Early Childhood Research Quarterly 47 (2019) 507–517
Contents lists available at ScienceDirect
Early Childhood Research Quarterly
A longitudinal population study of literacy and numeracy outcomes
for children identified with speech, language, and communication
needs in early childhood
Sharynne McLeod ∗ , Linda J. Harrison, Cen Wang
Charles Sturt University, Bathurst, NSW, Australia
a r t i c l e
i n f o
Article history:
Received 25 March 2016
Received in revised form 16 July 2018
Accepted 31 July 2018
Available online 28 August 2018
Keywords:
Speech
Language
Communication
Literacy
Numeracy
Growth trajectories
a b s t r a c t
Speech and language competence in early childhood can influence academic achievement at school. The
aim of this research was to examine longitudinal progress in literacy and numeracy achievement from age
8 through 12 years for children identified as typically developing or with speech and language concern
(SLC) based on parent-reported concern about speech and language at ages 4–5 and 6–7 years. Participants
were 4322 children in the K(indergarten) cohort and 4073 children in the B(irth) cohort of the Longitudinal
Study of Australian Children (LSAC). The majority of children identified with SLC had not accessed speechlanguage pathology services. Linked data from national testing of literacy and numeracy achievement
were analysed for the K cohort in Grades 3, 5, and 7, and for the B cohort in Grade 3. Cross-sectional
analyses showed that children with SLC achieved lower scores for reading, writing, spelling, grammar,
and numeracy at all assessment points than children with typical speech and language skills. Results
for all children, however, were above the national minimum standard for each grade level. Longitudinal
growth curve analyses showed no difference in the growth trajectories for literacy and numeracy test
scores for children in the typically developing and SLC groups, suggesting that SLC children showed
typical patterns of progression but did not catch up to the levels achieved by their typically developing
peers.
© 2018 Elsevier Inc. All rights reserved.
1. Introduction
Children’s communicative capacity encompasses the ability to
speak and listen using the conventions of speech1 (consonants,
vowels, prosody) and language (vocabulary, grammar, sentence
structure, discourse) in a variety of contexts. During early childhood, most children learn to use the conventions of speech and
language; however, a significant proportion have speech, language
and communication needs in comparison to their peers (Law,
Boyle, Harris, Harkness, & Nye, 2000; McLeod & Harrison, 2009;
McLeod & McKinnon, 2007). These children require, and can benefit from, additional speech and language support to maximise their
academic and social potential. In many countries, speech and language disorder2 is recognised in national legislation for children
∗ Corresponding author at: Charles Sturt University, Panorama Ave, Bathurst,
NSW, Australia.
E-mail address: smcleod@csu.edu.au (S. McLeod).
1
Sign language is another mode of communication used instead of or in addition
to speech, but is not addressed in this paper.
2
Speech and language disorder is used in this paper as an umbrella term to
encompass terms such as developmental language disorder (previously called specific
https://doi.org/10.1016/j.ecresq.2018.07.004
0885-2006/© 2018 Elsevier Inc. All rights reserved.
with disabilities, and speech-language pathology intervention is
made available within educational settings (e.g., United Kingdom:
Bercow, 2018; Department for Health, 2008; McCartney, Boyle, &
Ellis, 2015; United States: Farquharson, Tambyraja, Logan, Justice,
& Schmitt, 2015; US Department of Education, 2002). In Australia,
however, despite national recognition of “the significant benefits to
both the individual and society of a strategy that prioritises early
intervention of speech and language disorders” (Commonwealth of
Australia, 2014, p. 109), children with speech and language disorder
are rarely mentioned in education, health, or disability legislation,
policy or reports (McLeod, Press, & Phelan, 2010), and the provision
of speech-language pathology intervention services is inconsistent
across state and local government jurisdictions (Speech Pathology
Australia, 2014).
Speech and language competence in the years before school
is an important prerequisite for later achievements in literacy.
Speech and language disorder and late talking have been linked
with poorer performance in phonological processing and reading
language impairment), speech sound disorder, communication disorder, and speech,
language and communication needs.
508
S. McLeod et al. / Early Childhood Research Quarterly 47 (2019) 507–517
(Anthony et al., 2011; Lewis, Freebairn, & Taylor, 2000; Rescorla,
2009) that extends to adolescence (Lewis et al., 2015). Longitudinal growth curve studies of children in early childhood identified
as having speech and language disorder show that these children
start school with underdeveloped reading skills, compared to their
peers with typical speech and language, and continue to achieve
lower levels of reading, despite having similar growth trajectories
to children with typical speech and language skills (Foster & Miller,
2007; Skibbe et al., 2008). Similarly, studies of children recruited
at school entry or Grade 1 and followed across the school grades
have shown significant differences in achievement but similar rates
of growth for typically developing children versus children with
speech and language disorder, in reading (Catts, Bridges, Little, &
Tomblin, 2008) and writing (Kim, Puranik, & Al Otaiba, 2015). Comparative studies of children with speech and language disorder also
exist, but have either not modelled the effects on growth trajectories in reading or other areas of literacy (Nathan, Stackhouse,
Goulandris, & Snowling, 2004a) or, if they do, have not contrasted
growth patterns with those of typically developing children (Law,
Tomblin, & Zhang, 2008; Wei, Blackorby, & Schiller, 2011). Of note,
two of the latter studies showed that children with speech and
language disorder had poorer outcomes than children with “specific speech difficulties” (Nathan et al., 2004a, p. 377) and better
outcomes that children with multiple disabilities or intellectual
impairment (Wei et al., 2011).
Researchers have also linked speech and language competence in early childhood with children’s later numeracy skills
(Dockrell & Lindsay, 1998; Nathan, Stackhouse, Goulandris, &
Snowling, 2004b). Children with speech and language disorder
have been found to have difficulty with symbolic representation,
verbal counting, recall of numbers, and mathematical calculations
(Arvedson, 2002; Fazio, 1996, 1999; Harrison, McLeod, Berthelsen,
& Walker, 2009; Hecht, Torgesen, Wagner, & Rashotte, 2001). These
findings are not surprising, since the acquisition and application
of mathematical knowledge require language (Chen & ChaloubDeville, 2016). For example, Purpura, Hume, Sims, and Lonigan
(2011), reported a predictive relationship between young children’s vocabulary and print knowledge and scores on numeracy
assessments.
The present study extends these two areas of research by applying longitudinal analytical techniques to a large sample of children
to examine children’s speech and language status at school entry
as a predictor of subsequent literacy and numeracy achievement.
The study not only builds on previous evidence that children with
speech and language disorder are at risk of difficulties with literacy and numeracy, but also examines the question of whether the
shape of children’s growth trajectories differs by speech and language status. Whilst some studies have reported no difference in
rates of growth (Catts et al., 2008; Kim et al., 2015), Skibbe et al.
(2008) addressed the specific question of whether relative reading
trajectories for children with typical language and with “language
difficulties” (p. 475) were best represented by a cumulative trajectory, where the gap between the two groups widens over time, or a
compensatory trajectory where the differences between the groups
decreases over time. Their analysis of reading trajectories from preK to Grade 5 showed a higher rate of growth over the school years
for the children with language difficulties than for children with
typical language, “suggesting some compensation for the reading
disadvantages that were seen at 54 months” (Skibbe et al., 2008,
p. 482). Nonetheless, they noted that these differences were difficult to see in the parallel trajectories of the two groups, and that
children with language difficulties were unlikely to ever catch up
to their typically developing peers.
A further contribution of the present study is its use of national
test results to compare the achievement of typically developing children versus children identified with speech and language
concern. As such, the study is highly relevant to recent interest in the results of international comparative testing in primary
and secondary schools (Meyer & Benavot, 2013; National Center
for Educational Statistics, n.d.). It extends the small number of
studies that have examined national test results in relation to
children’s “diverse needs” (Carmichael, MacDonald, & McFarlandPiazza, 2014, p. 640) or “developmental speech disorder” (Nathan
et al., 2004b, p. 173). Australia, along with United States, United
Kingdom, and a number of other countries has instituted national
testing on a regular basis, with student performance results being
used by governments, schools, and parents for accountability of
schools and teachers, benchmarking, identification of areas of need,
and funding support. Australia’s National Assessment Program –
Literacy and Numeracy (NAPLAN) assesses all children in Grades 3
and 5 of primary school and Grades 7 and 9 of secondary school
on numeracy abilities and four aspects of literacy: reading, writing, spelling, and grammar/punctuation (Australian Curriculum,
Assessment and Reporting Authority, ACARA, 2014).
In the present study, we access children’s NAPLAN test scores
through linked data in the Longitudinal Study of Australian Children (LSAC), which has allowed comparisons of the educational
attainments of the study children with national minimal standards
for achievement at each Grade level in the wider Australian population of school students. Furthermore, our analyses of NAPLAN
outcomes within a large population sample (LSAC) allow the
effects of important child and demographic factors on outcomes
to be statistically controlled. Previous research has shown that
speech and language disorder, and children’s literacy and numeracy
achievement are associated with child factors including child sex,
temperament, hearing, and motor coordination, and family factors
including parental education level, family socio-economic status,
family history of speech and language difficulties, maternal wellbeing, parents’ language background other than English, and sibling
status (Harrison & McLeod, 2010; Letts, Edwards, Sinka, Schaefer,
& Gibbons, 2013; Taylor, Christensen, Lawrence, Mitrou, & Zubrick,
2014; Wren, Miller, Peters, Emond, & Roulstone, 2016). Our study
design also acknowledges that speech and language concern can
present comorbidly with developmental disabilities, such as Down
Syndrome and autism spectrum disorder (McCormack, Harrison,
McLeod, & McAllister, 2011), and that in Australia, Aboriginal and
Torres Strait Islander children have a greater risk of hearing loss
(Aithal, Yonovitz, & Aithal, 2008).
A further consideration when researching an Australian national
sample is the unavailability of school-based identification and
referral of children with speech and language disorder, as occurs
systematically in the United States (e.g., Lewis et al., 2015) and the
United Kingdom (e.g., Law et al., 2008). For this reason, we also
examined parent-reported access to speech-language pathology
services across the five data-points of the study (preschool, Grade
1, Grade 3, Grade 5, and Grade 7).
1.1. Aims
The aim of this research was to examine literacy and numeracy
achievement at three time-points (Grades 3, 5, and 7) for children identified in early childhood as either typically developing or
with speech and language concern (SLC), based on parent report.
By using a large two-cohort, nationally representative study and
linked data to national test scores, the study sought to compare
children’s achievement with national standards for literacy and
numeracy attainment at Grade 3 for both cohorts, and Grades 5
and 7 for the older cohort. A further aim was to examine growth
trajectories of literacy and numeracy achievement from Grades
3 to 7 for the older cohort. Based on the results of earlier largecohort, longitudinal studies (Catts et al., 2008; Skibbe et al., 2008),
we hypothesised that children identified with SLC in early child-
S. McLeod et al. / Early Childhood Research Quarterly 47 (2019) 507–517
hood will (1) achieve lower scores on literacy and numeracy tests
than their typically developing peers at all assessment points, but
(2) show similar growth trajectories for literacy and numeracy to
their typically developing peers.
2. Method
2.1. Participants
Participants were from LSAC a two-cohort, nationally representative, longitudinal study of children’s development funded by the
Australian Government Department of Social Services to examine
the lives of approximately 10000 Australian children (Australian
Institute of Family Studies, AIFS, 2009). The sampling frame for
LSAC was the Medicare Australia (public health) database, which
is the most comprehensive database of the Australian population.
A two-stage clustered design was used: postcodes (zipcodes) were
randomly selected; children were randomly selected within each
postcode. Stratification was implemented to ensure the number of
selected children was proportionate to the total number of children
within each state or territory. Data collection started in 2004 when
children in the (K)indergarten cohort (N = 4983) were 4.5–5 years
old and children in the (B)irth cohort (N = 5107) were 6–12 months
old. Subsequent waves of data have been collected every two years.
At the time of writing this paper, five waves of data were available
for analyses: the K cohort to age 12–13 years, the B cohort to age
8–9 years.
Participants in the current study were all children in the K and
B cohorts of LSAC whose parents provided responses to questions
about the child’s speech and language in two waves of data collection: when the child was age 4–5 years (wave 1 for the K cohort;
wave 3 for the B cohort) and age 6–7 years (wave 2 for the K cohort;
wave 4 for the B cohort). The LSAC waves of data collection, and
data available for the present study and sample are summarised
in Table 1. Note that the smaller sample size for K cohort Grade
3 (wave 3) was due to the large number of children who entered
Grade 3 in 2007, the year before national testing (NAPLAN) was
introduced.
2.1.1. K(indergarten) cohort
A total of 4332 children of the original 4983 K cohort sample provided data for the present study; 2209 (51%) were boys and 2123
(49%) girls. The average age at recruitment (wave 1) was 4.17 years
(SD = .38). Compared to the included cases, children who were not
included because of missing data for speech and language at age 4–5
or 6–7 (n = 651) were more likely to come from families with lower
socio-economic position (SEP) (F = 122.13, p < .001), to be of Indigenous background (2 (1) = 10.37, p = .001), and to speak languages
other than English as their main language (2 (1) = 60.96, p < .001).
The patterns of missingness are typical of longitudinal attritions.
2.1.2. B(irth) cohort
Participants were 4073 children (of the original 5107) for
whom data were available at wave 3: 2104 (51.7%) were boys
and 1969 (48.3%) girls. The average age at wave 3 was 4.25 years
(SD = .43). Compared to the included cases, children who were missing (n = 1034) were more likely to come from families with lower
SEP (F = 98.16, p < .001), to be Indigenous (2 (1) = 99.59, p < .001),
and to speak languages other than English as their main language
(2 (1) = 22.33, p < .001).
2.2. Identification and coding of speech and language groups
Children’s speech and language status was identified from
parents’ responses to two of the ten questions in the Parent’s Evaluation of Developmental Status (PEDS) (Glascoe, 2000): “Do you
509
have any concerns about how your child talks and makes speech
sounds?” (yes, a little, no); and “Do you have any concerns about
how your child understands what you say to him?” (yes, a little,
no). The PEDS is reported to have good sensitivity and specificity
and concurrent validity (Coghlan, Kiing, & Wake, 2003; Glascoe,
1996), although not as high as some other measures (Limbos &
Joyce, 2011). A summary of four studies of PEDS indicated a range
of 74–79% for sensitivity and 70–80% for specificity for children
aged 0–8 years (Glascoe, 2003). The two selected PEDS questions
have been used as identifiers in previous papers addressing speech
and language of children within LSAC (Harrison & McLeod, 2010;
Harrison et al., 2009; McCormack et al., 2011; McLeod & Harrison,
2009). In other studies, these two PEDS questions have been shown
to align well with clinical diagnosis by a speech-language pathologist (Harrison, McLeod, McAllister, & McCormack, 2017; McLeod,
Harrison, McAllister, & McCormack, 2013).
2.2.1. Typical versus speech and language concern
Children in the Typical group consisted of children whose parents gave a rating of no to both of the PEDS questions at 4–5 years
and 6–7 years. The children in the SLC group consisted of children
whose parents gave a response of yes and/or a little to either of the
two PEDS questions at 4–5 years and/or 6–7 years. Our decision
to define the typical group as having no speech or language difficulties across two waves of data decreased the chance of including
children identified at age 4–5 years whose communication difficulties had resolved by age 6–7 years. It also reduced the chance
of parents misidentifying their 4- to 5-year-old children as typical (cf. Harrison et al., 2009). Thus, our definition of typical was
conservative (strict), potentially reducing differences between the
two groups since the SLC group was likely to include children with
a range of impairment from severe to mild, as well as resolved difficulties. For the K cohort, there were 2890 (66.7%) children in the
Typical group and 1442 (33.3%) children in the SLC group. For the B
cohort, there were 2681 (65.8%) children in the Typical group and
1392 (34.2%) children in the SLC group.
2.3. Access to speech-language pathology services
In Australia, children identified with SLC may or may not have
had access to speech-language pathology services in early childhood or during their schooling. To investigate this, we used parent
and teacher reports collected in each wave. The question asked of
parents was: “In the past 12 months, have you used speech pathology services for your child?” The question for teachers was: “What
extra services has this child received through your centre/school
since being in your group?” with an option being “speech therapy”.
2.4. Child outcomes: NAPLAN literacy and numeracy scores
The LSAC dataset includes linked data to NAPLAN results
for individual children whose parents provide consent. NAPLAN
results for all five subtests (reading, writing, language conventions
(spelling, grammar and punctuation), and numeracy) are reported
as scaled scores on a range from 0 to 1000. Child outcomes are
also reported in Bands, which represent increasing competency
for students in Grades 3 to 9 (ACARA, 2014). For each school test
Grade, six Bands are used to report student achievement. The second lowest of these six Bands represents the national minimum
standard expected of students’ literacy and numeracy at each year
(ACARA, 2014; Victorian Curriculum and Assessment Authority,
VCAA, 2009). Students who have not achieved the national minimum standard “are likely to require focused intervention or
specialised support to fully participate in schooling” (VCAA, 2009,
p. 6).
510
S. McLeod et al. / Early Childhood Research Quarterly 47 (2019) 507–517
Table 1
Participants from LSAC with valid data who were included in the present study.
Age/Grade
LSAC Kindergarten Cohort
LSAC Birth Cohort
Total number of
participantsa
Valid data for current study
Total number of
participantsa
Valid data for current study
0–1 years
–
–
Not applicable
2–3 years
–
–
4–5 years/ Preschool
Wave 1
N = 4,983
Wave 2
N = 4,464
Wave 3
N = 4,331
Wave 4
N = 4,169
Wave 5
N = 3,956
n = 4,332
n = 3,779
Wave 1
N = 5,107
Wave 2
N = 4,606
Wave 3
N = 4,386
Wave 4
N = 4,242
Wave 5
N = 4,085
–
–
n = 3,441
–
–
6–7 years/ Grade 1
8–9 years/ Grade 3 NAPLANc
c
10–11 years/ Grade 5 NAPLAN
12–13 years/ Grade 7 NAPLANc
n = 4,332
n = 2,958 b
Not applicable
n = 4,073
n = 4,073
n = 3,467
a
The total number of participants is from Commonwealth of Australia (2016). LSAC Annual statistical report 2015. Retrieved from http://www.growingupinaustralia.gov.au/
pubs/asr/2015/asr2015a.html#a1-4.
b
Data linkage figures were lower than for subsequent years because approximately one quarter of the cohort were in Grade 3 in 2007, the year before NAPLAN was introduced
to Australian schools.
c
Years that NAPLAN testing was undertaken.
– Data were not collected in LSAC for these ages/years.
Parent consent for data linkage was collected at wave 3 (2008),
the year that NAPLAN testing was introduced. In a detailed analysis and explanation of the LSAC NAPLAN linked data, Daraganova,
Edwards, and Sipthorp (2013) reported that parental consent was
less likely to be given for children who had lower scores on the
Peabody Picture Vocabulary Test, were from non-English speaking backgrounds, whose parents held certificate-level educational
attainment, and whose mothers were not working when the child
was 4- to 5-years old. Lack of consent accounted for about 5% of
missing data. Daraganova et al. also noted that for a small proportion of children (2%), matching was not possible based on the
information provided (child’s name and date of birth, school name,
and postcode).
NAPLAN status was reported for participating LSAC children as:
(1) completed all NAPLAN testing for their year group, (2) absent but
not for all NAPLAN tests, (3) absent for all NAPLAN tests, (4) exempt
from NAPLAN testing. The Australian government allows exemption from testing if students have a significant disability, speak a
language other than English, or have lived in Australia for less than
a year (ACARA, 2014). Preliminary analyses were conducted to test
for possible differences in children’s NAPLAN status, for the Typical
and SLC groups (see Table 2). For the K cohort, although the number of exempt children was small, children in the SLC group were
significantly more likely to be exempt from NAPLAN testing than
children in the Typical group: Grade 3, 2 (3) = 26.70, p < .001; Grade
5, 2 (3) = 30.58, p < .001; and Grade 7, 2 (3) = 27.58, p < .001. Similarly, for the B cohort, children in the SLC group were significantly
more likely to be exempted from NAPLAN testing than children in
the Typical group: Grade 3, 2 (3) = 68.35, p < .001.
In the following analyses, the actual and estimated marginal
means of NAPLAN scores are based on children who attended the
NAPLAN testing. Children who were exempt from NAPLAN testing
were excluded from the NAPLAN cross-sectional comparisons. Subgroup analyses showed that a majority of children in the SLC group
who were exempted were reported as having a condition/disability
for 6 and more months (≥68.8%), having difficulty learning (≥62.5%)
and for a small number, having hearing problems (≥6.3%).
McLeod, 2010; Letts et al., 2013; Taylor et al., 2014; Wren et al.,
2016) were extracted from the LSAC dataset. These were family socio-economic position (SEP), child sex, language background
other than English (LBOTE), Indigenous status, having a disability,
and having ongoing hearing problems, all of which were collected
via parent report at age 4–5 years.3 SEP is a composite variable
derived from parental education, occupational status, and household income (Blakemore, Strazdins, & Gibbons, 2009). Disability
was reported as a yes/no response to the following questions: “Does
the child have a medical condition or disability that has lasted for 6
months or more?” For these children who received a yes, follow-up
questions were asked about 16 possible conditions. Hearing problems was one of the named conditions. Preliminary analyses were
conducted for each of these six variables to assess the presence of
significant differences across the Typical and SLC groups. Results
presented in Supplemental Appendix A confirmed that children in
the SLC group were more likely to have a lower family SEP, to be
boys, to have a LBOTE, to be Indigenous, to have a disability, or
to have hearing problems. The pattern of results was similar for
both the K cohort and B cohorts, apart from having a LBOTE and
Indigenous status that were only significantly different for the K
cohort.
2.6. Data analysis
2.6.1. NAPLAN cross-sectional comparisons: K cohort Grades 3, 5,
7; B cohort Grade 3
To examine the group differences in children’s NAPLAN outcomes at each Grade level, a series of one-way between-group
Analysis of Covariance (ANCOVA) tests were conducted (5 analyses for each Grade), comparing the Typical and SLC groups. These
analyses controlled for child age and six covariates (SEP, sex,
LBOTE, Indigenous background, disability status, and hearing problems). ANCOVA was used to produce estimated marginal means of
NAPLAN outcomes for each of the groups. These are the mean scores
after statistically removing the effects of the confounding variables.
Using estimated marginal means provided a better test of whether
2.5. Child characteristics and demographic variables
Six variables identified as key factors affecting speech, language,
and communication in childhood (Aithal et al., 2008; Harrison &
3
Disability status was not available for K cohort at 4–5 years; the first available
data was at 6–7 years. Therefore, we used disability status at 6–7 years as a covariate
for the K cohort.
S. McLeod et al. / Early Childhood Research Quarterly 47 (2019) 507–517
511
Table 2
NAPLAN exemption status for participants in the (K)indergarten and (B)irth cohorts in Grades 3, 5 and 7 in the typical and speech and language concern (SLC) groups.
Cohort
K
Grade
Grade 3
Grade 5
Grade 7
B
Grade 3
1
Completed all
2
Absent but not
for all tests
3
Absent for all
4
Exempt
n
%
n
%
n
%
n
%
Typical
Speech and Language Concern
Typical
Speech and Language Concern
Typical
Speech and Language Concern
1945
903
2455
1176
2249
1025
96.9%
94.8%
96.4%
95.5%
96.0%
93.3%
39
24
58
24
62
34
2.0%
2.5%
2.3%
1.9%
2.6%
3.1%
19
10
32
14
25
19
1.0%
1.0%
1.3%
1.1%
1.1%
1.7%
2
16
2
18
7
21
0.1%
1.7%
0.1%
1.5%
0.3%
1.9%
Typical
Speech and Language Concern
2227
1065
96.4%
92.0%
56
36
2.4%
3.1%
18
11
0.8%
0.9%
8
46
0.3%
4.0%
Group
the observed differences in NAPLAN score were due to group differences. Effect size was estimated using partial eta square (p 2 ),
which was more appropriate to use in this analysis where control
variables were added to the design (Levine & Hullett, 2002). Partial
eta squared refers to the sum of squares effect divided by the sum
of squares effect plus the sum of squares error. An effect of .01 is
small, .06 is medium and .14 is large.
2.6.2. NAPLAN growth trajectories (K cohort)
Multilevel modelling using linear mixed model in SPSS (Heck,
Thomas, & Tabata, 2014) with restricted maximum likelihood
estimation (REML) was conducted to examine the trajectories of
children’s NAPLAN reading, writing, grammar, spelling, and numeracy over three time points (i.e., Grade 3, Grade 5, and Grade 7) for the
K cohort. In multilevel modelling, repeated measures of children’s
NAPLAN outcomes (i.e., Level 1) are nested within individuals (i.e.,
Level 2). Multilevel modelling can handle missing data and includes
all the cases with NAPLAN data available for at least one time point.
Initial analyses were conducted for the whole sample. We
inspected the shape of each trajectory over time visually, as a recommended initial step (Heck, Thomas, & Tabata, 2014). The shape
indicated an increasing trajectory but a deceleration in the rate of
change between Grade 5 and Grade 7. Therefore, we included both
a linear and a quadratic component as time-related within-subject
variables (i.e., Level 1). To avoid multicollinearity, we coded the
linear (−1, 0, 1) and quadratic (1, −2, 1) time-related variables to
be orthogonal to ensure stabilisation in model estimation (Heck
et al., 2014). We then estimated unconditional models without
predictors with each of the five NAPLAN tests (i.e., reading, writing, spelling, grammar, and numeracy) as the outcome variable and
examined both the fixed and random effects of the model. The equations for the unconditional model are provided in Supplemental
Appendix B. Note that for repeated measurements at three time
points, the random effects of quadratic slope coefficients across
individuals cannot be estimated and were fixed at zero. We also
investigated different covariance structures for Level 1 and Level 2
until a parsimonious and stable model was obtained.
Children’s trajectories for each of the five NAPLAN subtests are
shown in Supplemental Appendix C. The results show the unconditional models with diagonal covariance structure for Level 1 and
unstructured covariance structure for Level 2 for each of the five
NAPLAN outcomes. Children’s scores on all five NAPLAN subtests
increased over time as shown by the significant positive linear
growth rate but the growth slowed slightly between the later time
points, as evidenced by the significant negative quadratic growth
rate. In addition, there was significant variation across individuals
in the overall grand mean (Intercept) for all NAPLAN subtest scores.
The linear growth rate across individuals achieved significance for
only children’s reading and numeracy NAPLAN subtests. Therefore,
we fixed the variance of linear growth rate at zero in the follow-
ing conditional models for NAPLAN writing, grammar and spelling
subtests, with scaled identity covariance structure used for Level 2.
Next, we examined the effect of children’s speech and language
status on the trajectories by estimating conditional models including the predictor of Typical versus SLC groups, while controlling
for children’s age as well as other characteristics and family demographics (SEP, sex, LBOTE, Indigenous background, disability status,
and hearing problems). Predicted NAPLAN scores adjusted based on
the seven covariates were saved and used for graphing purposes.
For all analyses, to reduce the chance of Type I error due to the
number of testings, we set alpha at ≤.003 (two tailed) to determine the significance of group differences or parameter estimates
of predictors.
3. Results
3.1. Access to speech-language pathology services
Children’s access to speech-language pathology services is presented in Table 3 for the K and B cohorts at each wave of data
collection for children identified as typically developing (Typical)
and having speech and language concern (SLC). Findings indicated that a very small proportion of children in the Typical
group (0.1–3.4%) and a larger proportion of children in the SLC
group (2.1–32.4%) had accessed speech-language pathology services. Notably, the proportion for the SLC group was far below
majority, which is in keeping with the unsystematic provision of
speech-language pathology services across Australia. Of further
interest is the pattern of results over time, which showed a marked
reduction in number of children accessing speech-language pathology services from preschool through the early to middle years
of school. Figures for parent-reported access decreased for the K
cohort from 31.3% in Preschool, to 21.3% in Grade 1, 12.3% in Grade
3, and 5.5% in Grade 5. A similar pattern was reported for the B
cohort: 32.4% in Preschool, 23.2% in Grade 1, and 12.1% in Grade 3.
3.2. NAPLAN achievement relative to bands and national
minimum standards
NAPLAN Bands represent the increasing complexity of assessed
skills in literacy and numeracy for each year of testing (Grades 3–9).
Table 4 presents the unadjusted mean scores for each NAPLAN test
for children in the Typical group and the SLC group in relation to
the NAPLAN Band for each grade. The results show that, for each
grade, children in both groups achieved scored that surpassed the
national minimum standard.
Comparison of Typical vs SLC groups for K-cohort Grade 3 Bands
showed that the children in the SLC group scored one Band lower
than the Typical group on all five subtests. By Grades 5 and 7, however, children in the two groups achieved equal Band scores, apart
from writing where the SLC group were one band lower. Similarly,
512
S. McLeod et al. / Early Childhood Research Quarterly 47 (2019) 507–517
Table 3
Proportion of participants in the (K)indergarten and (B)irth cohorts of LSAC who accessed, and did not access, speech and language therapy s accessed, and did not access,
speech-language pathology services.
Cohort
K
B
School Grade
Age years
Preschool
4–5
Grade 1
Grade 3
Grade 5
6–7
8–9
10–11
Grade 7
12–13
Preschool
4–5
Grade 1
6–7
Grade 3
8–9
Informant
Typical
Speech and language concern
Valid data
Access
No access
Access
No access
N
Parent
Teacher
Parent
Parent
Parent
Teachera
Parent
Teacher
3.4%
1.1%
2.0%
1.6%
1.4%
4.7%
0.6%
0.1%
96.6%
98.9%
98.0%
98.4%
98.6%
95.3%
99.4%
99.9%
31.3%
14.3%
21.3%
12.3%
5.5%
13.2%
4.2%
2.1%
68.7%
85.7%
78.7%
87.7%
94.5%
86.8%
95.8%
97.9%
4172
3258
3432
4331
4161
420
3913
2801
Parent
Teacher
Parent
Teachera
Parent
Teacher
2.7%
2.4%
1.9%
4.0%
1.9%
0.5%
97.3%
97.6%
98.1%
96.0%
98.1%
99.5%
32.4%
17.2%
23.2%
2.8%
12.1%
4.5%
67.6%
82.8%
76.8%
97.2%
87.9%
95.5%
4073
3097
4069
565
3794
3253
a
The very low number of reports by teachers is an unexplained aberration in the data collection for wave 4. The figures are reported, but cannot be considered representative
of the whole sample.
Table 4
Comparison of the unadjusted NAPLAN scaled scores and bands for participants in the (K)indergarten and (B)irth cohorts of LSAC (typical versus speech and language concern
(SLC) groups) with the national minimum standard.a
National minimum standarda
Cohort
Subtest
Grade
Typical
K
Reading
Grade 3
Grade 5
Grade 7
Grade 3
Grade 5
Grade 7
Grade 3
Grade 5
Grade 7
Grade 3
Grade 5
Grade 7
Grade 3
Grade 5
Grade 7
Actual score (Mean)
435.18
518.03
567.32
439.60
504.42
545.39
427.90
502.00
559.91
444.12
527.05
569.99
428.32
508.38
562.44
Band
5
6
7
5
6
7
5
6
7
5
6
7
5
6
7
Actual score (Mean)
406.93
486.95
543.00
412.12
476.57
515.18
400.37
479.22
532.72
407.44
493.67
541.31
405.79
490.43
545.28
Band
4
6
7
4
5
6
4
6
7
4
6
7
4
6
7
Range of scores (Mean)
>270 and ≤322
>374 and ≤426
>426 and ≤478
>270 and ≤322
>374 and ≤426
>426 and ≤478
>270 and ≤322
>374 and ≤426
>426 and ≤478
>270 and ≤322
>374 and ≤426
>426 and ≤478
>270 and ≤322
>374 and ≤426
>426 and ≤478
Band
2
4
5
2
4
5
2
4
5
2
4
5
2
4
5
Grade 3
Grade 3
Grade 3
Grade 3
Grade 3
433.74
420.94
417.85
437.38
403.54
5
4
4
5
4
392.99
381.24
374.87
392.79
373.77
4
4
3
4
3
>270 and ≤322
>270 and ≤322
>270 and ≤322
>270 and ≤322
>270 and ≤322
2
2
2
2
2
Writing
Spelling
Grammar
Numeracy
B
a
Reading
Writing
Spelling
Grammar
Numeracy
Speech and language concern
Victorian Curriculum and Assessment Authority (2009, p. 6).
Grade 3 Band results for the B cohort were one Band lower for the
SLC group for all subtests except writing.
3.3. NAPLAN outcomes in Grades 3, 5, and 7
3.3.1. Cross-sectional comparisons of Typical versus SLC groups
Children’s NAPLAN scores were then compared for the Typical and SLC groups, after controlling for family SEP, child age,
sex, LBOTE, Indigenous status, disability (>6 months), and hearing problems. Results showed significant differences for each
NAPLAN subtest (K cohort: Fs (1) ≥ 19.24, p < .001; B cohort: Fs
(1) ≥ 35.16, p < .001), with p 2 values ranging from .01 to .02. Children in the SLC group achieved lower scores than children in
the Typical group on all NAPLAN subtests (see Table 5). These
results support our hypothesis that literacy and numeracy test
scores would be lower at all assessment points for children
identified in early childhood as having speech and language difficulties.
3.3.2. NAPLAN growth trajectory comparisons of children in the
Typical and SLC subgroups
Children’s comparative rates of growth in reading, writing,
spelling, grammar, and numeracy for the Typical and SLC groups are
presented in Table 6 and graphically displayed in Fig. 1. In keeping
with our hypothesised expectations, results showed that for each
NAPLAN subtest the shapes of the trajectories were similar (parallel) for both groups. There was an initial steep increase in children’s
NAPLAN test scores that reduced over time for both the Typical and
SLC groups. The rate of linear growth over time by group did not
differ (see Table 6 non-significant estimates of linear growth rate
by group for each subtest).
4. Discussion
The aim of this research was to examine achievement in literacy
and numeracy over Grades 3, 5, and 7 for children whose speech
and language status was identified at age 4–5 and 6–7 years. Our
approach was to conduct secondary data analysis of a two-cohort,
S. McLeod et al. / Early Childhood Research Quarterly 47 (2019) 507–517
513
Table 5
Comparison of NAPLAN estimated marginal mean scores for participants in the (K)indergarten and (B)irth cohorts of LSAC (typical versus speech and language concern (SLC)
groups).
Cohort
NAPLAN test
School year
(1) Typical
EMMa (SE)
(2) SLC
EMM (SE)
F values and p
K
Reading
Year 3
431.51
(1.84)
514.55
(1.50)
564.41
(1.35)
436.04
(1.61)
500.98
(1.31)
541.25
(1.41)
425.12
(1.69)
499.39
(1.31)
557.41
(1.35)
440.64
(1.88)
523.00
(1.59)
566.61
(1.48)
426.08
(1.55)
506.71
(1.36)
560.84
(1.42)
415.09
(2.70)
494.95
(2.22)
549.76
(2.03)
420.00
(2.36)
484.58
(1.93)
524.70
(2.12)
406.77
(2.48)
485.19
(1.94)
538.55
(2.03)
415.21
(2.77)
503.00
(2.35)
549.28
(2.22)
410.87
(2.28)
494.50
(2.01)
549.28
(2.15)
F = 24.19
p < .001
F = 51.20
p < .001
F = 34.66
p < .001
F = 30.28
p < .001
F = 47.28
p < .001
F = 40.71
p < .001
F = 35.86
p < .001
F = 35.19
p < .001
F = 57.39
p < .001
F = 55.20
p < .001
F = 47.74
p < .001
F = 40.53
p < .001
F = 29.18
p < .001
F = 24.12
p < .001
F = 19.24
p < .001
429.73
(2.39)
416.85
(1.99)
414.35
(2.17)
432.71
(2.43)
401.33
(2.14)
401.34
(3.45)
389.49
(2.87)
381.90
(3.13)
402.22
(3.51)
378.51
(3.08)
F = 43.40
p < .001
F = 58.18
p < .001
F = 68.93
p < .001
F = 48.49
p < .001
F = 35.16
p < .001
Year 5
Year 7
Writing
Year 3
Year 5
Year 7
Spelling
Year 3
Year 5
Year 7
Grammar
Year 3
Year 5
Year 7
Numeracy
Year 3
Year 5
Year 7
B
a
Reading
Year 3
Writing
Year 3
Spelling
Year 3
Grammar
Year 3
Numeracy
Year 3
Estimated Marginal Means (EMM) are calculated after controlling for family SEP, child age, sex, LBOTE, Indigenous status, disability (>6 months), and hearing problems.
nationally representative Australian study, generating a combined
sample of 8395 children tested in Grade 3 and 4332 children tested
in Grades 3, 5 and 7. The availability of linked data to nationally administered testing of literary and numeracy achievement
allowed the examination of three current and critical questions
about the progress of children with speech and language concern
(SLC) compared to their typically developing peers.
First, using comparative analyses of children’s NAPLAN test
scores with Australia’s national minimum standards for achievement in reading, writing, spelling, grammar, and numeracy at
Grades 3, 5, and 7, results showed that children identified as having
SLC achieved test scores that were above national minimum standards. Further, the difference in Band scores for the Typical and
SLC groups narrowed over time for all but one area, writing. Whilst
this is an encouraging pattern, suggesting children with SLC have
reduced the gap, it is important to note that analyses by Daraganova
et al. (2013) comparing Grades 3 and 5 NAPLAN scores for LSAC children with scores achieved by all Australian children tested in 2008
and 2010 showed that, on average, LSAC children achieved higher
scores. They concluded that the “LSAC NAPLAN sample cannot be
considered as a representative sample of Australian children in a
specific year level, even after accounting for attrition” (p. 37). It is
also important to note that the study sample of children with SLC
was slightly smaller than the original LSAC sample, because parents
of children with lower receptive vocabulary scores were less likely
to consent to data to be linked to the NAPLAN scores (Daraganova
et al., 2013), and were more likely to be excluded from NAPLAN
testing. Consequently, the findings reported at the Band level may
underestimate the true extent of literacy and numeracy difficulties
in children with SLC.
Second, we conducted cross-sectional and longitudinal analyses to test for group differences in children’s adjusted NAPLAN
scores at Grades 3, 5, and 7. Our results were consistent with the
results of previous longitudinal research (Catts et al., 2008, Foster
& Miller, 2007; Kim et al., 2015; Nathan et al., 2004b; Skibbe et al.,
2008): children with early childhood speech and language disorders achieved lower scores than children with typically developing
speech and language on all NAPLAN tests at all assessment points.
Unlike some of the previous work, however, our analyses controlled
for a wide range of child and family demographic and health factors
known to affect literacy and numeracy development to ensure that
the observed effects were due to early childhood speech and language status and not to other confounding variables. Furthermore,
we applied a conservative definition for the typically developing
group (no parent-reported speech and language concern at age 4–5
years and at age 6–7 years) so that it did not include children who
had resolved speech and language disorders between preschool
(age 4–5) and Grade 1 (age 6–7).
Our study differed from a number of previous studies in its use
of a broad indicator of speech and language concern (two questions
514
S. McLeod et al. / Early Childhood Research Quarterly 47 (2019) 507–517
Fig. 1. Adjusted NAPLAN scaled scores over time for typically developing children (dark) and children with SLC (light) from the Kindergarten cohort.
completed by parents on the Parent’s Evaluation of Developmental Status (PEDS) home interview), rather than specialised testing,
such as the Preschool Language Scale used in Skibbe et al. (2008),
literacy assessment tests used in Foster and Miller (2007), or other
test batteries (Catts et al., 2008). It may not be all that different,
however, from studies that utilise school-based samples such as
Nathan et al. (2004b) who studied children referred by speech and
language therapists or “identified through a nursery” (p. 175) and
Kim et al. (2015) who studied a “student population at risk for
struggling to read” (p. 597). As pointed out in the introduction, Australian children attending early childhood programs and school do
not have routine access to speech-language pathology services or
testing. Thus, it was necessary to rely on the parent-report measure
included in LSAC. This choice is justified, however, by the results of
a comparative study of parent-reported SLC using the PEDS and
clinical diagnosis by speech-language pathologists (SLPs) using the
Diagnostic Evaluation of Articulation and Phonology (DEAP) which
showed an 86–90% agreement between parents and SLP identification of speech sound disorder (Harrison et al., 2017).
Third, we utilised multilevel modelling techniques to produce
and analyse trajectories for NAPLAN reading, writing, spelling,
grammar, and numeracy across Grades 3, 5, and 7. The results
were consistent with previous research, identifying an initial rapid
increase in test score growth rates from Grade 3 to 5, and a declining rate of growth from Grade 5 to 7. This pattern of higher followed
by lower rates of growth in language and literacy development has
been previously reported in other large cohort studies including
children with and without language and literacy difficulties (ECLSK: Catts et al., 2008; NICHD ECCRN: Skibbe et al., 2008), as well as
in smaller studies of children with speech and language disorder
(Law et al., 2008).
Comparison of the trajectories for the Typical and SLC groups
confirmed our expectation that children in the SLC group would
achieve similar rates of growth to their typically developing peers.
For each of the NAPLAN tests, graphed results for the two groups
S. McLeod et al. / Early Childhood Research Quarterly 47 (2019) 507–517
515
Table 6
Results of multi-level growth model comparison (typical versus speech and language concern, SLC groups) on the trajectories of NAPLAN outcomes.
Model parameter
Estimate (95% confidence interval)
SE
df
t
Reading (N = 3886)
Fixed effects
Intercept (grand mean)
Linear growth rate
Quadratic growth rate
Group (Typical vs SLC)
Linear growth rate * Group
482.43 (474.89, 489.96)
66.37 (62.29, 70.44)
−5.48 (-6.04, −4.92)
16.78 (12.16, 21.41)
−1.04 (-3.53, 1.45)
3.85
2.08
0.29
2.36
1.27
3866.38
3227.18
3520.29
3854.31
3189.63
128.46***
31.93***
−19.16***
7.12***
−0.82
Writing (N = 3885)
Fixed effects
Intercept (grand mean)
Linear growth rate
Quadratic growth rate
Group (Typical vs SLC)
Linear growth rate * Group
477.30 (470.92, 483.68)
51.20 (46.36, 56.05)
−4.30 (-4.95, −3.64)
16.35 (12.44, 20.26)
0.70 (−2.27, 3.66)
3.26
2.47
0.33
2.00
1.51
3814.89
4188.47
4546.54
3795.63
4165.95
146.62***
20.72***
−12.85***
8.19***
0.46
Grammar (N = 3883)
Fixed effects
Intercept (grand mean)
Linear growth rate
Quadratic growth rate
Group (Typical vs SLC)
Linear growth rate * Group
489.43 (481.72, 497.14)
67.84 (63.34, 72.35)
−7.21 (-7.90, −6.51)
20.52 (15.79, 25.25)
−3.75 (-6.50, −1.00)
3.93
2.30
0.35
2.41
1.40
3971.00
3717.15
4303.99
3959.16
3697.94
124.48***
29.51***
−20.31***
8.51***
−2.67
Spelling (N = 3885)
Fixed effects
Intercept (grand mean)
Linear growth rate
Quadratic growth rate
Group (Typical vs SLC)
Linear growth rate * Group
496.14 (488.90, 503.38)
63.96 (60.87, 67.05)
−3.66 (-4.06, −3.26)
16.68 (12.23, 21.12)
1.13 (−0.76, 3.01)
3.70
1.58
0.20
2.27
0.96
3909.74
3733.73
3837.10
3897.15
3693.86
134.27***
40.59***
−18.06***
7.36***
1.17
Numeracy (N = 3887)
Fixed effects
Intercept (grand mean)
Linear growth rate
Quadratic growth rate
Group (Typical vs SLC)
Linear growth rate * Group
504.88 (497.91, 511.84)
75.71 (71.94, 79.49)
−4.86 (-5.37, −4.34)
12.81 (8.55, 17.08)
−.84 (-3.15, 1.46)
3.55
1.93
0.26
2.18
1.18
3871.72
3202.64
3467.62
3858.20
3148.70
142.20***
39.29***
−18.55***
5.89***
−0.72
Note. *** p < .001; alpha = .003. Language Group was coded as Typical (1) and Speech and Language Concern, SLC (0).
Results have controlled for family SEP, child age, sex, LBOTE, Indigenous status, disability (>6 months), and hearing problems. The effect of child characteristics and demographic
variables are not included in the tables for clarity and brevity. Children who were girls, had higher SEP, spoke English as the main language, were non-Indigenous, and had no
disability achieved higher overall mean scores for NAPLAN writing, grammar, spelling and numeracy. Higher NAPLAN reading scores were predicted by being a girl, having
higher SEP, being non-Indigenous and having no disability. In addition, having no hearing problems was linked to a higher NAPLAN writing grand mean. Girls had a higher
growth rate in NAPLAN writing but a lower growth rate in numeracy over time compared to boys. Children who had higher SEP and spoke English as the main language also
had a higher numeracy growth rate. Children who were older at the beginning of the study showed lower growth rate in reading and grammar over time.
were parallel (Fig. 1), and statistical tests showed no differences in
linear growth by group. The findings are similar to growth patterns
in reading achievement reported by Catts et al. (2008) who reported
non-significant differences in the slopes of the groups across a similar but wider age span (Grades 2, 4, 8 and 10) for children with
language impairments and typical language. On the other hand,
the results differ somewhat from growth trajectories for reading
skills reported by Skibbe et al. (2008) across a younger age span
(Pre-K, Grades 1, 2, 5) for children with and without language difficulties. In the Skibbe et al. study, a slightly higher rate of growth
was observed for the group with language difficulties, as evidenced
by a gradual decrease in the size of the group differences as children
progressed through school. A similar decrease was not observed for
the present study. Taken together, the findings from these three
large-cohort longitudinal studies suggest that questions relating to
narrowing the gap for children affected by speech and language
disorder remain open for further study.
Nonetheless, it is clear that the results of this Australian study
provide strong support for the recommendations of Skibbe et al.
(2008) and Catts et al. (2008) that children with SLC be identified
as early as possible and referred for specialist support in speech,
language, literacy, and numeracy. This is a particular concern in
Australia, as demonstrated by the present findings that the majority
of children with SLC had not accessed speech-language pathology
services in early childhood or during the school years. This result,
along with the consistent pattern of lower achievement in literacy and numeracy test scores at Grades 3, 5, and 7, provides further
evidence that children with speech and language concern need specialist assessment and intervention in early childhood to “boost
children’s early skill levels” (Skibbe et al., 2008, p. 484).
4.1. Limitations
Despite the strengths of the LSAC design, which included population data linked to NAPLAN, and the robustness of the findings
(consistent over two cohorts of more than 4000 children in each
cohort), there are limitations that need to be considered. Firstly, the
size and scope of the LSAC restricted the use of specialised testing of
children’s speech, which meant that SLC was identified via parent
report. It is possible that fewer children may have been identified
as having a SLC if identification were by clinical testing. However, in
this case, the results would likely have resulted in a greater difference between the two groups of children on NAPLAN outcomes. We
also note that the effectiveness of parent report has been demonstrated in other studies examining the correspondence between
516
S. McLeod et al. / Early Childhood Research Quarterly 47 (2019) 507–517
parent report and clinical testing for speech and language disorder
(e.g., Harrison et al., 2017; McLeod et al., 2013).
Secondly, although the LSAC samples were originally identified
using nationally representative sampling techniques based on Australian census data, subsequent analyses have shown that LSAC is
not nationally representative of NAPLAN test scores. On average,
LSAC children achieved higher scores than the wider population of
Australian children (Daraganova et al., 2013).
Thirdly, whilst the information gathered from parents and
teachers at each wave of the LSAC provided data on children’s
access to speech-language pathology services, it was not possible
to test this as a moderating variable in our analysis of NAPLAN
outcomes. Australia does not have a systematic system for identification and provision of support for children with speech and
language disorder, as occurs in the US and the UK. Whilst the low
levels of access to speech-language pathology services reported by
parents and teachers in the present study were consistent with
other Australian research studies (McAllister, McCormack, McLeod,
& Harrison, 2011; McLeod et al., 2013; Ruggero, McCabe, Ballard,
& Munro, 2012), these reports were not sufficiently robust to be
included as a predictor of child outcomes.
4.2. Conclusion
Linked data to national test scores for reading, writing, spelling,
grammar, and numeracy were examined cross-sectionally and longitudinally in a two-cohort population sample of 8395 children at
Grade 3 and 4332 children at Grades 3, 5, and 7. Children identified
with SLC in early childhood scored significantly lower than typically
developing children on NAPLAN tests of literacy and numeracy for
Grades 3, 5, and 7. Although test results for children in the SLC
group improved at the same rate of growth as in the Typical group,
children whose parents were concerned about their speech and
language skills in early childhood did not catch up to the levels of
literacy and numeracy achieved by their typically developing peers.
Most of the children with SLC had not accessed speech-language
pathology services. Early identification and access to specialist services are needed to boost outcomes in literacy and numeracy skills
in early childhood and throughout school.
Acknowledgment
This research was supported by a grant from Speech Pathology
Australia.
Appendices A, B and C Supplementary data
Supplementary data associated with this article can be found,
in the online version, at https://doi.org/10.1016/j.ecresq.2018.07.
004.
References
Aithal, S., Yonovitz, A., & Aithal, V. (2008). Perceptual consequences of conductive
hearing loss: speech perception in Indigenous students learning English as a
‘school’ language. Australian and New Zealand Journal of Audiology, 30, 1–18.
http://dx.doi.org/10.1375/audi.30.1.1
Anthony, J. L., Aghara, R. G., Dunkelberger, M. J., Anthony, T. I., Williams, J. M., &
Zhang, Z. (2011). What factors place children with speech sound disorder at
risk for reading problems? American Journal of Speech-Language Pathology,
20(2), 146–160. http://dx.doi.org/10.1044/1058-0360(2011/10-0053)
Arvedson, P. J. (2002). Young children with specific language impairment and their
numerical cognition. Journal of Speech, Language, and Hearing Research, 45(5),
970–982. http://dx.doi.org/10.1044/1092-4388(2002/079)
Australian Curriculum, Assessment and Reporting Authority [ACARA] (2014).
NAPLAN achievement in reading, persuasive writing, language conventions and
numeracy: National report for 2014. Retrieved from http://www.nap.edu.au.
Australian Institute of Family Studies [AIFS] (2009). Growing up in Australia: The
Longitudinal Study of Australian Children. Retrieved from
http://www.aifs.gov.au/growingup/.
Bercow, J. (2018). Bercow: Ten years on. An independent review of provision for
children and young people with speech, language and communication needs in
England. Retrieved from. www.bercow10yearson.com
Blakemore, T., Strazdins, L., & Gibbings, J. (2009). Measuring family socio-economic
position. Australian Social Policy Journal, 8, 121–168.
Carmichael, C., MacDonald, A., & McFarland-Piazza, L. (2014). Predictors of
numeracy performance in national testing programs: Insights from the
Longitudinal Study of Australian Children. British Educational Research Journal,
40(4), 637–659. http://dx.doi.org/10.1002/berj.3104
Catts, H. W., Bridges, M. S., Little, T. D., & Tomblin, J. B. (2008). Reading
achievement growth in children with language impairments. Journal of Speech,
Language, and Hearing Research, 51, 1569–1579. http://dx.doi.org/10.1044/
1092-4388(2008/07-0259)
Chen, F., & Chaloub-Deville, M. (2016). Differential and long-term of language
impact on math. Language Testing, 33, 577–605. http://dx.doi.org/10.1177/
0265532215594641
Coghlan, D., Kiing, J. S. H., & Wake, M. (2003). Parents’ Evaluation of Developmental
Status in the Australian day-care setting: Developmental concerns of parents
and carers. Journal of Paediatrics and Child Health, 39(1), 49–54. http://dx.doi.
org/10.1046/j.1440-1754.2003.00084.x
Commonwealth of Australia. (2014). Senate Community Affairs References
Committee: Prevalence of different types of speech, language and communication
disorders and speech pathology services in Australia. Canberra, Australia: Author.
Daraganova, G., Edwards, B., & Sipthorp, M. (2013). Using National Assessment
Program-Literacy and Numeracy (NAPLAN) data in the Longitudinal Study of
Australian Children (LSAC): LSAC Technical Paper No. 8. Canberra, Australia:
Commonwealth of Australia.
Department for Health. (2008). Better communication: An action plan to improve
services for children and young people with speech, language and communication
needs. Nottingham, UK: DCSF.
Dockrell, J., & Lindsay, G. (1998). The ways in which speech and language
difficulties impact on children’s access to the curriculum. Child Language
Teaching and Therapy, 14(2), 117–133. http://dx.doi.org/10.1191/
026565998672704149
Farquharson, K., Tambyraja, S. R., Logan, J., Justice, L. M., & Schmitt, M. B. (2015).
Using hierarchical linear modeling to examine how individual SLPs
differentially contribute to children’s language and literacy gains in public
schools. American Journal of Speech-Language Pathology, 24, 504–516. http://dx.
doi.org/10.1044/2015 AJSLP-14-0055
Fazio, B. B. (1996). Mathematical abilities of children with specific language
impairment: A 2-year follow-up. Journal of Speech, Language, and Hearing
Research, 39(4), 839–849. http://dx.doi.org/10.1044/jshr.3904.839
Fazio, B. B. (1999). Arithmetic calculation, short-term memory, and language
performance in children with specific language impairment: A 5-year
follow-up. Journal of Speech, Language, and Hearing Research, 42(2), 420–431.
http://dx.doi.org/10.1044/jslhr.4202.420
Foster, W. A., & Miller, M. (2007). Development of the literacy achievement gap: A
longitudinal study of kindergarten through third grade. Language, Speech, and
Hearing Services in Schools, 38, 173–181. http://dx.doi.org/10.1044/01611461(2007/018)
Glascoe, F. P. (1996). The importance of discussing parents’ concerns about
development. Ambulatory Child Health, 2, 349–356. http://dx.doi.org/10.1001/
archpedi.1989.02150200115029
Glascoe, F. P. (2000). Parents Evaluation of Developmental Status: Authorized
Australian version. Parkville, Australia: Centre for Community Child Health.
Glascoe, F. P. (2003). Parents’ Evaluation of Developmental Status: How well do
parents’ concerns identify children with behavioral and emotional problems?
Clinical Pediatrics, 42(2), 133–138.
Harrison, L. J., & McLeod, S. (2010). Risk and protective factors associated with
speech and language impairment in a nationally representative sample of 4- to
5-year-old children. Journal of Speech, Language, and Hearing Research, 53,
508–529. http://dx.doi.org/10.1044/1092-4388(2009/08-0086)
Harrison, L. J., McLeod, S., Berthelsen, D., & Walker, S. (2009). Literacy, numeracy
and learning in school-aged children identified as having speech and language
impairment in early childhood. International Journal of Speech-Language
Pathology, 11, 392–403. http://dx.doi.org/10.1080/17549500903093749
Harrison, L. J., McLeod, S., McAllister, L., & McCormack, J. (2017). Speech sound
disorder in preschool children: Correspondence between clinical diagnosis and
parent and teacher report. Australian Journal of Learning Difficulties, 22, 35–48.
http://dx.doi.org/10.1080/19404158.2017.1289964
Hecht, S. A., Torgesen, J. K., Wagner, R. K., & Rashotte, C. A. (2001). The relations
between phonological processing abilities and emerging individual differences
in mathematical computational skills: A longitudinal study from second to
fifth grades. Journal of Experimental Child Psychology, 79, 192–227. http://dx.
doi.org/10.1006/jecp.2000.2586
Heck, R. H., Thomas, S. L., & Tabata, L. N. (2014). Multilevel and longitudinal
modelling with IBM SPSS. New York, NY: Routledge.
Kim, Y.-S., Puranik, C., & Otaiba, S. A. (2015). Developmental trajectories of writing
skills in first grade: Examining the effects of SES and language and/or speech
impairments. The Elementary School Journal, 115(4), 593–613. http://dx.doi.org/
10.1086/681971
Law, J., Boyle, J., Harris, F., Harkness, A., & Nye, C. (2000). Prevalence and natural
history of primary speech and language delay: Findings from a systematic
S. McLeod et al. / Early Childhood Research Quarterly 47 (2019) 507–517
review of the literature. International Journal of Language and Communication
Disorders, 35(2), 165–188. http://dx.doi.org/10.1080/136828200247133
Law, B., Tomblin, J. B., & Zhang, X. (2008). Characterizing the growth trajectories of
language-impaired children between 7 and 11 years of age. Journal of Speech,
Language, and Hearing Research, 51, 739–749. http://dx.doi.org/10.1044/10924388(2008/052)
Letts, C., Edwards, S., Sinka, I., Schaefer, B., & Gibbons, W. (2013). Socio-economic
status and language acquisition: Children’s performance on the new Reynell
Developmental Language Scales. International Journal of Language and
Communication Disorders, 48(2), 131–143. http://dx.doi.org/10.1111/14606984.1200
Levine, T. R., & Hullett, C. R. (2002). Eta squared, partial eta squared and the
misreporting of effect size in communication research. Human Communication
Research, 28, 612–625. http://dx.doi.org/10.1111/j.1468-2958.2002.tb00828.x
Lewis, B. A., Freebairn, L. A., & Taylor, H. G. (2000). Academic outcomes in children
with histories of speech sound disorders. Journal of Communication Disorders,
33, 11–30. http://dx.doi.org/10.1016/S0021-9924(99)00023-4
Lewis, B. A., Freebairn, L., Tag, J., Ciesla, A. A., Iyengar, S. K., Stein, C. M., & Taylor, H.
G. (2015). Adolescent outcomes of children with early speech sound disorders
with and without language impairment. American Journal of Speech-Language
Pathology, 24(2), 150–163. http://dx.doi.org/10.1044/2014 AJSLP-14-0075
Limbos, M. M., & Joyce, D. P. (2011). Comparison of the ASQ and PEDS in screening
for developmental delay in children presenting for primary care. Journal of
Developmental and Behavioral Pediatrics, 32(7), 499–511. http://dx.doi.org/10.
1097/DBP.0b013e31822552e9
McAllister, L., McCormack, J., McLeod, S., & Harrison, L. J. (2011). Expectations and
experiences of accessing and participating in services for childhood speech
impairment. International Journal of Speech-Language Pathology, 13(3),
251–267. http://dx.doi.org/10.3109/17549507.2011.535565
McCartney, E., Boyle, J., & Ellis, S. (2015). Developing a universal reading
comprehension intervention for mainstream primary schools within areas of
social deprivation for children with and without language-learning
impairment: A feasibility study. International Journal of Language and
Communication Disorders, 50(1), 129–135. http://dx.doi.org/10.1111/14606984.12124
McCormack, J., Harrison, L. J., McLeod, S., & McAllister, L. (2011). A nationally
representative study of the association between communication impairment
at 4–5 years and children’s life activities at 7–9 years. Journal of Speech,
Language, and Hearing Research, 54, 1328–1348. http://dx.doi.org/10.1044/
1092-4388(2011/10-0155)
McLeod, S., & Harrison, L. J. (2009). Epidemiology of speech and language
impairment in a nationally representative sample of 4- to 5-year-old children.
Journal of Speech, Language, and Hearing Research, 52(5), 1213–1229. http://dx.
doi.org/10.1044/1092-4388(2009/08-0085)
McLeod, S., & McKinnon, D. H. (2007). The prevalence of communication disorders
compared with other learning needs in 14,500 primary and secondary school
students. International Journal of Language and Communication Disorders,
42(S1), 37–59. http://dx.doi.org/10.1080/13682820601173262
McLeod, S., Harrison, L. J., McAllister, L., & McCormack, J. (2013). Speech sound
disorders in a community study of preschool children. American Journal of
Speech-Language Pathology, 22(3), 503–522. http://dx.doi.org/10.1044/10580360(2012/11-0123)
McLeod, S., Press, F., & Phelan, C. (2010). The (in)visibility of children with
communication impairment in Australian health, education, and disability
legislation and policies. Asia Pacific Journal of Speech, Language, and Hearing,
13(1), 67–75. http://dx.doi.org/10.1179/136132810805335173
517
Meyer, H.-D., & Benavot, A. (2013). PISA, power and policy: The emergence of global
educational governance. Oxford, UK: Symposium Books.
Nathan, L., Stackhouse, J., Goulandris, N., & Snowling, M. J. (2004a). The
development of early literacy skills among children with speech difficulties: A
test of the critical age hypothesis. Journal of Speech, Language, and Hearing
Research, 47(2), 377–391. http://dx.doi.org/10.1044/1092-4388(2004/031)
Nathan, L., Stackhouse, J., Goulandris, N., & Snowling, M. J. (2004b). Educational
consequences of developmental speech disorder: Key Stage 1 National
Curriculum assessment results in English and mathematics. British Journal of
Educational Psychology, 74, 173–186. http://dx.doi.org/10.1348/
000709904773839824
National Center for Educational Statistics (n.d.). Trends in international mathematics
and science study (TIMSS). Retrieved from https://nces.ed.gov/timss/.
Purpura, D. J., Hume, L. E., Sims, D. M., & Lonigan, C. J. (2011). Early literacy and
early numeracy: The value of including early literacy skills in the prediction of
numeracy development. Journal of Experimental Child Psychology, 110(4),
647–658. http://dx.doi.org/10.1016/j.jecp.2011.07.004
Rescorla, L. (2009). Age 17 language and reading outcomes in late-talking toddlers:
Support for a dimensional perspective on language delay. Journal of Speech,
Language, and Hearing Research, 52(1), 16–30. http://dx.doi.org/10.1044/10924388(2008/07-0171)
Ruggero, L., McCabe, P., Ballard, K. J., & Munro, N. (2012). Paediatric
speech-language pathology service delivery: An exploratory survey of
Australian parents. International Journal of Speech-Language Pathology, 14(4),
338–350. http://dx.doi.org/10.3109/17549507.2011.650213
Skibbe, L. E., Grimm, K. J., Stanton-Chapman, T. L., Justice, L. M., Pence, K. L., &
Bowles, R. P. (2008). Reading trajectories of children with language difficulties
from preschool through fifth grade. Language, Speech, and Hearing Services in
Schools, 39, 475–486. http://dx.doi.org/10.1044/0161-1461(2008/07-0016)
Speech Pathology Australia. (2014). Submission to the inquiry into the prevalence of
different types of speech, language and communication disorders and speech
pathology services in Australia. Senate Community Affairs References
Committee. Retrieved from. http://www.aph.gov.au/Parliamentary Business/
Committees/Senate/Community Affairs/Speech Pathology/Report
Taylor, C. L., Christensen, D., Lawrence, D., Mitrou, F., & Zubrick, S. R. (2014). Risk
factors for children’s receptive vocabulary development from four to eight
years in the Longitudinal Study of Australian children. PLOS ONE, 8(9) http://
dx.doi.org/10.1371/journal.pone.0073046 [e73046]
US Department of Education. (2002). To assure the free appropriate public education
of all children with disabilities (Individuals with Disabilities Education Act, Section
618) Twenty-fourth annual report to Congress on the implementation of the
Individuals with Disabilities Education Act, 2002. Rockville, MD: US Department
of Education.
Victorian Curriculum and Assessment Authority [VCAA]. (2009). National
Assessment Program Literacy and Numeracy: NAPLAN 2009 reporting guide. Year
3, Year 5, Year 7 and Year 9. Melbourne, Australia: Author.
Wei, X., Blackorby, J., & Schiller, E. (2011). Growth in reading achievement of
children with disabilities, ages 7 to 17. Exceptional Children, 78, 89–106. http://
dx.doi.org/10.1177/001440291107800106
Wren, Y., Miller, L. L., Peters, T. J., Emond, A., & Roulstone, S. (2016). Prevalence and
predictors of persistent speech sound disorder at eight years old: Findings
from a population cohort study. Journal of Speech, Language, and Hearing
Research, 59(4), 647–673. http://dx.doi.org/10.1044/2015 JSLHR-S-14-0282