math and reading LDs at age 19.
Thus, having a history of early LI substantially
increases the risk of later developing LD in all
academic areas.
Early language groups and individual difference
variables
Descriptive statistics for the individual difference
variables are presented in Table 2. The combined LI
and non-LI groups were compared on all four individual difference variables and the overall MANOVA
effect was significant, F(4, 210) ¼ 11.33, p < .0005.
Table 3 Prevalence of learning disabilities at age 19 by age 5 LI and non-LI groups
Non-lang. impaired
(n ¼ 155–156)
Chi square
(v2)b
Relative risk
(95% CI)
(36.8%)
(40.3%)
(53.9%)
(31.6%)
(34.2%)
(31.6%)
(29.9%)
10 (6.4%)
11 (7.1%)
19 (12.2%)
7 (4.5%)
5 (3.2%)
7 (4.5%)
4 (2.6%)
34.55
38.16
46.50
32.15
42.44
32.15
37.52
5.8 (2.9–11.2)
5.7 (3.0–10.7)
4.4 (2.8–7.1)
7.0 (3.2–15.5)
10.7 (4.3–26.7)
7.0 (3.2–15.5)
11.7 (4.2–32.5)
LD ¼ achievement < 25th percentile and VIQ or PIQ ‡ 80
Reading LD
20 (26.7%)
Spelling LD
22 (29.3%)
Math LD
32 (42.7%)
Reading + Spelling LD
16 (21.3%)
Reading + Math LD
18 (20.0%)
Spelling + Math LD
15 (20.0%)
LDs in all areas
15 (20.0%)
9 (5.8%)
11 (7.1%)
18 (11.5%)
7 (4.5%)
4 (2.6%)
7 (4.5%)
4 (2.6%)
20.15
20.34
28.94
15.88
27.01
14.01
20.40
4.6 (2.2–9.7)
4.1 (2.1–8.1)
3.7 (2.2–6.1)
4.7 (2.0–11.0)
9.4 (3.3–26.7)
4.4 (1.9–10.4)
7.8 (2.6–22.1)
Language impaired
(n ¼ 75–77)a
LD ¼ achievement < 25th percentile
Reading LD
Spelling LD
Math LD
Reading + Spelling LD
Reading + Math LD
Spelling + Math LD
LDs in all areas
a
b
28
31
41
24
26
24
23
The number of subjects varied for some analyses due to missing data.
All v2 ratios were significant at p < .0005.
Young adult academic outcomes
Follow-up univariate tests indicated that the non-LI
participants performed significantly better than LI
participants on RAN (p < .05), Pig Latin, Digits
Backward, and WCST total errors (all p < .0005).
Controlling for PIQ, univariate tests were significant
for Pig Latin (p < .05), Digits Backward (p < .01), and
WCST total errors (p < .05) but not for RAN (p ¼ .64).
Contributions of individual difference variables
to outcome
A series of hierarchical multiple regression analyses
were applied to the outcome data as an extension of
the MANOVA results presented thus far. These
analyses pose the question: What contribution do
individual difference variables make to academic
outcome in various domains? This question concerns the role of four individual difference variables,
namely phonological awareness (measured by Pig
Latin), naming speed (RAN), verbal working memory
(Digits Backward), and executive function (WCST),
on outcome in various measures of achievement,
including reading, spelling, and mathematics.
Recall, however, that these variables are examined
concurrently and are not part of the predictive analyses described earlier. Thus, they do not speak to
causation. Nevertheless, they do provide additional
information on relative contributors to current academic performance and are, therefore, of interest.
In each analysis Performance IQ was entered first
followed by phonological awareness and naming
speed. In each case, the contribution of each of these
variables over PIQ and over each other are reported.
Finally, the contributions of the remaining variables
(verbal working memory and executive function) are
reported at the last step in the analyses. These variables are included last to determine if they contribute to academic outcome over and above the
other individual difference variables. Results of these
analyses, completed separately for each of the early
language groups, are summarised in Table 4.
641
As predicted, PIQ made a significant contribution
to academic performance when entered at the first
step of the regression analyses across all achievement variables. Phonological awareness made substantial, unique contributions (over both PIQ and
naming speed) to all reading measures (word identification, word attack, and reading comprehension)
for both LI and non-LI groups. Thus, phonological
awareness appears to contribute to literacy skills in
young adulthood, regardless of initial language status. Naming speed also contributed unique variance
(over and above both PIQ and phonological awareness) to word identification in both groups but it did
not make a unique contribution to reading comprehension performance for either group. The relationship between naming speed and word attack varied
as a function of language group with a significant
unique contribution in only the LI group.
Phonological awareness also made a significant
unique contribution to spelling achievement in both
language groups but naming speed contributed
unique variance only within the non-LI group.
Finally, both phonological awareness and naming
speed contributed unique variance to mathematical
calculation performance for the non-LI group but
neither variable contributed to calculation scores
among the LI-only group.
In keeping with our interest in examining the
possible contributions of higher-order skills to academic outcome, measures of working memory or
executive function were entered at the final step of
the analyses. Recall that this set of analyses
addresses the possible role of these higher-level
skills to compensate for more basic deficits. Verbal
working memory (as measured by Digits Backwards)
contributed unique variance to word attack in all
groups and to spelling outcome in the non-LI group
but it did not contribute unique variance for any of
the other academic areas. Executive function (as
measured by WCST results) contributed uniquely to
calculation scores in both groups and to reading
Table 4 Summary of hierarchical regression analyses for prediction of academic outcomes
R2 change
Measure
Preceding steps
Word attack
Word ID
PIQ
Pig Latin
RAN
WCST
Digit backwards
entered first
PIQ+RAN
PIQ+PIG
PIQ+PIG+RAN
PIQ+PIG+RAN
.103*
.081**
.181***
.000
.031*
.197***
.101**
.067*
.014
.000
PIQ
Pig Latin
RAN
WCST
Digit backwards
entered first
PIQ+RAN
PIQ+PIG
PIQ+PIG+RAN
PIQ+PIG+RAN
.031*
.132***
.003
.007
.024*
.111***
.102***
.027*
.000
.009
Note. PIG ¼ Pig Latin; sig. of chg: ***p < .0005; **p < .005; *p < .05.
Reading comp.
Language impaired
.154**
.081*
.014
.033
.016
Non-language impaired
.173***
.054**
.006
.052**
.004
Spelling
Calc.
.122**
.136***
.029
.001
.008
.159**
.034
.019
.057*
.092**
.104***
.128***
.084***
.011
.047**
.300***
.053***
.042**
.044**
.002
642
Arlene Young et al.
comprehension in the non-LI group. Neither working
memory nor executive function made unique contributions to word identification.
Discussion
This study is the first comprehensive, longitudinal
study of children with language impairment identified at age 5 followed into young adulthood. As
such, it offers a unique opportunity to extend current research findings by examining the interplay
between early language delays and academic outcome across development. Examination of these
results indicates that while speech and control
groups were generally quite similar by age 19, clear
and pervasive differences were evident between
language impaired and non-impaired groups. Thus,
children first identified as LI at 5 years of age continued to lag behind their age 19, non-LI peers in
intellectual ability and all domains of academic
achievement. Further, differences in academic outcome were not due to intellectual ability differences
alone as the LI group performed significantly below
the non-LI group even after controlling for nonverbal intellectual ability (PIQ). The definition of LD
in the current study does not require a significant
discrepancy between IQ and achievement standard
scores. As noted earlier, the decision not to employ
an IQ–achievement discrepancy is in response to
recent critiques of the practice appearing in the
research literature (e.g., Moats & Lyon, 1993; Siegel, 1988, 1989; Stanovich, 1991). Those critiquing
the discrepancy definition in the area of reading
disability note, for example, that IQ typically accounts for only 10–25% of the variance in reading
achievement, even among adults (e.g., for a review
see Spear-Swerling & Sternberg, 1996). This is a far
cry from the assumption of perfect prediction inherent in the discrepancy definition. Further,
problems in reading can generalise over time to affect many of the cognitive skills, such as vocabulary
knowledge, that are measured in IQ tests (Stanovich, 1986, 1991), thereby reducing the discrepancy
between these scores. While it is beyond the scope
of the current study to resolve this issue, the more
lenient definition of LD used in the current study
will capture individuals with low academic
achievement that would not be defined as LD using
a discrepancy definition.
The academic difficulties of early LI individuals
identified in this study are similar to those reported
elsewhere in the literature (e.g., Aram et al., 1984;
Catts, 1991, 1993; Silva et al., 1987) for younger age
groups but we extend these findings, demonstrating
that differences persist into adulthood. Further, the
magnitude of the difference between groups is far
from trivial given that early LI subjects were approximately five times as likely to have academic
difficulties severe enough to be classified as learning
disabilities (as defined in the current study) as their
non-LI peers.
Many studies point to phonological awareness as a
necessary, though not always sufficient, language
sub-skill for developing adequate reading and spelling skills (e.g., Liberman & Shankweiler, 1985;
Snowling, Goulandris, & Defty, 1996). Studies of
reading disabled adults (e.g., Bruck, 1992; Elbro,
Nielsen, & Petersen, 1994; Pennington et al., 1990;
Pratt & Brady, 1988) demonstrate that phonological
awareness deficits persist well into adulthood and
account for much of the difference between good and
poor readers even at older ages. The importance of
phonological awareness to literacy skills in adulthood was further supported in the current study
with the finding that phonological awareness contributed unique variance (i.e., over and above nonverbal IQ and naming speed) to spelling and all of the
reading variables for both the LI and non-LI groups.
These findings are extended, however, by the performance in mathematics as well as literacy skills
and the inclusion of other individual difference variables. For example, naming speed was shown to
make a unique contribution over phonological
awareness and non-verbal IQ to word identification
for all groups, pseudo word reading for the LI group
and to spelling for the non-LI group. The lack of
unique contribution for some variables when the LI
or non-LI groups were analysed separately likely reflects the smaller magnitude of naming speed’s
contribution which does not always reach significance when examining effects in smaller groups.
Nevertheless, naming speed is clearly an important
component of single word reading and spelling in
adulthood. In contrast, naming speed does not contribute unique variance to reading comprehension.
Instead, executive skills (as measured by performance on the WCST) made a significant unique
contribution to text comprehension over and above
non-verbal IQ, phonological awareness, and naming
speed. Taken together, these findings indicate that
phonological awareness continues as a robust
predictor of reading skill in adulthood, naming speed
is more specifically relevant to the sub-skills
involved in single word reading and the combination
of higher-order skills subsumed under executive
function contribute to an adult’s ability to organise
and comprehend text. This later finding is particularly notable because the measure of executive
function was a non-verbal task, and thus, less
obviously related to reading comprehension than the
verbal measures. Given that executive function
involves control processes, self-monitoring, and goal
directed planning, the contribution to reading comprehension may well reflect the metalingustic or
metacognitive skills involved in comprehension
monitoring, and active, critical engagement with
textual information.
It is interesting that the LI group’s lowest mean
achievement score and highest rate of LD was in
Young adult academic outcomes
mathematics rather than reading or spelling. In fact,
the rates of LD for both groups were highest for
mathematics disability. These rates clearly exceed
those reported for younger age groups (e.g., Lewis,
Hitch, & Walker, 1994). The unusually high frequency of mathematics disability in this sample may
reflect the age of the current sample in that those
young adults who encounter difficulty in mathematics are more likely not to take mathematics
courses during high school and, thus, fall further
behind their peers at age 19. In contrast, reading and
spelling skills are typically not taught beyond the
early elementary level. Thus, all subjects are likely to
have received similar instruction in these areas
but likely varied considerably in their exposure to
mathematics instruction at the secondary level.
In mathematics performance, only executive skills
contributed unique variance over PIQ for both
groups. While verbal working memory also contributed some unique variance in the LI group, both
phonological awareness and naming speed contributed uniquely in the non-LI group. Thus, as with
reading comprehension, the planning, self-monitoring, and goal-orientation inherent in the construct of
executive function appears particularly important to
mathematical achievement. Working memory, as
measured by Digits Backwards, also contributes
unique variance to achievement in word attack,
spelling, and, within the LI group only, mathematics.
The lack of independent contribution of working
memory to reading comprehension is contrary to
other findings in the literature (e.g., Daneman &
Carpenter, 1980). Measurement issues may account
for some differences between studies (i.e., those
showing a large working memory effect typically do
not use Digits Backwards). Conversely, sample differences may be relevant in that the current study
examines young adults with and without LI whereas
most reading studies examine differences between
younger aged, specific RD and non-RD groups.
These sample differences may also account for more
limited findings than have been demonstrated elsewhere for the unique contributions of naming speed
to reading variables. In any case, the role of both
executive function and working memory to academic
outcome, once more basic processing variables have
been accounted for, lends support to Denckla’s
(1993) contention that such skills play an important
role in an individual’s ability to compensate in
adulthood for early cognitive difficulties. Further
study of these and similar higher-order cognitive
abilities may do much to further our understanding
of the long-term outcome of early impairments in
language as well as other developmental disorders.
A final issue to be addressed is the possible impact
of differential attrition on the generalisability of these
findings. Recall that while attrition was generally low
(19.4%), non-participants had lower initial language
scores and lower IQ scores than subjects who participated in the current study. Thus, these results may
643
be a somewhat optimistic estimate of academic outcome. In particular, the non-participating SLI subjects may have had the poorest academic outcomes
and this is not represented in the current results. The
effect of this omission is unlikely to have had a major
impact on the findings, however, given the relatively
small size of the non-participating group and the
wide range of performance, including some very low
scores, within the participating SLI subjects.
Acknowledgement
This study was made possible through the support of
Health Canada, grant 6606-5639-102.
Correspondence to
Arlene R. Young, Department of Psychology, Simon
Fraser University, 8888 University Drive, Burnaby,
British Columbia, V5A 1S6; Tel: (604) 291-5329;
Email: [email protected]
References
Aram, D.M., Ekelman, B.L., & Nation, J.E. (1984).
Preschoolers with language disorders: 10 years later.
Journal of Speech and Hearing Research, 27, 232–
244.
Aram, D.M., & Nation, J.E. (1980). Preschool language
disorders and subsequent language and academic
difficulties. Journal of Communication Disorders, 13,
159–170.
Badian, N.A., McAnulty, G.B., Duffy, F.H., & Als, H.
(1990). Prediction of dyslexia in kindergarten boys.
Annals of Dyslexia, 40, 152–169.
Badian, N.A., Duffy, F.H., Als, H., & McAnulty, G.B.
(1991). Linguistic profiles of dyslexic and good readers. Annals of Dyslexia, 41, 221–245.
Bankson, N.W. (1977). Bankson Language Screening
Test. Baltimore, MD: University Park Press.
Bashir, A.S., & Scavuzzo, A. (1992). Children with
language disorders: Natural history and academic
success. Journal of Learning Disabilities, 25, 53–65.
Beitchman, J.H., Douglas, L., Wilson, B., Johnson, C.,
Young, A., Atkinson, L., & Escobar, M. (1999).
Adolescent substance use disorders: Findings from
a 14-year follow-up of speech and language impaired
and control children. Journal of Clinical Child Psychology, 28, 312–321.
Beitchman, J.H., Nair, R., Clegg, M., Ferguson, B., &
Patel, P.G. (1986a). Prevalence of psychiatric disorders in children with speech and language disorders.
Journal of the American Academy of Child Psychiatry,
25, 528–535.
Beitchman, J.H., Nair, R., Clegg, M., & Patel, P.G.
(1986b). Prevalence of speech and language disorders
in 5-year-old kindergarten children in the OttawaCarleton region. Journal of Speech and Hearing
Disorders, 51, 98–110.
Beitchman J.H., Wilson, B., Brownlie, E.B., Walters, H.,
& Lancee, W. (1996a). Long-term consistency in
speech/language profiles: I. Developmental and
644
Arlene Young et al.
academic outcomes. Journal of the American Academy of Child and Adolescent Psychiatry, 35, 804–824.
Beitchman, J.H., Wilson, B., Brownlie, E. B., Walters,
H., Inglis, A., & Lancee, W. (1996b). Long-term
consistency in speech/language profiles: II. Behavioral, emotional, and social outcomes. Journal of the
American Academy of Child and Adolescent Psychiatry, 35, 815–825.
Bell, L.C., & Perfetti, C.A. (1994). Reading skill: Some
adult comparisons. Journal of Educational Psychology, 86, 244–255.
Blishen, B.R., Carroll, W.K., & Moore, C. (1987). The
1981 Socioeconomic Index for Occupations in Canada. Canadian Review of Sociology and Anthropology,
24, 465–488.
Bowers, P.G. (1995). Tracing symbol naming speed’s
unique contributions to reading disabilities over time.
Reading and Writing: An Interdisciplinary Journal, 7,
189–216.
Bowers, P.G., & Wolf, M. (1993). Theoretical links
between naming speed, precise timing mechanisms
and orthographic skill in dyslexia. Reading and
Writing: An Interdisciplinary Journal, 5, 69–85.
Bruck, M. (1992). Persistence of dyslexics’ phonological
awareness deficits. Developmental Psychology, 28,
874–886.
Carrow, E. (1973). Screening Test for Auditory Comprehension of Language (5th edn). Boston, MA: Teaching
Resources Corp.
Catts, H.W. (1991). Early identification of Dyslexia:
Evidence from a follow-up study of speech-language
impaired children. Annals of Dyslexia, 41, 163–177.
Catts, H.W. (1993). The relationship between speechlanguage impairments and reading disabilities. Journal of Speech and Hearing Research, 36, 948–958.
Connor, F.P. (1983). Improving school instruction for
learning disabled children: The Teachers College
Institute. Exceptional Education Quarterly, 4, 23–44.
Daneman, M., & Carpenter, P.A. (1980). Individual
differences in working memory and reading. Journal
of Verbal Learning and Verbal Behavior, 19, 450–466.
Denckla, M.B. (1993). The child with developmental
disabilities grown up: Adult residua of childhood
disorders. Neurologic Clinics, 11, 105–125.
Denckla, M., & Rudel, R.G. (1976). Rapid ‘automatized’
naming (RAN): Dyslexia differentiated from other
learning disabilities, Neuropsychologia, 14, 471–479.
Dunn, L.M., & Dunn, L.M. (1981). Peabody Picture
Vocabulary Test – Revised Manual. Circle Pines, MN:
American Guidance Service.
Elbro, C., Nielsen, I., & Petersen, D.K. (1994). Dyslexia
in adults: Evidence for deficits in non-word reading
and in the phonological representation of lexical
items. Annals of Dyslexia, 44, 205–226.
Fazio, B.B. (1994). The counting abilities of children
with specific language impairment: A comparison of
oral and gestural tasks. Journal of Speech and
Hearing Research, 37, 358–368.
Fazio, B.B. (1996). Mathematical abilities of children
with specific language impairment: A 2-year followup. Journal of Speech and Hearing Research, 39,
839–849.
Goldman, R., Fristoe, M., & Woodcock, R.W. (1974).
Goldman Fristoe-Woodcock Auditory Memory Tests.
Circle Pines, MN: American Guidance Services, Inc.
Hall, P., & Tomblin, B. (1978). A follow-up study of
children with articulation and language disorders.
Journal of Speech and Hearing Disorders, 43,
227–241.
Heaton, R.H. (1981). Wisconsin Card Sorting Test
Manual. Odessa, FL: Psychological Assessment
Resources.
Johnson, C.J., Beitchman, J.H., Young, A.R., Escobar, M., Atkinson, L., Wilson, B., Brownlie, E.B.,
Douglas, L., Taback, N., Lam, I., & Wang, M.
(1999). Fourteen-year follow-up of children with
and
without
speech/language
impairments:
Speech/language stability and outcomes. Journal
of Speech, Language, Hearing Research, 42,
744–760.
Jorm, A.R., Share, D.L., Maclean, R., & Matthews, R.
(1986). Cognitive factors at school entry predictive of
specific reading retardation and general reading
backwardness: A research note. Journal of Child
Psychology and Psychiatry, 21, 45– 54.
Kessler, R.C., McGonagle, K.A., Zhae, S., Nelson,
C.B., Hughes, M., Eshleman, S., Wittchen, H.U., &
Kendler, K.S. (1994). Lifetime and 12-month
prevalence of DSM-III-R psychiatric disorders in
the United States. Archives of General Psychiatry,
51, 8–19.
King, R.R., Jones, C., & Lasky, E. (1982). In retrospect:
A fifteen-year follow-up report of speech-languagedisordered children. Language, Speech, and Hearing
Services in Schools, 13, 24–32.
Kulak, A.G. (1993). Parallels between math and reading
disability: Common issues and approaches. Journal
of Learning Disabilities, 26, 666–673.
Lewis, C., Hitch, G.J., & Walker, P. (1994). The prevalence of specific arithmetic difficulties and specific
reading difficulties in 9- to 10-year-old boys and girls.
Journal of Child Psychology and Psychiatry, 35, 283–
292.
Liberman, I.Y., & Shankweiler, (1985). Phonology and
the problems of learning to read and write. Remedial
and Special Education, 6, 8–17.
Lyytinen, H., Ahonen, T., & Rasanen, P. (1994).
Dyslexia and dyscalculia in children: Risks, early
precursors, bottlenecks and cognitive mechanisms.
Acta Paedopsychiatrica, 56, 179–192.
Mann, V.A. (1986). Why some children encounter
reading problems: The contribution of difficulties
with language processing and linguistic sophistication to early reading disability. In J.D. Torgesen &
B.Y. Wong (Eds.), Psychological and educational perspectives on learning disabilities (pp. 133–159). New
York, NY: Academic Press.
Mawhood, L., Howlin, P., & Rutter, M. (2000). Autism
and developmental receptive language disorder – a
comparative follow-up in early adult life. I: Cognitive
and language outcomes. Journal of Child Psychiatry
and Psychology, 41, 547–559.
McLeod, T., & Armstrong, S. (1982). Learning disabilities in mathematics-skill deficits and remedial
approaches at the intermediate and secondary
grades. Learning Disabilities Quarterly, 5, 305–311.
Moats, L.C., & Lyon, G.R. (1993). Learning disabilities
in the United States: Advocacy, science, and the
future of the field. Journal of Learning Disabilities, 26,
127–133.
Young adult academic outcomes
Newcomer, P.L., & Hamill, D.D. (1977). The Test of
Language Development (TOLD). Austin, TX: Empiric
Press.
Pennington, B.G., Van Orden, G., Smith, S., Green, P.,
& Haith, M. (1990). Phonological processing skills
and deficits in adult dyslexics. Child Development, 61,
1753–1778.
Pendergast, K., Dickey, S.E., Selmar, J.W., & Soder,
A.L. (1969). Photo Articulation Test. Danville, IL:
Interstate.
Pratt, A.C., & Brady, S. (1988). Relation of phonological
awareness to reading disability in children and adults.
Journal of Educational Psychology, 80, 319–323.
Rissman, M., Curtiss, S., & Tallal, P. (1990). School
placement outcomes of young language impaired
children. Journal of Speech Language Pathology and
Audiology, 14, 49–58.
Russell, R.L., & Ginsburg, H.P. (1984). Cognitive analysis of children’s mathematics difficulties. Cognition
and Instruction, 1, 217–244.
Sattler, J. (1988). Assessment of children (3rd edn).
San Diego: Jerome M. Sattler.
Scarborough, H.S., & Dobrich, W. (1990). Development
of children with early language delay. Journal of
Speech and Hearing Research, 33, 70–83.
Schretlen, D., Benedict, H.B., & Bobholz, J.H. (1994).
Composite reliability and standard errors of measurement for a seven-subtest short form of the Wechsler Adult Intelligence Scale-Revised. Psychological
Assessment, 6, 188–190.
Share, D.L., Moffitt, T.E., & Silva, P.A. (1988). Factors
associated with arithmetic-and-reading disability and
specific arithmetic disability. Journal of Learning
Disabilities, 21, 313–320.
Share, D.L., & Silva, P.A. (1987). Language deficits and
specific reading retardation: Cause or effect? British
Journal of Disorders of Communication, 22, 219–226.
Shaywitz, S.E., Fletcher, J.M., & Shaywitz, B.A. (1996).
A conceptual model and definition of dyslexia: Findings emerging from the Connecticut Longitudinal
Study. In J.L. Beitchman, N.J. Cohen, M.M. Konstantareas, & R. Tannock (Eds.), Language, learning
and behavior disorders: Developmental, biological,
and clinical perspectives (pp. 199–223). New York,
NY: Cambridge University Press.
Siegel, L.S. (1988). Evidence that IQ scores are irrelevant to the definition and analysis of reading difficulties. Canadian Journal of Psychology, 42, 201–215.
Siegel, L.S. (1989). IQ is irrelevant to the definition of
learning disabilities. Journal of Learning Disabilities,
22, 469–478.
Siegel, L.S. (1990). An evaluation of the discrepancy
definition of dyslexia. Journal of Learning Disabilities,
25, 618–629.
645
Silva, P.A., Williams, S., & McGee, R. (1987). A longitudinal study of children with developmental language delay at age three: Later intelligence, reading
and behaviour problems. Developmental Medicine
and Child Neurology, 29, 630–640.
Snowling, M.J., Goulandris, N., & Defty, N. (1996). A
longitudinal study of reading development in dyslexic
children. Journal of Educational Psychology, 88, 653–
669.
Spear-Swerling, L., & Sternberg, R.J. (1996). Off Track:
When poor readers become ‘learning disabled’. Boulder, CO: Westview Press.
Stanovich, K.E. (1986). Matthew effects in reading:
Some consequences of individual differences in the
acquisition of literacy. Reading Research Quarterly,
21, 360–406.
Stanovich, K.E. (1988a). Explaining the differences
between the dyslexic and the garden-variety poor
reader: The phonological-core variable-difference
model. Journal of Learning Disabilities, 21, 590–604.
Stanovich, K.E. (1988b). The right and wrong places to
look for the cognitive locus of reading disability.
Annals of Dyslexia, 38, 154–177.
Stanovich, K. (1991). Conceptual and empirical problems with discrepancy definitions of reading disability. Learning Disabilities Quarterly, 14, Special Issue,
269–280.
Torgesen, J.D. (1988). Studies of children with learning
disabilities who perform poorly on memory span
tasks. Journal of Learning Disabilities, 21, 605–612.
Wagner, R.K., & Torgesen, J.K. 1987. The nature of
phonological processing and its causal role in the
acquisition of reading skills. Psychological Bulletin,
101, 192–212.
Walker, D., Greenwood, C., Hart, B., & Carta, J. (1994).
Prediction of school outcomes based on early language production and socioeconomic factors. Child
Development, 65, 606–621.
Ward, L.C. (1990). Prediction of verbal, performance,
and Full Scale IQs from seven subtests of the WAIS-R.
Journal of Clinical Psychology, 46, 436–440.
Wechsler, D. (1981). Manual for the Wechsler Adult
Intelligence Scale – Revised. New York, NY: The
Psychological Corporation.
Wilkinson, G. (1993). Wide Range Achievement Test-3.
Wilmington, DE: Jastak.
Woodcock, R.W., & Johnson, W.B. (1989). Manual for
the Woodcock-Johnson Psycho-Educational BatteryRevised (WJ-R). TX: DLM.
Woodward, D.W., & Peters, D.J. (1983). The learning
disabled adolescent. Rockville, NY: Aspen Publications.
Manuscript accepted 18 December 2001