%0 Online Multimedia %A Heilmann, John %A Tucci, Alexander %A Plante, Elana %A Miller, Jon %D 2020 %T Assessing functional language (Heilmann et al., 2020) %U https://asha.figshare.com/articles/media/Assessing_functional_language_Heilmann_et_al_2020_/12456719 %R 10.23641/asha.12456719.v1 %2 https://asha.figshare.com/ndownloader/files/23038925 %2 https://asha.figshare.com/ndownloader/files/23038985 %2 https://asha.figshare.com/ndownloader/files/23039045 %2 https://asha.figshare.com/ndownloader/files/23039051 %2 https://asha.figshare.com/ndownloader/files/23039087 %K clinical focus %K speech-language pathologist %K school-age children %K language %K language sample analysis %K comprehensive assessment %K assessment %K participation %K Systematic Analysis of Language Transcripts (SALT) %K word %K morpheme %K utterance %K discourse features %K functional language %K computer %K technology %K computer technology %K software development %K transcription %K accuracy %K repeatability %K validity %K reliability %K databases %K typical speakers %K status %K automated report writing %K Language %K Computer Software %X
Purpose:The goal of this clinical focus article is to illustrate
how speech-language pathologists can document the
functional language of school-age children using language
sample analysis (LSA). Advances in computer hardware and
software are detailed making LSA more accessible for clinical use.
Method: The article illustrates how documenting schoolage
student’s communicative functioning is central to
comprehensive assessment and how using LSA can meet
multiple needs within this assessment. LSA can document
students’ meaningful participation in their daily life through
assessment of their language used during everyday tasks.
The many advances in computerized LSA are detailed with
a primary focus on the Systematic Analysis of Language
Transcripts (Miller & Iglesias, 2019). The LSA process is
reviewed detailing the steps necessary for computers
to calculate word, morpheme, utterance, and discourse
features of functional language.
Conclusion: These advances in computer technology
and software development have made LSA clinically
feasible through standardized elicitation and transcription
methods that improve accuracy and repeatability. In
addition to improved accuracy, validity, and reliability
of LSA, databases of typical speakers to document
status and automated report writing more than justify
the time required. Software now provides many
innovations that make LSA simpler and more accessible
for clinical use.

Supplemental Material S1. Play based—preschool.

Supplemental Material S2. Conversation—school age.

Supplemental Material S3. Narrative retell— Frog, where are you? (Mayer, 2003).
Supplemental Material S4. Expository—school age.


Supplemental Material S5. Persuasion.


Heilmann, J., Tucci, A., Plante, E., & Miller, J. F. (2020). Assessing functional language in school-aged children using language sample analysis. Perspectives of the ASHA Special Interest Groups. Advance online publication. https://doi.org/10.1044/2020_PERSP-19-00079
%I ASHA journals