10.23641/asha.12456719.v1 John Heilmann John Heilmann Alexander Tucci Alexander Tucci Elana Plante Elana Plante Jon Miller Jon Miller Assessing functional language (Heilmann et al., 2020) ASHA journals 2020 clinical focus speech-language pathologist school-age children language language sample analysis comprehensive assessment assessment participation Systematic Analysis of Language Transcripts (SALT) word morpheme utterance discourse features functional language computer technology computer technology software development transcription accuracy repeatability validity reliability databases typical speakers status automated report writing Language Computer Software 2020-06-27 23:03:09 Media https://asha.figshare.com/articles/media/Assessing_functional_language_Heilmann_et_al_2020_/12456719 <div><b>Purpose:</b>The goal of this clinical focus article is to illustrate</div><div>how speech-language pathologists can document the</div><div>functional language of school-age children using language</div><div>sample analysis (LSA). Advances in computer hardware and</div><div>software are detailed making LSA more accessible for clinical use.</div><div><b>Method:</b> The article illustrates how documenting schoolage</div><div>student’s communicative functioning is central to</div><div>comprehensive assessment and how using LSA can meet</div><div>multiple needs within this assessment. LSA can document</div><div>students’ meaningful participation in their daily life through</div><div>assessment of their language used during everyday tasks.</div><div>The many advances in computerized LSA are detailed with</div><div>a primary focus on the Systematic Analysis of Language</div><div>Transcripts (Miller & Iglesias, 2019). The LSA process is</div><div>reviewed detailing the steps necessary for computers</div><div>to calculate word, morpheme, utterance, and discourse</div><div>features of functional language.</div><div><b>Conclusion: </b>These advances in computer technology</div><div>and software development have made LSA clinically</div><div>feasible through standardized elicitation and transcription</div><div>methods that improve accuracy and repeatability. In</div><div>addition to improved accuracy, validity, and reliability</div><div>of LSA, databases of typical speakers to document</div><div>status and automated report writing more than justify</div><div>the time required. Software now provides many</div><div>innovations that make LSA simpler and more accessible</div><div>for clinical use.</div><div><br></div><div><div><b>Supplemental Material S1. </b>Play based—preschool.</div><div><br></div><div><b>Supplemental Material S2. </b>Conversation—school age.</div><div><br></div><div><b>Supplemental Material S3. </b>Narrative retell—<i> Frog, where are you?</i> (Mayer, 2003).</div><div><b>Supplemental Material S4. </b>Expository—school age.</div><p></p><div><br></div><div><b>Supplemental Material S5.</b> Persuasion.</div><div><br></div><div><br></div><div>Heilmann, J., Tucci, A., Plante, E., & Miller, J. F. (2020). Assessing functional language in school-aged children using language sample analysis. <i>Perspectives of the ASHA Special Interest Groups.</i> Advance online publication. https://doi.org/10.1044/2020_PERSP-19-00079</div></div>