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Use of Language Sample Analysis by School-Based SLPs: Results of a Nationwide Survey
Open accessAbstract
Purpose This article examines use of language sample analysis (LSA) by school-based speech-language pathologists (SLPs), including characteristics of language samples, methods of transcription and analysis, barriers to LSA use, and factors affecting LSA use, such as American Speech-Language-Hearing Association certification, number of years' experience, and caseload characteristics. Method School-based SLPs responded to an invitation to complete an electronic survey related to LSA. Results One third of respondents indicated they did not use LSA during the 2012-2013 school year. SLPs who served middle and high school students were less likely to use LSA. Most respondents reported using conversation to analyze fewer than 10 samples and transcribing in real time. Additional analyses revealed that SLPs who had 3 or fewer years of experience and who analyzed fewer than 20 language samples per year were statistically less likely to report using real-time transcription. The most frequently cited barrier to using LSA was "LSA is too time-consuming." Conclusion Many school-based SLPs do not routinely use LSA. Further, many did not report engaging in evidence-based practices, such as recording samples, using established protocols, or using tasks designed to elicit complex syntax. These results indicate a continued need for professional development regarding evidenced-based practices relative to LSA use.
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In the citing paper
“LSA is frequently adopted as an informal language assessment measure, with less than one third of school-based SLPs using a specific protocol (Pavelko et al., 2016).”
From Early Identification of Language Disorders Using Natural Language Processing and Machine Learning: Challenges and Emerging Approaches (2025) · cites this paper as Pavelko et al., 2016
In the citing paper
“In a large survey of school-based SLPs, only 67% ( N = 1,336) of respondents reported using LSA during the 2012-2013 school year (Pavelko et al., 2016).”
From Early Identification of Language Disorders Using Natural Language Processing and Machine Learning: Challenges and Emerging Approaches (2025) · cites this paper as Pavelko et al., 2016
In the citing paper
“Indeed, many SLPs report a desire for further training in LSA (84%) and the interpretation of results (83%; Pavelko et al., 2016).”
From Early Identification of Language Disorders Using Natural Language Processing and Machine Learning: Challenges and Emerging Approaches (2025) · cites this paper as Pavelko et al., 2016
In the citing paper
“However, despite their accessibility, objectivity, and cost effectiveness, few SLPs (2%-7%) reported using computer-based approaches (McLeod & Baker, 2014; Pavelko et al., 2016).”
From Early Identification of Language Disorders Using Natural Language Processing and Machine Learning: Challenges and Emerging Approaches (2025) · cites this paper as Pavelko et al., 2016
In the citing paper
“This was particularly true of more experienced respondents (Pavelko et al., 2016).”
From Early Identification of Language Disorders Using Natural Language Processing and Machine Learning: Challenges and Emerging Approaches (2025) · cites this paper as Pavelko et al., 2016
In the citing paper
“Many SLPs report a preference for language sampling approaches relative to standardized norm-referenced tests but cite time constraints, lack of training opportunities, and lack of computing resources as major barriers (Calder et al., 2017; Pavelko et al., 2016; Westerveld & Claessen, 2014).”
From Early Identification of Language Disorders Using Natural Language Processing and Machine Learning: Challenges and Emerging Approaches (2025) · cites this paper as Pavelko et al., 2016
In the citing paper
“Clinicians indicate a strong need for proper training using computerized tools (Pavelko et al., 2016);”
From Early Identification of Language Disorders Using Natural Language Processing and Machine Learning: Challenges and Emerging Approaches (2025) · cites this paper as Pavelko et al., 2016
In the citing paper
“SLPs report a lack of time and training as major barriers to the feasibility of LSA (Pavelko et al., 2016; Klatte et al., 2022).”
From Early Identification of Language Disorders Using Natural Language Processing and Machine Learning: Challenges and Emerging Approaches (2025) · cites this paper as Pavelko et al., 2016
Citations
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Metadata
- Authors
- Pavelko, S. L., Owens, R. E., Ireland, M., & Hahs-Vaughn, D. L.
- Affiliations
- James Madison University (US)
- College of Saint Rose (US)
- Virginia Department of Education (US)
- University of Central Florida, Orlando
- Venue
- Language, Speech, and Hearing Services in Schools
- Published
- 2016-07
- Volume
- 47
- Issue
- 3
- Pages
- 246-258
- ISSN
- 0161-1461 (ISSN-L)
- 1558-9129
- OpenAlex
- W2471087218
- Cited by
- 179
- References
- 39
- Citations by year
- 2026: 23 · 2025: 9 · 2024: 17 · 2023: 20 · 2022: 23 · 2021: 15 · 2020: 24 · 2019: 30 · 2018: 12 · 2017: 6
- FWCI
- 37.46
- Type
- journal article
- Language
- en
- Open access
- Locations
- Language Speech and Hearing Services in Schools (publishedVersion)
- PubMed (publishedVersion)
- Journal of International Crisis and Risk Communication Research (submittedVersion)
- Funders
- Virginia Department of Education
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Language Development and Disorders
- Health Sciences › Health Professions › Speech and Hearing › Voice and Speech Disorders
- Social Sciences › Psychology › Developmental and Educational Psychology › Behavioral and Psychological Studies
- Keywords
- language sample analysis, school-based speech-language pathologists, transcription methods, real-time transcription, evidence-based practice, professional development, middle and high school students
- MeSH terms
- Child, Child Language, Child, Preschool, Clinical Competence, Female, Humans, Infant, Language Development, Language Development Disorders, Language Tests, Male, Surveys and Questionnaires, School Health Services, Speech-Language Pathology, Students, United States, American Speech-Language-Hearing Association, Workload
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