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From the Editors

From the Editors

Language Sample Analysis: New and Neglected Clinical Applications

Section Editor(s): Troia, Gary A. PhD, CCC-SLP; Co-Editor; Wallace, Sarah E. PhD, CCC-SLP; Co-Editor

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Topics in Language Disorders: April/June 2020 - Volume 40 - Issue 2 - p 131
doi: 10.1097/TLD.0000000000000216
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In a recent survey study by Finestack and Sutterland (2018), formal language sample analysis (LSA) was reportedly used frequently to evaluate child language outcomes by less than a third of speech-language pathologists who provided early intervention services or who worked with elementary-aged children. The respondents indicated they most often employed mean length of utterance and type-token ratio as outcome measures when using language sampling as an evaluation tool for their clients. These survey results, along with results from other such surveys (e.g., Pavelko, Owens, Ireland, & Hahs-Vaughn, 2016; Westerveld & Claessen, 2014), were the impetus for this issue of Topics in Language Disorders, edited by Dr. Cheryl Scott. The group of articles in this issue addresses the potential for LSA to help identify spoken and written language difficulties, determine important therapeutic goals, and effectively monitor treatment progress when working with individuals who are at risk for or have been identified with a language disorder. These articles also shed light on many shortcomings of conventional LSA and associated measures (such as those mentioned earlier), while offering suggestions for overcoming at least some of these shortcomings using novel approaches and measures. We hope that these articles will inspire more practitioners and researchers to use formal LSA in their work on behalf of children and adults with language impairments and to think “outside the box” about the possibilities of LSA as a vital diagnostic and outcomes assessment tool. We suspect in the next decade or so, as efforts to deploy automation (e.g., the LENA [Language Environment Analysis] system) and artificial intelligence in linguistic applications (e.g., machine learning and natural language processing) continue to accelerate and improve, that the collection, transcription, and extraction of data from contextual language samples will be afforded without great effort or much time on the part of the clinician or researcher. The coupling of new and neglected clinical applications and measures discussed in this collection of articles with advances in technology and computing pose exciting future prospects for LSA, but as we see in this issue, LSA has much to offer even now.

—Gary A. Troia, PhD, CCC-SLP
—Sarah E. Wallace, PhD, CCC-SLP


Finestack L. H., Satterlund K. E. (2018). Current practice of child grammar intervention: A survey of speech-language pathologists. American Journal of Speech-Language Pathology, 27, 1329–1351.
Pavelko S. L., Owens R. E. Jr., Ireland M., Hahs-Vaughn D. L. (2016). Use of language sample analysis by school-based SLPs: Results of a nationwide survey. Language, Speech, and Hearing Services in Schools, 47(3), 246–258.
Westerveld M. F., Claessen M. (2014). Clinician survey of language sampling practices in Australia. International Journal of Speech-Language Pathology, 16(3), 242–249.
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