scholarly journals Attitude of Korean Speech-Language Pathologists toward Usage of Language Sample Analysis and Computer-aided LSA

2020 ◽  
Vol 25 (3) ◽  
pp. 651-668
Author(s):  
YoonKyoung Lee ◽  
Jieun Choi ◽  
So Jung Oh ◽  
Ji Hye Yoon ◽  
Yu-Seop Kim
Keyword(s):  
Sample Analysis ◽  
Speech Language Pathologists ◽  
Language Sample Analysis ◽  
Computer Aided ◽  
Language Sample

Issues in Language Sample Collection and Analysis With Children Using AAC

2014 ◽  
Vol 23 (2) ◽  
pp. 65-74 ◽  
Author(s):  
Gail Van Tatenhove
Keyword(s):  
Clinical Practice ◽  
Augmentative And Alternative Communication ◽  
Language Production ◽  
Expressive Language ◽  
Sample Analysis ◽  
Alternative Communication ◽  
Sample Collection ◽  
Speech Language Pathologists ◽  
Language Sample Analysis ◽  
Language Sample

Language sample analysis is considered one of the best methods of evaluating expressive language production in speaking children. However, the practice of language sample collection and analysis is complicated for speech-language pathologists working with children who use augmentative and alternative communication (AAC) devices. This article identifies six issues regarding use of language sample collection and analysis in clinical practice with children who use AAC devices. The purpose of this article is to encourage speech-language pathologists practicing in the area of AAC to utilize language sample collection and analysis as part of ongoing AAC assessment.

The Clinical Utility of Language Samples

2010 ◽  
Vol 17 (1) ◽  
pp. 9-15 ◽  
Author(s):  
Amy Costanza-Smith
Keyword(s):  
Standardized Tests ◽  
Clinical Utility ◽  
Language Disorders ◽  
Standardized Assessments ◽  
School Age ◽  
Sample Analysis ◽  
Speech Language Pathologists ◽  
Language Sample Analysis ◽  
Language Sample

Abstract Speech-language pathologists typically use standardized assessments to diagnose language disorders. Although standardized tests are important in diagnosing school-age language disorders, the use of language sample analysis should not be ignored. This article summarizes the benefits of language sample analysis and introduces considerations for collecting and analyzing language samples.

Myths and Realities of Language Sample Analysis

2010 ◽  
Vol 17 (1) ◽  
pp. 4-8 ◽  
Author(s):  
John J. Heilmann
Keyword(s):  
Clinical Practice ◽  
Language Skills ◽  
Clinical Use ◽  
Sample Analysis ◽  
Speech Language Pathologists ◽  
Language Sample Analysis ◽  
Key Issues ◽  
Language Sample

Abstract While language sample analysis (LSA) is frequently recommended for assessing children’s language skills, many speech-language pathologists do not use it regularly. This article reviewed key issues surrounding clinical use of LSA to provide clinicians with a realistic overview of the process and encourage increased use of LSA in clinical practice.

scholarly journals Language Sample Analysis in Clinical Practice: Speech-Language Pathologists' Barriers, Facilitators, and Needs

2021 ◽  
pp. 1-16
Author(s):  
Inge S. Klatte ◽  
Vera van Heugten ◽  
Rob Zwitserlood ◽  
Ellen Gerrits
Keyword(s):  
Focus Group ◽  
Language Disorder ◽  
Daily Practice ◽  
Insurance Companies ◽  
Sample Analysis ◽  
Speech Language Pathologists ◽  
Actual Performance ◽  
Time Investment ◽  
Language Sample Analysis ◽  
Language Sample

Purpose Most speech-language pathologists (SLPs) working with children with developmental language disorder (DLD) do not perform language sample analysis (LSA) on a regular basis, although they do regard LSA as highly informative for goal setting and evaluating grammatical therapy. The primary aim of this study was to identify facilitators, barriers, and needs related to performing LSA by Dutch SLPs working with children with DLD. The secondary aim was to investigate whether a training would change the actual performance of LSA. Method A focus group with 11 SLPs working in Dutch speech-language pathology practices was conducted. Barriers, facilitators, and needs were identified using thematic analysis and categorized using the theoretical domain framework. To address the barriers, a training was developed using software program CLAN. Changes in barriers and use of LSA were evaluated with a survey sent to participants before, directly after, and 3 months posttraining. Results The barriers reported in the focus group were SLPs' lack of knowledge and skills, time investment, negative beliefs about their capabilities, differences in beliefs about their professional role, and no reimbursement from health insurance companies. Posttraining survey results revealed that LSA was not performed more often in daily practice. Using CLAN was not the solution according to participating SLPs. Time investment remained a huge barrier. Conclusions A training in performing LSA did not resolve the time investment barrier experienced by SLPs. User-friendly software, developed in codesign with SLPs might provide a solution. For the short-term, shorter samples, preferably from narrative tasks, should be considered.

Assessing Functional Language in School-Aged Children Using Language Sample Analysis

2020 ◽  
Vol 5 (3) ◽  
pp. 622-636
Author(s):  
John Heilmann ◽  
Alexander Tucci ◽  
Elena Plante ◽  
Jon F. Miller
Keyword(s):  
Computer Hardware ◽  
School Age Children ◽  
Functional Language ◽  
School Age ◽  
Clinical Use ◽  
Sample Analysis ◽  
Validity And Reliability ◽  
Language Sample Analysis ◽  
Accuracy And Repeatability ◽  
Language Sample

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 This clinical focus article illustrates how documenting school-age 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 https://doi.org/10.23641/asha.12456719

Language Sample Analysis and Elicitation Technique Effects in Bilingual Children With and Without Language Impairment

2017 ◽  
Vol 60 (10) ◽  
pp. 2852-2864 ◽  
Author(s):  
Maria Kapantzoglou ◽  
Gerasimos Fergadiotis ◽  
M. Adelaida Restrepo
Keyword(s):  
Language Impairment ◽  
Classification Accuracy ◽  
Sentence Length ◽  
Primary Language ◽  
Sample Analysis ◽  
Story Retelling ◽  
Language Sample Analysis ◽  
English Speaking ◽  
Primary Language Impairment ◽  
Language Sample

Purpose This study examined whether the language sample elicitation technique (i.e., storytelling and story-retelling tasks with pictorial support) affects lexical diversity (D), grammaticality (grammatical errors per communication unit [GE/CU]), sentence length (mean length of utterance in words [MLUw]), and sentence complexity (subordination index [SI]), which are commonly used indices for diagnosing primary language impairment in Spanish–English-speaking children in the United States. Method Twenty bilingual Spanish–English-speaking children with typical language development and 20 with primary language impairment participated in the study. Four analyses of variance were conducted to evaluate the effect of language elicitation technique and group on D, GE/CU, MLUw, and SI. Also, 2 discriminant analyses were conducted to assess which indices were more effective for story retelling and storytelling and their classification accuracy across elicitation techniques. Results D, MLUw, and SI were influenced by the type of elicitation technique, but GE/CU was not. The classification accuracy of language sample analysis was greater in story retelling than in storytelling, with GE/CU and D being useful indicators of language abilities in story retelling and GE/CU and SI in storytelling. Conclusion Two indices in language sample analysis may be sufficient for diagnosis in 4- to 5-year-old bilingual Spanish–English-speaking children.

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