Issues in Language Sample Collection and Analysis With Children Using AAC

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.

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Myths and Realities of Language Sample Analysis

Perspectives on Language Learning and Education ◽

10.1044/lle17.1.4 ◽

2010 ◽

Vol 17 (1) ◽

pp. 4-8 ◽

Cited By ~ 17

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.

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The Clinical Utility of Language Samples

Perspectives on Language Learning and Education ◽

10.1044/lle17.1.9 ◽

2010 ◽

Vol 17 (1) ◽

pp. 9-15 ◽

Cited By ~ 24

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.

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Incorporating Computer-Aided Language Sample Analysis Into Clinical Practice

Language Speech and Hearing Services in Schools ◽

10.1044/0161-1461(2009/08-0054) ◽

2010 ◽

Vol 41 (2) ◽

pp. 206-222 ◽

Cited By ~ 32

Author(s):

Lisa Hammett Price ◽

Sean Hendricks ◽

Colleen Cook

Keyword(s):

Clinical Practice ◽

Sample Analysis ◽

Language Sample Analysis ◽

Computer Aided ◽

Language Sample

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Expressive Language in Preschoolers Born Preterm: Results of Language Sample Analysis and Standardized Assessment

Journal of Speech Language and Hearing Research ◽

10.1044/2018_jslhr-l-18-0224 ◽

2019 ◽

Vol 62 (4) ◽

pp. 884-895 ◽

Cited By ~ 6

Author(s):

Caitlin M. Imgrund ◽

Diane F. Loeb ◽

Steven M. Barlow

Keyword(s):

Language Skills ◽

Expressive Language ◽

Full Term ◽

Language Skill ◽

Standardized Assessment ◽

Group Differences ◽

Sample Analysis ◽

Assessment Scores ◽

Language Sample Analysis ◽

Language Sample

Purpose Preschoolers born preterm are at an increased risk for the development of language impairments. The primary objective of this study was to document the expressive language skills of preschoolers born preterm through 2 assessment procedures, language sample analysis, and standardized assessment. A secondary objective was to investigate the role of nonlinguistic factors in standardized assessment scores. Method The language skills of 29 children born preterm (mean gestational age of 31 weeks) were compared to a group of 29 preschoolers born full term. Language samples were collected during free play and objective measures of semantic and grammatical skills were calculated. Likewise, grammatical and semantic measures of language were obtained from a standardized assessment. Information on nonlinguistic factors, including attention, hyperactivity, and nonverbal intelligence quotient, was also collected. Results The results of analyses of variance indicated that the children in the PT group had significantly poorer performance than the children born full term on all of the measures of language skill obtained from the language sample analysis. Group differences were found for only 1 measure of language skill obtained from the standardized assessments. Nonverbal factors were not found to account for group differences in assessment scores. Conclusions Generally, the children born preterm performed more poorly when language skill was measured via language sample analysis than standardized assessment. These findings underscore the importance of using language sample analysis in conjunction with standardized assessment in the evaluation of children's developing language skills.

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Language Sample Collection and Analysis in People Who Use Augmentative and Alternative Communication: Overcoming Obstacles

American Journal of Speech-Language Pathology ◽

10.1044/2020_ajslp-19-00055 ◽

2021 ◽

Vol 30 (1) ◽

pp. 47-62

Author(s):

Aine Mooney ◽

Allison Bean ◽

Amy Miller Sonntag

Keyword(s):

Standardized Testing ◽

Augmentative And Alternative Communication ◽

Relevant Information ◽

Valuable Insight ◽

Alternative Communication ◽

Sample Collection ◽

Language Abilities ◽

Systematic Analysis ◽

Parts Of Speech ◽

Language Sample

Purpose Language sample collection and analysis provides important information regarding the language abilities of individuals for whom standardized testing may not be appropriate, such as persons who use augmentative and alternative communication (PWUAACs). Despite its clinical utility, language sample collection and analysis has not been fully incorporated into the assessment of PWUAACs due to a variety of challenges. This study seeks to investigate the ability of language sample collection and analysis to provide clinically relevant information and explore ways to circumvent language sample collection and analysis challenges for PWUAACs. Method This is a case study of the narratives of two PWUAACs, one child and one adult. Analyses were conducted using manual calculations and computerized language sample analysis software (i.e., Systematic Analysis of Language Transcripts and Child Language Exchange System) and Realize Language. Conclusion Although the language samples took longer to complete relative to verbal controls, the information obtained from language sample collection and analysis provided valuable insight into the language system of the two participants that would not be revealed through standardized language assessment, including the distribution of their parts of speech and syntactic complexity. Given the important clinical data that may be obtained through language sample collection and analysis, we propose strategies to enable clinicians to overcome previously identified challenges.

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Using Language Sample Analysis to Assess Spoken Language Production in Adolescents

Language Speech and Hearing Services in Schools ◽

10.1044/2015_lshss-15-0051 ◽

2016 ◽

Vol 47 (2) ◽

pp. 99-112 ◽

Cited By ~ 18

Author(s):

Jon F. Miller ◽

Karen Andriacchi ◽

Ann Nockerts

Keyword(s):

Language Production ◽

Language Use ◽

Spoken Language ◽

Sample Analysis ◽

Systematic Analysis ◽

Mean Values ◽

School Work ◽

Language Sample Analysis ◽

Language Measures ◽

Language Sample

Purpose This tutorial discusses the importance of language sample analysis and how Systematic Analysis of Language Transcripts (SALT) software can be used to simplify the process and effectively assess the spoken language production of adolescents. Method Over the past 30 years, thousands of language samples have been collected from typical speakers, aged 3–18 years, in conversational and narrative contexts. These samples have been formatted as reference databases included with SALT. Using the SALT software, individual samples are compared with age- and grade-matched samples selected from these databases. Results Two case studies illustrate that comparison with database samples of typical adolescents, matched by grade and elicitation context, highlights language measures that are higher or lower than the database mean values. Differences in values are measured in standard deviations. Conclusion Language sample analysis remains a powerful method of documenting language use in everyday speaking situations. A sample of talking reveals an individual's ability to meet specific speaking demands. These demands vary across contexts, and speakers can have difficulty in any one or all of these communication tasks. Language use for spoken communication is a foundation for literacy attainment and contributes to success in navigating relationships for school, work, and community participation.

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

Language Speech and Hearing Services in Schools ◽

10.1044/2021_lshss-21-00026 ◽

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.

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