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90 JCPSLP Volume 15, Number 2 2013 Journal of Clinical Practice in Speech-Language Pathology
et al., 2006), Spanish (Restrepo, 1998) and Chinese (Ooi &
Wong, 2012). For example, if testing a 5-year old child in
their dominant language and a mean length of utterance
(MLU) of 2 is observed, there is a high probability that the
child is having significant language difficulties. If, on the
other hand, that same sample with a MLU of 2 was
collected from a 2-year old who was observed to be
combining many words, we have data suggesting that
language skills are not significantly below age-level
expectations. In addition, summarising the child’s
production of discourse-level variables, such as production
of mazes, provides data on the child’s formulation skills
(Miller, Andriacchi et al., 2012). Such skills are important for
communicating effectively and may be less influenced by
the linguistic structure of the language. These
interpretations should be made with caution and only used
to complement additional data summarising performance in
L1 as part of a comprehensive assessment.
Even if resources to acquire a fully transcribed sample in
L1 are not available, the SLP can still work with a trained
interpreter to elicit and record a sample. The SLP can
also collect a sample from one of the child’s age-matched
siblings or peers for whom there is no concern of a
language to use as a frame of reference (Wyatt, 1998). The
SLP can guide a well-trained interpreter to compare the two
samples and judge if they are equivalent or if one seems
considerably more immature than the other. The SLP can
query regarding the child’s syntactic complexity (Did the
child produced well-formed and complete sentences/
utterances?), grammatical accuracy (Do you notice many
errors?), lexical complexity (Does the child use overly simple
vocabulary?), and discourse skills (Could you understand
the main points the child was presenting?).
Using LSA data within a dynamic
assessment in English
The SLP can more accurately interpret language ability in
the child’s non-dominant language by documenting how
effectively children learn new English skills through dynamic
assessment. When completing dynamic assessment, the
clinician collects a baseline assessment, such as a
language sample, and then provides intensive intervention
on a target language skill. After the intervention, a
subsequent assessment is completed to determine if there
were notable gains. Peña, Iglesias, and Lidz (2001) used
this test–teach–retest dynamic assessment procedure
when assessing CALD children on a norm-referenced
expressive vocabulary test. Peña et al. (2001) documented
that all CALD children performed significantly below national
norms on the norm-referenced test. After training the
children how to accurately complete expressive vocabulary
tasks, the CALD children without true impairments
performed significantly better (and on par with national
norms) while the children with true language impairments
continued to score poorly on the test. In a subsequent
study, Peña et al. (2006) documented that clinicians could
accurately identify CALD children with language impairment
using a narrative dynamic assessment protocol, where the
examiner documented children’s deficits in their narrative
productions, completed intensive interventions in those
areas, and determined if significant gains were made as a
result of the intervention. Given the power of dynamic
assessment for accurately identifying CALD children with
speech and language impairments, Speech Pathology
Australia (2009) recommends that dynamic assessments
are included in all assessments with CALD children.
To successfully implement a dynamic assessment with
a CALD child, the SLP will first need to identify a functional
and meaningful elicitation context, which will be the focus
of the intensive intervention. For example, if working with
toddlers and young preschool-age children, the SLP may
be most interested in conversational discourse, which is a
critical skill for success in preschool classrooms. With older
preschool-age children and young school-age children,
personal narrative discourse becomes a critical component
of the curriculum. With older school-age children, the
increased demands of the curriculum require more technical
language use, which can be addressed through expository
or persuasive discourse (ACARA, 2012).
After collecting baseline data, Miller, Gillam, and Peña
(2001) recommend identifying children’s relative strengths
and weaknesses by completing a comprehensive analysis
of the initial sample. For example, in an analysis of a
child’s narrative, the SLP may identify that a child has
relative strengths in the microlinguistic features of the
narrative (e.g., relatively long MLU and minimal semantic
and syntactic errors) but a relative weakness in narrative
organisation. The SLP may choose to provide direct
instruction on narrative organisation skills and collect an
additional narrative after enough time has passed to see the
effect of the intervention. If the child makes marked gains
in narrative organisation skills, this could indicate that the
child does not have a true language impairment. Rather, the
child may have had limited experience telling stories or did
not initially comprehend the expectations for the task. If the
child fails to make notable gains in narrative organisation
skills, despite intensive intervention and high examiner
effort, the child is more likely to have a true impairment. See
Peña et al. (2006) for a full review of dynamic assessment.
Miller et al. (2001) produced a packaged dynamic
assessment program focusing on narrative discourse
that assists examiners in collecting multiple samples
and provides ideas for intensive interventions. If the SLP
does not have access to these materials or if they are
inappropriate for the client, clinicians can feel comfortable
developing their own dynamic assessment protocols. We
recommend that the elicitation contexts and interventions
are meaningful to the child and that the interventions are
clearly focused on the area(s) of deficit and implemented
with a high enough of a dosage so that the examiner may
expect a change in performance. When collecting follow-
up language samples, the elicitation procedures should be
consistent to ensure any gains observed are due to a true
improvement in performance.
Technological advances to assist
with language sampling
Technological advances have made the process of
recording, transcribing, and analysing language samples
more efficient and more accurate. SLPs may record their
samples with an inexpensive digital audio recorder, which
can then be downloaded to their computers. Most
operating systems come with audio players preinstalled, or
the SLP may use one of the many freely available audio
players (e.g., . It is
recommended that clinicians use language analysis
software when transcribing and analysing their samples. In
his study of 256 students from America and Australia, Long
(2001) identified that the use of software to analyse
language samples was significantly more accurate and
significantly quicker than completing analyses by hand.
Popular software options include the Systematic Analysis of