Using Language Sample Analysis to Assess Spoken Language Production in Adolescents

2016 ◽  
Vol 47 (2) ◽  
pp. 99-112 ◽  
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.

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.

scholarly journals Using Computer Programs for Language Sample Analysis

2020 ◽  
Vol 51 (1) ◽  
pp. 103-114 ◽  
Author(s):  
Mollee J. Pezold ◽  
Caitlin M. Imgrund ◽  
Holly L. Storkel
Keyword(s):  
Preschool Children ◽  
Word Processing ◽  
Computer Programs ◽  
Treatment Goals ◽  
Potential Treatment ◽  
Sample Analysis ◽  
Systematic Analysis ◽  
Grammatical Analysis ◽  
Language Sample Analysis ◽  
Language Sample

Purpose Although language sample analysis is widely recommended for assessing children's expressive language, few school-based speech-language pathologists routinely use it, citing a lack of time, resources, and training ( Pavelko, Owens, Ireland, & Hahs-Vaughn, 2016 ). The purpose of this clinical tutorial is (a) to describe options for language sample analysis using computer programs and (b) to demonstrate a process of using language sample analysis focused on the assessment of 2 preschool children as case studies. Method We provide an overview of collecting and analyzing child language samples and describe 3 programs for language sample analysis: 2 dedicated software programs (Computerized Language Analysis [ MacWhinney, 2000 ] and Systematic Analysis of Language Transcripts [ Miller & Iglesias, 2015 ]) and 1 protocol for using word processing software to analyze language samples (Sampling Utterances and Grammatical Analysis Revised; Pavelko & Owens, 2017 ). We also present analysis results from each program for play-based language samples from 2 preschool children and detailed analysis of the samples with potential treatment goals. Results Each program offers different analyses, comparison databases, and sampling contexts. We present options for additional analysis, clinical interpretations, and potential treatment goals based on the 2 preschool cases. Conclusion Clinicians can use computer programs for language sample analysis as part of a process to make naturalistic language assessment more feasible. Supplemental Material https://doi.org/10.23641/asha.10093403

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

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.

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.

Use of Language Sample Analysis by School-Based SLPs: Results of a Nationwide Survey

2016 ◽  
Vol 47 (3) ◽  
pp. 246-258 ◽  
Author(s):  
Stacey L. Pavelko ◽  
Robert E. Owens ◽  
Marie Ireland ◽  
Debbie L. Hahs-Vaughn
Keyword(s):  
High School Students ◽  
Real Time ◽  
Nationwide Survey ◽  
Sample Analysis ◽  
School Students ◽  
Factors Affecting ◽  
School Based ◽  
Language Sample Analysis ◽  
Evidenced Based ◽  
Language Sample

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.

Spoken Language Production in Huntington's and Parkinson's Diseases

2000 ◽  
Vol 43 (6) ◽  
pp. 1350-1366 ◽  
Author(s):  
Laura L. Murray
Keyword(s):  
Language Production ◽  
Spoken Language ◽  
Control Group ◽  
Language Abilities ◽  
Motor Speech ◽  
Parkinson’S Diseases ◽  
Language Measures ◽  
Simple Sentences ◽  
And Control ◽  
Language Profiles

The purpose of this study was to investigate the presence and nature of spoken language deficits in Huntington's (HD) and Parkinson's (PD) diseases. Specifically, the study examined whether (a) the spoken language abilities of patients with HD or PD differ from those of age-matched control participants with no brain damage, (b) HD and PD are associated with similar spoken language profiles, and (c) the spoken language abilities of patients with HD or PD are related to the severity of their motor speech deficits, cognitive impairments, or both. All participants completed picture description tasks and a battery of cognitive and motor speech tests. Syntactic, quantitative, and informativeness measures of spoken language were analyzed. In terms of syntax, patients with HD produced shorter utterances, a smaller proportion of grammatical utterances, a larger proportion of simple sentences, and fewer embeddings per utterance than their non-brain-damaged peers. The HD group also produced utterances that were shorter and syntactically simpler than those of the PD group, despite similar performances on the cognitive and motor speech tests. The only syntactic difference between the PD group and their control group was that patients with PD produced a smaller proportion of grammatical sentences. Although the patient and control participants tended to produce similar amounts of verbal output, less of what the patients said was considered informative. Correlations between language measures and test battery results suggested that the spoken language abilities of patients with HD or PD are related to a variety of neuropsychological and motor speech changes. The implications of these findings for the clinical management of HD and PD are discussed.

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