Published for the British Institute of Learning Disabilities
Journal of Applied Research in Intellectual Disabilities 2012, 25, 396–413
Individual and Environmental Characteristics
Associated with Cognitive Development in
Down Syndrome: A Longitudinal Study
Donna Couzens*, Michele Haynes and Monica Cuskelly*
*School of Education, Faculty of Social and Behavioural Sciences, The University of Queensland, Brisbane, Queensland, Australia;
Institute for Social Science Research, The University of Queensland, Brisbane, Queensland, Australia
Accepted for publication
15 October 2011
Background Associations among cognitive development
and intrapersonal and environmental characteristics were
investigated for 89 longitudinal study participants with
Down syndrome to understand developmental patterns
associated with cognitive strengths and weaknesses.
Materials and Methods Subtest scores of the StanfordBinet IV collected between ages 4–30 years were analysed in multilevel models of age-related change. Predictor
variables were systematically entered into the models
to identify associations with development for each
subtest.
Results Temperament, maternal education, medical conditions and school experiences were associated with
cognitive differences. Additional associations with rate
of development were detected for negative mood,
Introduction
This article considers intrapersonal and environmental
characteristics associated with cognitive development
and their contribution to variation in development
between individuals with Down syndrome. Although
cross-sectional research has identified a behavioural
phenotype associated with Down syndrome, it is not
clear how individual and environmental characteristics
combine to influence age-related change for the different
abilities that underpin phenotypic patterns. Longitudinal
studies are necessary to understand long-term influences
on change over time and can assist the development of
targeted interventions by informing the type and timing
of services to maximize long-term benefit (Fidler et al.
2009). This study draws on data of trajectories of change
over time with respect to the core subtests of the
2012 Blackwell Publishing Ltd
persistence, maternal education level and elementary
school experience for several subtests.
Conclusions Early cognitive advantage and consistent
opportunities to learn academic content appear to facilitate cognitive development, although this latter was confounded with ability and maternal education in this
study. Data presented endorse research into interventions that enhance verbal and problem solving environments through-out early and middle childhood and
target reductions in negative affect in relation to
supporting cognitive development for individuals with
Down syndrome.
Keywords: cognitive development, developmental trajectories, Down syndrome, phenotype
Stanford-Binet Fourth edition (Thorndike et al. 1986) for
individuals with Down syndrome reported in Couzens
et al. 2011).
Characteristics that have been shown to influence
cognitive development were identified from both general population and Down syndrome specific research.
In addition to characteristics considered malleable
through intervention, gender and year of birth were
included to control for any associations with performance. Female advantage for verbal abilities have been
identified within the general population (Martins &
Castro-Caldas 2005; Neubauer et al. 2005) and Down
syndrome population (Carr 1988; Crombie 1994),
whereas male advantage on spatial and visual tasks
were identified with general populations only (Jensen
1998; Martins & Castro-Caldas 2005; Neubauer et al.
2005). One exception to these findings was a Down
10.1111/j.1468-3148.2011.00673.x
Journal of Applied Research in Intellectual Disabilities 397
syndrome specific study by Kittler et al. (2004) where a
female advantage at ages 30–50 years for visuo-constructive ability, psychomotor speed, associative memory and
perceptual comparison was identified but not for verbal
tasks. This last finding suggests that age may interact
with gender to influence cognitive strengths and weaknesses for individuals with Down syndrome.
Individual characteristics central to the present study
included behaviour style dimensions for activity level,
persistence and negative affect and medical conditions
impacting daily life. Contextual characteristics related to
maternal education level and elementary school experience.
Behaviour style (temperament)
Behaviour styles may support or inhibit learning and
development. An early longitudinal study by Matheny
(1989) investigated the association of verbal and performance IQ score with temperament, as operationalized
by Thomas & Chess (1977), for children aged from 5 to
12 years. They identified consistent but weak relationships for activity level, adaptability and attention ⁄ persistence, although not at all ages tested. High activity level
was associated with lower full-scale IQ whereas adaptability and higher attention ⁄ persistence were both associated with higher full-scale scores. Since Matheny
collected these data the temperament measures of Thomas & Chess (1977) have been reduced and refined (see
Rothbart et al. 2000) and more complex interactions
between IQ and behaviour style have been demonstrated.
Lawson & Ruff (2004) observed children at ages 1 and
2 years on play tasks designed to measure attention and
negative emotionality. Together, these two behaviour
styles accounted for 15% of the variance in full-scale IQ
at age 3.5 years. High attentiveness and low negativity
were predictive of higher IQ scores for boys only, and
high attentiveness acted as a protective factor for children high in negative affect. Karrass & Braungart-Rieker
(2003) found that early distress to novelty (negative
affect) in infancy predicted higher IQ scores, but not for
children who demonstrated an insecure attachment style
with their mother. A mother’s interactive behaviours
may change in relation to a child’s behaviour style,
resulting in differential associations with cognitive
development.
Keogh et al. (1997), in a study of children with developmental delays found those with high ‘easy’ temperament ratings declined faster on cognitive assessments
than those with high ‘difficult’ ratings. Increased interac 2012 Blackwell Publishing Ltd, 25, 396–413
tions were hypothesized to support the greater cognitive
development of ‘difficult’ children (Keogh et al. 1997).
The specific nature of each child’s difficulties, however,
may have been associated with measures of both behaviour style and developmental quotient. Aetiology-specific studies are necessary to separate these hypotheses.
Maternal education level
Poverty is a major risk factor for poor cognitive outcomes
(see Emerson 2004; Umek et al. 2005) and may provide
one of the mechanisms behind the commonly found association between maternal education and child development (see Miller 1998). An additional explanatory
mechanism may be differences in children’s environments with parents with higher educational levels providing a more enriched environment (Umek et al. 2005).
Gilmore et al. (2009) found that maternal support for
autonomy was associated with higher levels of persistence and self-regulation for children with Down syndrome but not for a group of typically developing
children matched by mental age (MA). Parental ability
to engage their child in problem-solving behaviour may
have a greater effect on the developing abilities of children with learning or behavioural difficulties than on
development of children who are developing typically
(Hauser-Cram 1996; Neitzel & Stright 2004; Gilmore
et al. 2009).
Medical conditions
Medical conditions can have both direct and indirect
influences on IQ performance. Direct influences include
memory and attention difficulties that can occur with
conditions such as epilepsy and depression, and the
stunting of intellectual growth and lethargy identified
with severe hypothyroidism (Coleman 1994). Indirect
influences include side effects from medications, interruptions to learning due to hospital visits or frailty and
sleeping difficulties.
Down syndrome increases susceptibility to certain conditions, including congenital malformations of the heart
(Miller 1998) and gastrointestinal tract (Frid et al. 1999),
respiratory illness (Ugazio et al. 1992), visual (Dyke et al.
2007) and hearing conditions (Dyke et al. 2007). Conditions frequently seen with Down syndrome and associated with lower cognitive and learning outcomes include
hypothyroidism (Coleman 1994), ear, nose and throat
problems and sleep disorders (Shott & Joseph 2001; Andreou et al. 2002). In a longitudinal study comparing children with symptomatic congenital heart disease and
398 Journal of Applied Research in Intellectual Disabilities
those with mild or without heart conditions Miller found
no differences between groups in overall IQ. He did,
however, detect differences in some sub-domain scores of
the McCarthy Scale (McCarthy 1972) in relation to symptomatic heart conditions, including lower scores on the
perceptual-performance subscale at age 3 and on the
memory subtest at age 5 years. Earlier studies (Reed et al.
1980) identified larger associations between heart disease
and cognitive ability for children with Down syndrome
indicating that developments in the medical treatment of
heart defects may have mitigated associations with lower
cognitive abilities.
School experience
Turner & Alborz (2003) suggested that floor effects and
the wide increments that separate standard scores for
children with Down syndrome mask relationships
between IQ and education quality. The decision to
include school experience in the present study was made
because of its links to three sub-domains of cognitive
development: language, numeracy skills and memory.
Poor academic attainment for individuals with Down
syndrome can reflect a lack of opportunity to learn
rather than lack of capacity for this group, particularly in
relation to reading (Bochner et al. 2001) and number
skills (Bochner & Pieterse 1996). The development of
number skills, although typically poor for individuals
with Down syndrome, can be improved when specifically targeted in the curriculum (Bochner & Pieterse
1996). Vocabulary and memory skills may be improved
for children with Down syndrome through reading
instruction (Laws et al. 1995). Turner et al. (2008) found
that school placement type, while confounded by early
mental age (MA) scores and social advantages related to
mothers’ access to higher education, accounted for a
small amount of variance in academic attainment by age
21 years after controlling for academic attainment at
around 9 and 14 years of age. These researchers found
that individuals with high MA scores who attended a
mainstream school had higher academic achievement
scores at 21 years than those with equivalently high MA
scores who attended a segregated school.
Hypotheses
On the basis of the literature reported, we hypothesized
that female participants would score higher than male
participants on the verbal subtests (Vocabulary, Comprehension) and on the memory subtest (Memory for
Sentences) and male participants would score higher on
the spatial and visual processing subtests (Pattern Analysis, Quantitative, Bead Memory) of the SB-IV. We
anticipated that individuals rated high in persistence
would perform higher on most subtests except where
the subtest represents a specific weakness related to
Down syndrome, such as verbal memory (Brock & Jarrold 2005). Individuals identified as high in negative
mood and high in activity level were anticipated to
score lower on most subtests. A positive relationship
was expected between mothers’ education level and
cognitive competence, particularly for tests of verbal
ability (Vocabulary, Comprehension) and problem-solving abilities such as those assessed by the Pattern Analysis subtest. We also wanted to explore the possibility
that maternal education would interact with behaviour
styles (negative mood and persistence) to influence performance on the problem solving subtest of Pattern
Analysis. We expected medical conditions to be associated with poorer outcomes across all subtests, particularly when conditions were rated as severely interfering
with daily life. In addition, we hypothesized that children born in later years may have benefitted from
improvements in medical and educational services and,
as a result, demonstrate higher scores across subtests.
Method
Participants
The SB:IV protocols of 89 individuals who were participants in longitudinal research as part of the Down Syndrome Research Program, the Fred and Eleanor Schonell
Research Centre at the University of Queensland, were
used for the present analyses. The group represents a
subset of individuals included in growth curve analyses
of SB:IV subtest data (see Couzens et al. 2011). To be
included, a participant’s files had to contain information
on all the characteristics studied. Fifty-four of the participants had been recruited as part of a population sample
and 35 families had volunteered for participation in a longitudinal study. From the original population sample
consisting of 76 individuals, 11 did not have an SB:IV
assessment because they died at a very young age (n = 3)
they withdrew from the study early (n = 2) or contact
was lost due to moving from the area (n = 3) or other reasons (n = 3). An additional 11 individuals who were part
of the original population sample did not have temperament data collected during middle childhood and were
therefore unable to be included in these analyses. Two of
these individuals died early, two withdrew from the
study and contact was lost for other reasons for the other
2012 Blackwell Publishing Ltd, 25, 396–413
Journal of Applied Research in Intellectual Disabilities 399
seven individuals including late entry into the study and
distance barriers. Table 1 provides descriptive information on all participants included in the present analyses.
Table 1 Descriptive data available for
individuals (N = 89) including
independent measures in analyses
Table 2 provides details on full-scale MA scores that
were collected at ages 6 and 9 years for individuals who
had been part of the original population sample but
Variable (discrete)
Value
n
%
[0]
[1]
48
41
54
46
45
4
1
7
51
5
1
8
[0]
[1]
37
52
42
58
[0]
[1]
53
36
60
40
[0]
[1]
[2]
[3]
11
33
37
8
12
37
42
9
[0]
[1]
[2]
[3]
32
31
20
6
36
35
22
7
Variable (continuous)
n
Min
Max
M (SD)
Mothers’ age at birth
SB1 Mean Age Equivalent score in
years at 6 years
SB Mean Age Equivalent score in
years at 9 years
Activity (TTQ2) [0] low to [6] high
Neg3. affect (TTQ) [0] neg. to [6] rare neg.
Persistence (TTQ) [0] low to [6] high
69
51
20
1
45
3.88
30.38 (6.70)
2.65 (0.58)
85
1
4.91
3.54 (0.74)
89
89
89
1
1
1
5.83
7
6.33
3.23 (1.19)
4.48 (1.37)
3.95 (1.18)
Gender
Male
Female
Karyotype
Trisomy 21
Translocation
Mosaic
Unknown
Birth, year
1973–1976
1977–1981
Maternal education level
Year 10 (approx. age 15) or below
Year 11 or technical college and above
Medical condition
No conditions
Mild impact or treated
Moderate impact
Severe impact
Elementary school experience
Segregated (life skills)
Segregated (academic)
Mixed experiences
Regular elementary school
Data were missing for some individuals for karyotype (n = 32), mothers’ age at birth
(n = 20), MAE at age 6 (n = 38), and MAE at age 9 (n = 4).
Medical conditions were rated as having (i) no effect – no medical conditions were
reported in questionnaires used to acquire this information or in interviews; (ii) mild
effect – reports indicate that condition(s) had minimal effect on the person’s participation in daily life; (iii) moderate effect – reports of one or more medical condition(s) that
interfered for an extended period on the child’s participation at school and ⁄ or with
home activities; (iv) severe effect – reports of one or more conditions that substantially
inhibited participation in school and daily living activities through-out childhood.
1
Stanford-Binet Intelligence Scale.
2
Teacher Temperament Questionnaire.
3
Negative.
2012 Blackwell Publishing Ltd, 25, 396–413
400 Journal of Applied Research in Intellectual Disabilities
Table 2 Number of data points for all individuals in the
population sample and individuals included in the present
sample related to average MAs at age 6 and 9 years
Reasons
n
MA at age 6
(SD) [NA]
MA at age 9
(SD) [NA]
0
11
2.56 (0.94) [3]
3.19 (0.93) [7]
11
2.62 (0.80) [4]
3.09 (1.45) [3]
16
11
23
18
12
9
2.29
2.97
2.73
2.61
2.78
2.71
3.28
3.55
3.62
3.46
3.83
3.49
1
2
3
4
5
6
No temperament
data1
No SB:IV
assessments1
1
2
(0.94)
(0.37)
(0.39)
(0.45)
(0.31)
(0.14)
[6]
[4]
[8]
[5]
[5]
[5]
(1.14)
(0.60)
(0.56)
(0.59)
(0.68)
(0.62)
[2]
[3]
[1]
[1]
[NA], MA data were not available at ages 6 or 9 years for the
number of individuals indicated. Only 5 individuals were neither assessed at age 6 nor 9 years.
1
One individual was neither assessed at 6 or 9 years.
2
Two individuals were neither assessed at 6 or 9 years.
were not included in the present analyses and the MA
scores of individuals contributing different numbers of
assessments to these current data. Of the participants
with only one or two data points, three individuals were
difficult to assess due to particularly low ability levels,
for two individuals, illness within their family limited
their availability for assessments, for two individuals,
distance became a barrier and for the remaining individuals contact was lost at different times for time specific
reasons. The group with the greatest number of assessment points had higher average MA scores than those
without assessments. Data screening indicated that individuals from the population sample (born earlier) were
more likely than those born in later years to have mothers who had finished their formal education before age
15 years. The population sample also contained more
individuals who were rated as high in persistence by
their teacher during middle childhood. The sample is
not as representative of individuals from low SES backgrounds as would typically occur in the general population. The population and self-selected longitudinal
groups demonstrated comparable distribution on the
independent variables of gender, mother’s age at birth,
MAE scores at ages 6 and 9 years, medical impact and
behaviour style.
Whereas multilevel models are robust to missing
data, where these data are missing systematically, as
commonly occurs in longitudinal studies in relation to
retaining families with the most difficult life situations,
we cannot generalize findings as fully representative of
these individuals.
A power analysis using the software ‘Optimal Design’
(Spybrook et al. 2006) was conducted to ensure that data
from the 89 individuals were sufficient to detect main
effects for independent variables included in the models
of age-related change. Power estimates indicated that
available data were sufficient (power ‡ 0.8) for detecting
moderate to large main effects. Smaller effects (d < 0.5)
have a lower probability of being detected (power < 0.8)
irrespective of covariates included in the model.
Dependent measures
Dependent measures were raw scores for the six core
subtests of the SB:IV: Vocabulary, Comprehension, Quantitative, Pattern Analysis, Bead Memory and Memory for
Sentences. Information about these subtests is presented
in Table 3 along with descriptive information for the
sample at two ages (for indicative purposes). These subtests were designed and standardized for every year
group from ages 2 to 23 years. Vocabulary, Comprehension and Quantitative subtest scores are measures of crystallized intelligence, Pattern Analysis is a measure of
fluid intelligence and Bead Memory and Memory for
Sentences are measures of short-term memory (Thorndike et al. 1986). Analysis is justified at the subtest level
as the primary theoretical interest in this study is the
Down syndrome phenotype, and statistical correlation
between subtests is moderate (Couzens et al. 2011). Multilevel regression models were developed for each subtest.
Independent measures
Age cohort (individuals born from 1973 to 1976 and
individuals born from 1977 to 1981) and gender were
entered into the model as independent variables first, to
control for these effects on the outcome. The older
cohort had received minimal opportunities for early
intervention whereas systemic early intervention was
available for the younger cohort (see Crombie 1994).
Maternal education level and mother’s age at the birth
of their child with Down syndrome was obtained from
a demographic questionnaire completed by families as
they entered the longitudinal study. Maternal education
was dichotomized because most left school at the completion of either 10 or 12 years of schooling. The two
groups comprised individuals, who at the time of their
birth, had mothers who received no more than 10 years
of formal education and those whose mothers had
received 11 or more years of schooling including techni 2012 Blackwell Publishing Ltd, 25, 396–413
2012 Blackwell Publishing Ltd, 25, 396–413
0.78 to 0.94
0.79 to 0.96
0.80 to 0.95
0.85 to 0.96
0.82 to 0.95
0.85 to 0.94
Vocabulary
Comprehension
Quantitative
Pattern
Analysis
Bead Memory
Memory for
Sentences
1–9
10–12
13–16
17–(42)
1–4
5
6–8
9–10
11
12
13–18
19–(30)
1–6
7–16
17–24
25–36
37–(42)
1–10
11–(18)
1–6
7–(42)
1–14
15–(46)
Items
(max)2
Match dice (number of spots)
Count 5 spots on a dice
Match die sequence (2 die)
Add number of spots (2 die)
Add spots (2 die), show answer (1 die)
Complete die series, 1–2–3–4 (2 die)
Question relating to picture
Word problems (written & spoken)
Complete a 3 hole form board
Match 1 & 2 cube patterns (3D)
Match 3 & 4 cube patterns (3D)
Match picture (2D) of 3 & 4 cubes
Match picture (2D) of 6 & 9 cubes
Reveal 1–2 beads (2–3 s). Point to picture
Shown picture of beads on stick (5s),
remove, then build configuration
Repeat a 2–4 word sentence
Repeat 5 word sentence
Repeat a 7–8 word sentence
Repeat an 11–22 word sentence
Point to body parts (picture)
Answer comprehension questions
Name the picture
Give a definition for each word
Item description
6.17 (2.78)
AE = 3 years
6 months
5.83 (4.10)
AE = 2 years
9 months
10.23 (3.66)
AE = 4 years
3 months
13.1 (4.05)
AE = 4 years
6 months
9.17 (4.43)
AE = 3 years
6 months
5.93 (3.32)
AE = 4 years
0 months
Mean (SD)
15.51 (4.13)
AE = 5 years
6 months
12.44 (5.11)
AE = 4 years
5 months
8.64 (5.03)
AE = 5 years
0 months
17.74 (7.83)
AE = 6 years
6 months
9.97 (5.63)
AE = 4 years
8 months
7.53 (4.26)
AE = 3 years
2 months
2 ⁄ 22
0 ⁄ 23
0 ⁄ 12
4 ⁄ 17
0 ⁄ 14
0 ⁄ 17
14
9
5
9.5
6
5
Mean (SD)
Min ⁄
max
7
8
17.5
9
14
16
Median
Subgroup at 21 years (n = 71)
Median
Subgroup at 9 years (n = 30)
0 ⁄ 17
0 ⁄ 23
0 ⁄ 30
0 ⁄ 17
0 ⁄ 21
0 ⁄ 24
Min ⁄
max
Reliability coefficients are Kuder–Richardson Formula 20 (KR-20) indices of internal consistency for raw scores across each age from 4 to 23 years for the standardization sample (Thorndike et al. 1986).
2
Final item (in parentheses) indicates the maximum possible raw score for each subtest.
1
Reliability1
Variable
Stanford-Binet (4th edition) subtest information
Table 3 Descriptive data for dependent variables [raw scores and age equivalent (AE) scores] for individuals assessed at ages 9 and 21 years
Journal of Applied Research in Intellectual Disabilities 401
402 Journal of Applied Research in Intellectual Disabilities
cal college and beyond. Maternal age at the birth of
their child with Down syndrome ranged from 20 to
45 years, with most mothers in the programme giving
birth between the ages 25 and 35 years. Since there were
no very young mothers in the group, associations of
poor cognitive outcomes for children of young teenage
mothers (Keown et al. 2001) were not relevant to this
sample. Therefore, maternal age was not included in
these analyses.
Medical impact was rated on the basis of medical
reports in each individual’s file. Parents had been asked
about their child’s health using both unstructured interviews and questionnaires shortly after joining and during the study and this information was tabulated and
rated for each individual. A broad rating system, based
on the impact of a condition on daily living was developed and included four severity levels: no impact, mild
impact, moderate impact and severe impact. Two raters
initially reached 70.4% agreement in categorizing medical conditions based on database notes. Criteria were
then discussed and further rating decisions were made
until agreement was 100%.
Educational experience was rated for elementary schooling (ages 6–12 years) on the basis of interviews with
parents and visits by researchers to educational settings
during the data collection phases of the longitudinal
study. Placements could include a segregated special
school, a special class or unit in a regular school, a regular class with ‘pull-out’ supports and an inclusive class.
In segregated classes, children with Down syndrome
were provided a predominantly life-skills programme, a
predominantly academic programme, or a mix of these
programme types. In regular classrooms, children were
exposed to academic skills such as reading and number
skills. Some children (n = 20) experienced a range of
placements and were coded as having a mixed schooling experience.
The behaviour styles of activity level, negative mood
and persistence were measured each year, between the
years 1982 and 1989, using the Teacher Temperament
Questionnaire (TTQ, Thomas & Chess 1977). Assessments for individuals as close to age 10 years as possible were used for analysis, with age ranges for the
assessment stretching as low as age 7 years and as high
as 12 years 11 months to ensure that as many individuals as possible could be included. Internal consistency
was assessed across each of the first six occasions that
the TTQ had been used. Activity level had adequate
internal consistency (a ‡ 0.74). Only those items relevant
to negative mood were included in the analysis of the
Mood subscale (a = 0.66–0.80). Internal consistency on
the persistence subscale was improved by removing
items 22 and 28 (a = 0.65–0.77). Ethical clearance for this
project was provided by the ethics committee of The
University of Queensland.
Analytical method
The analytical approach taken in this article was to
assess the contribution of each of the independent variables in explaining a significant amount of variation in
subtest scores after accounting for baseline trends as
identified in previous research (Couzens et al. 2011). Due
to the relatively small sample size (N = 89) it is acknowledged that significant associations will generally be
detected for moderate to large effect sizes only. Associations that are not identified as significant in these data
should not be ruled out, however, this article focuses on
the results substantiated by the data available.
Baseline models included a quadratic term for age for
Vocabulary, Comprehension, Quantitative, Bead Memory and Memory for Sentences scores. The quadratic
model indicated that scores increased steadily throughout the schooling years prior to an observed plateaux
and downturn in early adulthood. A logarithm (log) of
age term was included in the model of change for Pattern Analysis scores as there was no evidence that the
scores on Pattern Analysis declined from any age in the
sample (Couzens et al. 2011). The best model for this
subtest demonstrated a relatively rapid rise in scores
through early and middle childhood becoming more
gradual but continuing to rise through adolescence and
early adulthood. These estimated trends for age were
used as baseline models for these analyses. The independent variables of interest were added into these
models of age-related change and model improvements
were assessed to determine whether the variable would
be retained in the final model. A systematic process for
adding blocks of independent variables was determined
to ensure consistency across each of the six subtests.
The first block of variables included gender and cohort
to control for their effects before assessing associations
with mothers’ education level, medical impact and elementary school experience. Medical impact and school
experience were included after maternal education
because these variables were likely to have been influenced, to some extent, by parental advocacy and opportunity. The final block of characteristics assessed
included the three behaviour styles and their interactions with maternal education. All independent variables were time-invariant as these data were not
collected on every assessment occasion.
2012 Blackwell Publishing Ltd, 25, 396–413
Journal of Applied Research in Intellectual Disabilities 403
The decision to retain a variable in the final model for
a specific subtest was made according to the level of
improvement in the model. This was determined using
deviance and pseudo R2 statistics. Variables retained in
the final model differed for each subtest. This was the
result of the exclusion of any variable that did not
explain a significant amount of variation in the subtest
score (see Table 4). Pseudo R2 statistics provide proportional estimates of the variance explained by the addition of each variable.
The last step in the model identification process
involved an assessment of differential associations for
each variable at different ages. Interactions of main
effects with age were entered individually into each
model and retained where improvements were
observed. (Note: With the exception of Vocabulary, only
final models are provided herein. Interim models are
available on request.)
Interpreting regression coefficients
The age variable was centred so that 4 years, the earliest
age an assessment was given, was represented at the
intercept. Independent variables were also centred to
Table 4 Building the final model for subtest scores on the Vocabulary subtest. SE – italics (288 occasions)
Model C
Predictor added
None
Fixed effects
Initial status
10.90c (0.78)
Intercept
Rate of change
0.44c (0.10)
Intercept
)0.01b (0.003)
Age2
M’s education [£years 10]
M’s education [>years 10]
Persistence (0 = low)
Persistence · age
Medical impact [None]
[Mild]
[Mod]
[Severe]
Primary school [Life-sk]
[Acad]
[Mixed]
[Reg. S]
Variance components
Level 1
Within-person
2.54c (0.31)
Level 2
In initial status
13.74c (3.37)
Time
Variance
0.07c (0.02)
Covariance
)0.53a (0.21)
Pseudo R2 and goodness-of-fit statistics
Total outcome variation
0.119
Within residual variance
0.504
Between residual (intercept)
0.190
Between residual (slope)
0.141
)2*log likelihood (IGLS)
1388.69
Model N
M’s education
Persist
Persist · age
Medical
School
9.90c (0.86)
7.64c (1.29)
9.87c (1.60)
11.45c (1.89)
11.09c (1.78)
0.47c (0.10)
0.45c (0.10)
0.28a (0.13)
0.28a (0.13)
0.28a (0.13)
)0.01c (0.003)
)0.01a (0.004)
)0.01c (0.003)
)0.01c (0.003)
)0.01c (0.003)
2.01b (0.71)
2.34c (0.71)
0.75a (0.31)
2.32c (0.70)
)0.08 (0.47)
0.07a (0.03)
1.99b (0.66)
)0.32 (0.46)
0.07a (0.03)
1.20a (0.61)
)0.48 (0.46)
0.07a (0.03)
0.57 (1.05)
)1.22 (1.04)
)3.67a (1.46)
0.04 (0.94)
)1.24 (0.92)
)4.44c (1.30)
2.20b (0.71)
1.06 (0.80)
5.44c (1.19)
2.53c (0.30)
2.51c (0.30)
2.54c (0.30)
2.52c (0.30)
2.50c (0.30)
13.68c (3.36)
15.23c (3.60)
13.59c (3.34)
12.91c (3.21)
11.77c (3.00)
0.07c (0.02)
)0.58b (0.21)
0.07c (0.02)
)0.67b (0.22)
0.06c (0.02)
)0.56b (0.20)
0.06c (0.02)
)0.59b (0.20)
0.06c (0.02)
)0.65b (0.20)
0.166
0.506
0.194
0.128
1381.14
0.195
0.510
0.102
0.141
1375.74
0.196
0.504
0.199
0.244
1370.67
0.258
0.508
0.239
0.231
1357.56
0.382
0.512
0.307
0.192
1336.18
All effects were originally tested with those significantly improving the model retained in this final model.
<0.05, b<0.01, c<0.001.
a
2012 Blackwell Publishing Ltd, 25, 396–413
404 Journal of Applied Research in Intellectual Disabilities
ensure meaningful zero values (see Table 1). The most
reliable indicator that a variable explained substantial
variation in the model was change in deviance statistics
when the variable was first added into the model.
Model fitting issues
Since the data sets were relatively small and unbalanced
(individuals were measured at different ages and had
different numbers of assessment points) the number of
parameters estimated in the model were often restricted
by the size of the data set. These restrictions resulted in
simple but relevant models. In these parsimonious models the between-person variance was represented by a
random intercept only.
Results
To assess associations between each of the independent
variables – maternal education, medical impact,
elementary school experience and behaviour style for
individuals – correlational analyses were conducted.
Associations with school experience, a categorical
variable, were assessed using the chi-squared test of
independence and all other associations were assessed
using Kendall’s tau-b. Two associations reached significance: maternal education with elementary school experience of the individual (v23 ¼ 10:14, P < 0.05, N = 89)
and negative mood with activity level (Kendall’s TauB = )0.29, P < 0.01). Mothers with higher education levels were significantly more likely to obtain an integrated
or academic school placement for their child and
individuals who were rated as more active in middle
childhood were also rated to be somewhat more argumentative, likely to complain and likely to become upset
more easily.
The final model developed for Vocabulary is provided
in Table 4 and demonstrates model improvements with
the addition of each independent variable that was
retained on the basis of the change in deviance. Initial
growth models and final models are shown in Table 5 for
each of the Comprehension, Quantitative, Pattern Analysis, Bead Memory and Memory for Sentences subtests.
Crystallized abilities
On the Vocabulary, Comprehension and Quantitative
subtests, significant main effects were detected for
maternal education level (Vocabulary: v21 ¼ 10:01,
P < 0.01; Comprehension: v21 ¼ 7:27, P < 0.01; Quantitative: v21 ¼ 7:70, P < 0.05) and elementary school experi-
ence (Vocabulary: v23 ¼ 23:28, P < 0.001; Comprehension:
v23 ¼ 18:45,
P < 0.001;
Quantitative:
v23 ¼ 27:54,
P < 0.001). Individuals whose mother received formal
education beyond age 15 years scored an average
increase of 1.2 raw scores on both the Vocabulary and
Quantitative subtests. On all subtests, as additional
independent variables were added into the model, the
association with maternal education reduced. Students
who experienced an academic programme in a special
school or went to a regular elementary school, respectively, scored an average of 2.2 or 5.4 points higher on
the Vocabulary subtest, an average 3 or 5.6 points
higher on the Comprehension subtest and 3.9 or 5.7
points higher on the Quantitative subtest than individuals who experienced a ‘life skills’ programme in a special school. For students who experienced a mixture of
elementary school programmes, significant advantages
over a life skills programme were only detected on the
Quantitative subtest, with these individuals scoring an
average of 2.4 points higher than individuals who had
attended a life skills programme in a special school.
A main effect for persistence was detected on the
Vocabulary (v21 ¼ 5:40, P < 0.05) and Quantitative subtests (v21 ¼ 7:45, P < 0.01) but not on the Comprehension
subtest. For both Vocabulary and Quantitative subtests
the detection of a significant interaction of persistence
by age (Vocabulary: v21 ¼ 5:07, P < 0.05; Quantitative:
v21 ¼ 4:13, P < 0.05) indicated that individuals with high
persistence ratings in middle childhood developed relatively faster on these subtests.
Significant associations with medical conditions were
found for the Vocabulary (v23 ¼ 13:11, P < 0.01) and
Comprehension subtests (v23 ¼ 11:19, P < 0.025) when
entered into the final model, but not on the Quantitative
subtest (v23 ¼ 5:18, P = 0.159). Only medical conditions
with a severe impact on daily life were significantly
associated with lower scores. These individuals scored
an average 4.5 points lower on the Vocabulary subtest
than those for whom no medical conditions were indicated. Although individuals with moderate health
impact scored an average 1 or 1.7 points lower on the
Vocabulary and Comprehension subtests, respectively;
the large variability within the group with moderate
medical impact [shown by the standard errors (SE),
Table 4 and Table 5] indicate non-significance on the
basis of this traditional indicator.
Significant declines in deviance from the original quadratic model to the final model were demonstrated for
Vocabulary (v29 ¼ 52:52, P < 0.001), Comprehension
(v28 ¼ 41:17, P < 0.001) and for the Quantitative subtests
(v26 ¼ 46:82, P < 0.001). These provide useful interim
2012 Blackwell Publishing Ltd, 25, 396–413
0.75c (0.11)
)0.02c (0.004)
0.68c (0.13)
)0.02c (0.004)
2012 Blackwell Publishing Ltd, 25, 396–413
–
–
–
3.86c (0.78)
2.42b (0.92)
5.72c (1.33)
–
–
–
5.87c (0.58)
5.89c (0.59)
12.59c (2.24)
)1.07 (1.12)
)1.76 (1.09)
)5.51c (1.53)
3.00c (0.84)
1.70 (0.94)
5.60c (1.45)
–
–
–
5.43c (0.55)
7.84c (1.50)
35.54c (6.07)
–
0.11 (0.06)
6.51c (1.31)
1.23 (0.69)
)0.23 (1.07)
16.91c (3.19)
14.28c (2.72)
–
–
–
3.42b (1.12)
0.78 (1.33)
6.07b (2.00)
8.19c (1.79)
10.10c (1.00)
3.35c (0.90)
0.93 (1.04)
4.81b (1.54)
–
1.66a (0.80)
4.10c (0.41)
8.86c (1.60)
5.11c (1.01)
4.04c (0.40)
–
–
–
2.84c (0.67)
2.76c (0.78)
6.10c (1.18)
–
–
–
0.10a (0.05)
)0.37 (0.89)
–
1.13c (0.33)
–
0.48c (0.11)
)0.02c (0.003)
NA
–
1.47 (0.94)
–
)3.51 (3.04)
)1.55 (3.55)
)8.65 (5.55)
11.46c (1.14)
0.54c (0.10)
)0.02c (0.003)
3.30c (0.73)
M for Sentences (284)
–
0.92c (0.15)
)0.03c (0.005)
NA
–
)3.43a (1.55)
–
–
–
10.07c (1.00)
0.83c (0.15)
)0.02c (0.005)
2.99b (1.10)
Bead Memory (291)
)3.88a (1.63)
)5.42c (1.61)
)9.72c (2.29)
–
1.93 (1.08)
0.66 (0.46)
–
0.08 (0.46)
0.05 (0.03)
–
–
NA
NA
0.12 (1.39)
)1.23 (0.92)
11.75b (4.36)
0.73a (0.34)
12.50c (1.24)
NA
NA
4.61c (0.50)
3.06a (1.40)
–
0.70c (0.12)
)0.03c (0.004)
NA
–
)1.13 (1.54)
Pattern Analysis (292)
–
–
2.50b (0.88)
Quantitative (290)
6.20c (1.43)
Comprehension (282)
Fixed effects
Initial status
Intercept
6.17c (0.86)
Rate of change
Intercept
0.75c (0.11)
2
)0.02c (0.004)
Age
Loge age
Neg. mood
(0 = very neg)
Neg. mood · age
(Loge age)
Persistence (0 = low)
Persistence · age
M’s education [£years 10]
[>years 10]
[£years 10] · age
[>years 10] · age
Medical [None]
[Mild]
[Mod]
[Severe]
School [Life skills]
[Acad. Spec]
[Mixed]
[Reg. School]
Sch · age [L-skills · age]
[Acad. Sp · age]
[Mixed · age]
[Regular · age]
Variance components
Level 1
Within-person
5.49c (0.56)
Level 2
In initial status
13.48c (2.38)
Subtest (occasions tested)
Table 5 Initial and final models for Comprehension, Quantitative, Pattern Analysis, Bead Memory and Memory for Sentences (SE) (n = 89)
Journal of Applied Research in Intellectual Disabilities 405
1336.23
1377.76
1462.42
0.020
NA, not applicable to the model; –, No significant effect during model building and therefore not included in this final model.
<0.05, b<0.01, c<0.001.
a
1511.63
1421.25
1464.81
1764.21
1689.95
1643.79
1608.00
0.429
0.009
0.429
0.005
0.526
0.464
)0.034
0.430
0.003
0.139
0.125
0.124
0.126
0.326
0.178
0.176
0.222
statistics
0.128
Pseudo R2 and goodness-of-fit
Total outcome
variation
Within residual
variance
Between residual
(intercept)
)2*log
likelihood(IGLS)
0.214
Comprehension (282)
0.390
0.090
0.265
0.348
0.058
0.348
0.126
0.511
Fluid abilities
0.387
0.158
models for understanding associations with raw scores
over time while there remains much-unexplained variance still to be understood.
Subtest (occasions tested)
Table 5 Continued
Quantitative (290)
Pattern Analysis (292)
Bead Memory (291)
M for Sentences (284)
406 Journal of Applied Research in Intellectual Disabilities
Main effects for Pattern Analysis, the only core subtest
on the SB-IV measuring fluid abilities, were detected in
relation to persistence (v21 ¼ 6:14, P < 0.025), maternal
education level (v21 ¼ 8:61, P < 0.01) and medical impact
(v23 ¼ 12:83, P < 0.01). The final model indicated that
individuals rated high in persistence scored an average
0.7 points higher on Pattern Analysis than those rated
low in persistence, individuals with mothers educated
beyond year 10 scored an average 1.9 points higher than
those whose mother’s received 10 or fewer years of formal education. Mild, moderate and severe medical
impacts were associated with significantly lower scores
in contrast to the condition of no medical impact. Mild
impact associated with an average decrease of 3.9
points, moderate impact an average 5.4 points decrease
and severe impact an average decrease of 9.7 points.
Interactions with age were detected for negative mood
(v21 ¼ 7:52, P < 0.01) and school experience (v23 ¼ 15:25,
P < 0.01). Low negativity rated in middle childhood and
an academic programme in either a special or a regular
elementary school were associated with faster development on the Pattern Analysis subtest. Significant associations were not indicated for individuals who received
mixed elementary school experiences as scores for this
group were more variable (see SE, Table 5).
An additional analysis was implemented to assess
whether associations with negative mood and Pattern
Analysis scores changed in relation to persistence rating
(as suggested by Lawson & Ruff 2004). This was tested
using procedures described by Krull & MacKinnon
(2001). No mediated effects were detected.
Short-term memory abilities
Main effects for persistence (v21 ¼ 10:98, P < 0.001),
maternal education level (v21 ¼ 7:02, P < 0.01) and elementary school experience (v23 ¼ 17:79, P < 0.001) were
detected in relation to Bead Memory raw scores. Individuals rated high in persistence in middle childhood
scored an average 1.1 points higher than individuals
rated as low in persistence and individuals with mothers
educated beyond year 10 scored an average 1.7 points
higher than those with mothers accessing 10 or fewer
years of formal education. Individuals who received an
academic programme in a special school scored an
2012 Blackwell Publishing Ltd, 25, 396–413
Journal of Applied Research in Intellectual Disabilities 407
average 3.4 points higher, and individuals who went to a
regular elementary school scored an average 4.8 points
higher on the Bead Memory subtest than individuals
who had received a life-skills programme. Medical conditions did not improve the model significantly when
entered into the final model after mothers’ education
level and persistence (v23 ¼ 5:95, P = 0.114).
On the Memory for Sentences subtest, main effects
were only detected in relation to maternal education
(v21 ¼ 7:22, P < 0.01) and elementary school experience
(v23 ¼ 29:55, P < 0.001). An additional interaction
detected for maternal education by age (v21 ¼ 4:75,
P < 0.05) indicated that individuals with mothers who
accessed more than 10 years of formal education tended
to develop faster on the Memory for Sentences subtest
than those with mothers who accessed 10 or fewer years
of formal education. Individuals who experienced an
academic programme in a special school or had a mixed
programme, scored an average 2.8 points higher on
Memory for Sentences while those who attended a regular school scored an average 6.1 points higher on this
subtest in contrast to individuals who accessed a lifeskills programme during their elementary years.
A summary of the variance accounted for when variables were entered into models for each subtest is
provided in Table 6. Total variance refers to the proportional amount of additional variance an independent
variable explains when included in the model. Betweenperson variance refers to the proportion of the total
between-person variation that was estimated through
the unconditional growth model of subtest scores that is
explained by each variable.
Discussion
This research involved complex analyses of longitudinal
data from a small number of individuals with Down
syndrome. Associations identified in these analyses have
been demonstrated with a relatively small population
sample across a limited age range. A power analysis
indicated that these data were sufficient to detect associations with medium to large effect sizes. Although
weaker and more complex associations may not have
been detected in this study, those that were detected
were relatively strong, emphasizing their importance in
future research and intervention.
Cognitive domains
The purpose of this study was to explore specific cognitive abilities for individuals with Down syndrome and
2012 Blackwell Publishing Ltd, 25, 396–413
differentiate characteristics of individuals and their environments that might be related to the development of
each. This goal provided the justification for analysis at
the subtest level. Where high correlation was indicated
between subtests, they have been grouped and trends
for the domain are indicated. Where correlation between
subtests was of a medium magnitude, differences in
associations were more evident.
The Vocabulary, Comprehension and Quantitative
subtests all measure crystallized abilities and demonstrated high correlation for this group (Couzens et al.
2011). Associations with maternal education, and primary school experience were similar.
Those who experienced a mix of elementary school
programmes performed significantly better on the Quantitative subtest than those who received a life-skills programme in their elementary years. This suggests that
the increased exposure to numeracy instruction likely to
have been experienced by the first group was influential
in increasing mathematical skills (see also, Turner & Alborz 2003). All associations between schooling experience and performance on the subtests of the SB-IV need
to be interpreted cautiously as children who were
enrolled in integrated settings or who received a more
academically oriented programme in segregated settings
may have been more able. In addition, children in these
settings were more likely to have better educated mothers, and may thus have experienced home environments
that were more supportive of cognitive growth.
Pattern Analysis, measuring fluid ability, was moderately associated with the crystallized ability subtests of
Vocabulary and Comprehension, and the short-term
memory subtest Memory for Sentences (Couzens et al.
2011). Low levels of negative mood, high persistence
and higher levels of maternal education were associated
with higher Pattern Analysis scores. Academic experiences during the elementary years were associated with
higher scores and medical conditions at all levels of
impact were associated with lower Pattern Analysis
scores.
Rate of development on Pattern Analysis was slower
for individuals high in negative mood and faster for
individuals who received academically focussed programmes during their elementary years. These findings
suggest there is scope for improving abilities that underpin the Pattern Analysis subtest.
Differential associations were expected for the two
short-term memory subtests of Bead Memory and Memory for Sentences based on established differences in the
visual and verbal abilities of individuals with Down
syndrome (Brock & Jarrold 2005) even though they have
ns
ns
ns
30.7% i
19.2% s
ns
38.2%
ns
6.8% i
Increase for
slope
ns
12.4%
ns
ns
ns
ns
4.0% i
ns
ns
6.2%
ns
38.7%
ns
ns
ns
14.1%
ns
ns
5.0%
6.4%
0.4%
ns
ns
ns
ns
12.8%
Total
ns
43.0%
ns
ns
ns
21.2%
ns
ns
9.9%
9.6%
0.3%
ns
ns
ns
ns
0.2%
Between
Comprehension
0 3%
33.3%
ns
ns
19.2%
ns
ns
ns
4.6%
ns
ns
ns
5.9%
1.4%
9.0%
Total
decrease
45.4%
ns
ns
29.9%
ns
ns
ns
9.0%
ns
ns
ns
6.9%
0.6%
0%
Between
Quantitative
51.1%
ns
2.0%
ns
11.8%
ns
8.3%
6.1%
ns
3.9%
1.1%
2.1%
ns
15.8%
Total
52.6%
ns
1.9%
ns
16.7%
ns
12.0%
9.3%
ns
5.2%
0.8%
6.4%
ns
0.3%
Between
Pattern Analysis
ns
37.7%
ns
ns
ns
11.5%
ns
ns
ns
5.1%
ns
ns
ns
5.5%
ns
12.6%
Total
ns
50.2%
ns
ns
ns
21.2%
ns
ns
ns
9.2%
ns
ns
ns
13.3%
ns
0.5%
Between
Bead Memory
ns
34.8%
ns
ns
22.8%
ns
ns
ns
5.7%
0.5%
ns
ns
ns
ns
5.8%
Total
ns
42.9%
ns
ns
32.3%
ns
ns
ns
9.6%
0.1%
ns
ns
ns
ns
0.9%
Between
Memory for
Sentences
i is between-person variance at the intercept, s is between-person variance in slope (only for Vocabulary subtest scores because of convergence errors on other subtests).
Effect of school on age-related change
Age · mixed school experiences
Age · academic programme
(special school)
Age · regular school experience
Variance accounted for in the
final model
0.4%
ns
4.7%
ns
ns
ns
ns
9.7% i
10.3% s
ns
ns
2.9%
0.1%
Mothers’ education
Mothers’ education level
Mothers’ education level · age
Medical
Mild medical conditions
Moderate medical conditions
Severe medical conditions
Elementary School experience
Mixed school experience
Academic special school programme
Regular elementary school
19.0% i
14.1% s
11.9%
Variance accounted for in the base Model
(Quadratic or loge age model)
Behaviour style
Negative mood
Negative mood · age
Persistence
Persistence · age
Between
Total
Significant predictors
Vocabulary
Table 6 Summary table of incremental increase in variance explained as each significant predictor is added into the final model for each subtest
408 Journal of Applied Research in Intellectual Disabilities
2012 Blackwell Publishing Ltd, 25, 396–413
Journal of Applied Research in Intellectual Disabilities 409
been found to be moderately correlated for individuals
with Down syndrome (Couzens et al. 2011). High persistence was significantly associated with higher Bead
Memory scores but not with Memory for Sentences. Success on visual memory tasks may be more amenable to
effort than memory tested through the auditory mode.
This is consistent with reduced variability in Memory
for Sentence scores found for this population (Couzens
et al. 2011) and with memory training studies that have
shown an advantage of training for visual versus verbal
memory for this population (see Laws et al. 1996).
Although higher maternal education was associated
with higher scores on both short-term memory subtests,
only on the Memory for Sentences subtest was maternal
education related to a faster rate of development. This
complements research linking higher maternal education
to environments that are superior for children’s language development (Bornstein & Tamis-LeMonda 1997;
Keown et al. 2001; Umek et al. 2005).
Intra-individual characteristics
Individuals who were rated high in persistence during
middle childhood demonstrated a small overall benefit
on the Pattern Analysis and the Bead Memory subtests
and faster development on the Vocabulary and Quantitative subtests. Although high persistence was not associated with overall higher Vocabulary raw scores, an
age by persistence interaction was detected. The interaction of age by persistence on the Vocabulary subtest
may partly reflect the nature and form of the tasks in
the Vocabulary subtest rather than growth in vocabulary
size alone. Individuals who progressed into the descriptive items of the subtest (from item 15) were often asked
to repeat or refine an initial answer; those who were
prepared to comply by repeating or elaborating on their
answer demonstrated persistence not seen in those who
refused.
Associations with negative mood only reached significance for scores on the Pattern Analysis subtest; with an
age by mood interaction indicating slower growth on
the Pattern Analysis subtest for individuals high in negativity. The finding that scores on the Pattern Analysis
subtest were higher for individuals rated low in negative affect and also for individuals rated high in persistence is consistent with Lawson & Ruff’s (2004) research
with younger, typically developing children for whom
negativity and persistence were important predictors of
IQ scores. González et al. (2001), studying associations
between behaviour style and attention in typically
developing children, found poor attention was
2012 Blackwell Publishing Ltd, 25, 396–413
associated with parent ratings of child susceptibility to
anger and discomfort. The authors suggested that children high in negative affect have more difficulty filtering out non-relevant information in a task. It is possible
that our findings of significant associations between Pattern Analysis scores with negative affect and persistence
reflect the interplay of two different but complementary
systems – i.e. attention and motivation.
Although no interactions or mediated associations
were detected between persistence and negative affect
on Pattern Analysis scores the relatively small dataset
may have resulted in insufficient power to detect associations and mediator variables. Further investigation of
persistence and negative affect and interactions associated with cognitive development and learning is warranted. As negative affect and persistence are associated
with poor learning and performance, intervention studies that target these behaviour styles represent promising areas for research.
Health was clearly associated with performance on
the SB-IV for the individuals in this study as demonstrated by the decreasing log-likelihood statistic as medical conditions were entered into all subtest models.
Although the present study found that only the most
severe health conditions were associated with large subtest score differences, as did Carr (1988) and Crombie
(1994), caution needs to be taken in interpreting this
finding. Firstly, significant associations were found for
all levels of severity and performance on Pattern Analysis. Secondly, the absence of significance is not evidence
of no effect with this relatively small sample size, and it
is important to consider the size of effects and standard
errors, both of which were large. Thirdly, the large variation in subtest scores between individuals with moderately impacting medical conditions are likely related to
the coarse measures of medical conditions that were
used in these analyses. Because of the complex nature of
medical conditions and their differential influences on
participation in daily activities, the impact of health conditions was kept as simple as possible. This limited
information available to these analyses as data on specific medical conditions were aggregated.
On the basis of change in deviance statistics, the association of medical conditions with subtest scores for
Bead Memory and Quantitative subtests disappeared in
the final models when persistence and maternal
education were entered into the model first. Persistence
and maternal education on these subtests explained all
the variance that was accounted for by medical conditions during the model building process. Medical conditions may have contributed to reduced persistence,
410 Journal of Applied Research in Intellectual Disabilities
which in turn was associated with poorer performance.
Only on the Vocabulary and Pattern Analysis subtests
did medical conditions explain variance over and above
that explained by associations with persistence.
Further studies are required to understand the relationships between medical conditions and persistence
and development of individuals. Specific conditions
have been identified in the literature as influencing performance on tests of intelligence (Coleman 1994; Miller
1998; Andreou et al. 2002). Longitudinal studies are
required to determine how these contribute to changes
within a person over time and contribute to variation
between individuals with Down syndrome. Medical
information, collected at each assessment occasion,
would add precision to the identification of specific
associations with cognitive development and could
guide the type and timing of supports for individuals
with specific conditions.
Environmental characteristics
Higher levels of maternal education were associated
with higher scores on all subtests during the building of
the final models. The size of the influence was small
and reduced or disappeared on all but the Memory for
Sentences subtest, once elementary school experience
was built into the models. Increased rate of development on Memory for Sentences by individuals with
mothers who had higher levels of education supports
the enhancement of verbal environments in the home
for promoting practice and development in auditory
memory for this population.
Associations of educational programme with higher
scores on all subtests reflect the findings of Turner et al.
(2008) who found a similar advantage for children with
Down syndrome attending regular school settings in
relation to academic attainments. As found by Turner
et al. (2008), selection processes can largely explain associations between cognitive abilities and elementary
school experiences as only students with the higher MA
scores prior to school entry were able to access inclusive
settings. Within our sample individuals in the oldest
group were often denied access to mainstream education on the basis of having an intellectual disability,
whereas individuals in the younger group were generally accepted or denied entry to educational placements
on the basis of performance on a test of intelligence (see
Swan 1996). Access was influenced by where the individual was living, effective advocacy related to school
placement, and the historical time the individual entered
elementary school. Like Turner et al. (2008), our data
indicate that higher maternal education positively influenced school placement decisions. Unlike Turner et al.
(2008), we could not separate the influence of school
placement on subtest scores; however, the large effect
sizes related to school placement in our data warrant
further controlled studies that investigate the proximal
aspects of school placement and inclusive practices on
academic motivation, performance and development for
different cognitive abilities.
Students who attended a regular elementary school
demonstrated the highest scores across all subtests followed by those who experienced an academic programme in a special school. Since students who
received an academic elementary education were scoring higher at age 4-years than those who received a life
skill programme during their elementary years, the large
effect size found for school placement in this study cannot be separated from the higher cognitive level of these
students on entering school. The additional association
with rate of development specific to the Pattern Analysis
subtest, however, indicates faster development related
to accessing a local elementary school and academic
programme on this subtest. Although these data do not
indicate causation, they provide support for the speculation that regular school placements and academically
oriented programmes result in higher cognitive outcomes generally and more rapid development of abilities measured by the Pattern Analysis subtest than lifeskill programmes provided in segregated settings (Buckley et al. 2006).
The mechanisms by which school programme type is
associated with development of abilities measured by
the Pattern Analysis subtest are complex and require
finer analysis than was possible in the present study.
There were large and fundamental differences in philosophy, teaching methods and expectations underpinning
academic and life-skill programmes. Expectations of students in academic programmes were likely to reflect an
emphasis on thinking, exploring, and solving novel
problems. Life-skill programmes, on the other hand,
were predominantly based on behavioural learning theory, where individuals learnt behaviours of self-care,
daily living and community access through teacherdeveloped systems of cueing and prompting. Evidence
reported herein supports Flynn’s (2007) argument that
everyday experiences are likely to influence subtest level
scores and extends this to individuals with an intellectual disability and Down syndrome specifically. Appropriate, cognitively challenging expectations and
scaffolds that allow students to work on increasingly
novel and abstract tasks should assist the development
2012 Blackwell Publishing Ltd, 25, 396–413
Journal of Applied Research in Intellectual Disabilities 411
of cognitive abilities assessed by intelligence tests.
Where inherent barriers exist permanent supports are
required; however, if a cognitive ability is free from
innate barriers, such as abilities measured by the Pattern
Analysis subtest appear to be (see Couzens et al. 2011),
deprivation within the learning environment appears to
be an important consideration in relation to limiting
development. Although the support for this hypothesis
is limited to one subtest in the present analyses, this
provides a starting point for considering the effect of
education programme on rate of development for other
cognitive abilities.
those who experienced a severe impact from medical
conditions performed between 30 and 65 months lower
in age equivalent scores than those with no impact.
Limitations
No associations were detected between performance and
age cohort or gender. The lack of a female advantage on
verbally loaded tests contrasts with previous findings
(Carr 1988; Crombie 1994; Martins & Castro-Caldas 2005;
Neubauer et al. 2005). Within the sample studied herein
several male participants scored at high levels in relation
to the group; however, this data set had a larger proportion of female participants scoring at higher levels. These
data indicate that, although female participants may be
more likely to score at higher levels on cognitive assessments, male participants can equally attain high scores,
although fewer male participants appear to do so.
The sample included in this study is large relative to
other studies of this low incidence population; however,
the size of the sample placed limits on the analyses that
could be performed and the complexity of associations
that could be detected. In addition, the sample is not a
random sample as participants came from families willing to participate in longitudinal research. The fact that
all the SB-IV subtests correlate with each other indicates
that a multivariate multilevel analysis would provide
more information than the univariate analyses performed here. This was beyond the scope of the current
research. Other limitations already discussed include the
lack of sophistication in the measurement of health, and
the confounds between age cohort and educational
placement and between ability level and educational
placement. Finally, there are limitations associated with
the incomplete match between abilities measured by
tests of intelligence, primarily formulated for use with
individuals with no organic impairments, and the constructs they represent in individuals with Down syndrome (Couzens et al. 2004).
Clinical significance
Conclusion
Although we can be confident that the associations we
have identified are robust as they are evident with a relatively small sample, their clinical significance is less easily
ascertained. Main effects shown in the final models were
associated with average differences of between 1 or 2 raw
scores (behaviour style and maternal education), and 5–6
raw score differences (school experience and medical
conditions). The largest average score difference was the
reduction of 9.7 raw scores associated with severe medical conditions on the Pattern Analysis subtest.
The standard errors of measurement of the subscales
of SB:IV combined with the large variation we identified
in within-person scores, indicates that interpretation of
small changes (e.g. 1–2 raw scores) identified in relation
to this assessment is inappropriate, and differences of
this size may have little impact on individuals’ day-today functioning. When considering larger raw score differences, however, it is possible to calculate a rough
measure of average change in age equivalence on the
subtests drawing on data presented in the technical
manual of the SB:IV. For example, on Pattern Analysis
This study identified characteristics associated with cognitive development for individuals with Down syndrome
informing important areas for intervention and future
research. Development of methods for detecting and
reducing early negativity and promoting persistence are
supported by the research. Early attention to verbal and
problem solving opportunities within the family environment are emphasized for supporting both verbal and
non-verbal abilities and the study indicates benefits of
academically focussed schooling for promoting cognitive
development, at least in relation to certain visual processing tasks. Additional longitudinal studies are required
that include time variant predictors to identify the timing
of specific proximal processes within the home, school
and individual that contribute to development with age.
Control variables
2012 Blackwell Publishing Ltd, 25, 396–413
Acknowledgments
Preparation of this manuscript was supported by an
Australian Postgraduate Award from the Commonwealth of Australia. Data were collected through the
412 Journal of Applied Research in Intellectual Disabilities
Down Syndrome Research Program (DSRP). The DSRP
was founded by Robert J. Andrews, Paul Berry, Patricia
Gunn and Corralie Price and other contributors include
Alan Hayes, John Elkins, David Chant, Anne Jobling,
Mary Crombie, Sali Smith, Linda Gilmore and Jan
Lloyd. Thanks are also extended to the families participating in this longitudinal project and in particular to
the Michael Cameron fund which has enabled the continuation of this research.
Correspondence
Any correspondence should be directed to Donna Couzens, School of Education, Faculty of Social and
Behavioural Sciences, The University of Queensland, St
Lucia Campus, Brisbane, Queensland, Australia (e-mail:
[email protected]).
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