Developmental Language Disorder and the Assessment of Spoken Language

Developmental language disorder (DLD), previously known as specific language impairment (SLI), is a long-term developmental disorder affecting approximately 7.5% of children. Language abilities in children with DLD are variable and can be challenging to ascertain with confidence. This chapter aims to discuss some of the challenges associated with assessing the language skills of children with DLD through an overview of different forms of language assessment including standardized language testing, language sample analysis, and observations. Uses and limitations of the different forms of assessment are considered, bearing in mind the different functions of assessment and the need to gain a full understanding of children’s profiles of strength and weakness and communicative functioning in context. The authors conclude with requirements for best practice in assessment and promising avenues of development in this area.

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

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.

Unanalyzable Utterances in Language Sample Analysis Based on the P-LARSP: Preliminary Data of Typical Children Aged 18 - 60 Months

Background: The Persian language assessment, remediation, and screening procedure (P-LARSP) is the first formal approach to the analysis of language samples. Objectives: The present study aimed to investigate the unanalyzable utterances and mean length of utterances (MLUs: morpheme/analyzable text units) based on the first two sections of the P-LARSP. Methods: Experienced speech and language pathologists (SLPs) collected and analyzed the 10-minute language samples from 96 typical children aged 18 - 60 months within the context of free play. The unanalyzable units included unintelligible utterances, symbolic noise, deviant, incomplete, ambiguous, and stereotyped units, repetition, and structurally abnormal text units. Results: No significant differences were observed between the age groups in terms of the total number of the text units (P > 0.05) and unanalyzable text units (P = 0.08). Analyzable text units (P = 0.008) and MLUs (P = 0.004) were significant across the age groups. In addition, each category of the unanalyzable text units had a specific pattern, and the percentage of the incomplete utterances increased significantly from 18 to 60 months of age (P = 0.002). Conclusions: By applying the first two sections of the P-LARSP, we could sieve the analyzable from the unanalyzable text units and demonstrate the increasing trend of MLUs across the age groups. Increased incomplete utterances with age should be considered by SLPs during intervention and evaluation.

Bayesian Generalized Linear Mixed-Model Analysis of Language Samples: Detecting Patterns in Expository and Narrative Discourse of Adolescents With Traumatic Brain Injury

Purpose Generalized linear mixed-model (GLMM) and Bayesian methods together provide a framework capable of handling a wide variety of complex data commonly encountered across the communication sciences. Using language sample analysis, we demonstrate the utility of these methods in answering specific questions regarding the differences between discourse patterns of children who have experienced a traumatic brain injury (TBI), as compared to those with typical development. Method Language samples were collected from 55 adolescents ages 13–18 years, five of whom had experienced a TBI. We describe parameters relating to the productivity, syntactic complexity, and lexical diversity of language samples. A Bayesian GLMM is developed for each parameter of interest, relating these parameters to age, sex, prior history (TBI or typical development), and socioeconomic status, as well as the type of discourse sample (compare–contrast, cause–effect, or narrative). Statistical models are thoroughly described. Results Comparing the discourse of adolescents with TBI to those with typical development, substantial differences are detected in productivity and lexical diversity, while differences in syntactic complexity are more moderate. Female adolescents exhibited greater syntactic complexity, while male adolescents exhibited greater productivity and lexical diversity. Generally, our models suggest more advanced discourse among adolescents who are older or who have indicators of higher socioeconomic status. Differences relating to lecture type were also detected. Conclusions Bayesian and GLMM methods yield more informative and intuitive results than traditional statistical analyses, with a greater degree of confidence in model assumptions. We recommend that these methods be used more widely in language sample analysis. Supplemental Material https://doi.org/10.23641/asha.14226959

Language Sample Analysis in Children With Cleft Lip and Palate

Objectives: Cleft Palate (CP) with or without Cleft Lip (CL/P) are the most common craniofacial birth defects. Cleft Lip and Palate (CLP) can affect children’s communication skills. The present study aimed to evaluate language production skills concerning morphology and syntax (morphosyntactic) in children with CLP. Methods: In the current cross-sectional study, 58 Persian-speaking children (28 children with CLP & 30 children without craniofacial anomalies=non-clefts) participated. Gathering the language samples of the children was conducted using the picture description method. The 50 consecutive intelligible utterances of children were analyzed by the Persian Developmental Sentence Scoring (PDSS), as a clinical morphosyntactic measurement tool. Results: The PDSS total scores of children with CLP were lower than those of the non-clefts children. A significant difference was found between the studied children with CLP and children without craniofacial anomalies in the mean value of PDSS total scores (P=0.0001). Discussion: Children with CLP demonstrate a poor ability for using morphosyntactic elements. Therefore, it should be considered how children with CLP use the grammatical components.

Using Free Computer-Assisted Language Sample Analysis to Evaluate and Set Treatment Goals for Children Who Speak African American English

Purpose Spoken language sample analysis (LSA) is widely considered to be a critical component of assessment for child language disorders. It is our best window into a preschool child's everyday expressive communicative skills. However, historically, the process can be cumbersome, and reference values against which LSA findings can be “benchmarked” are based on surprisingly little data. Moreover, current LSA protocols potentially disadvantage speakers of nonmainstream English varieties, such as African American English (AAE), blurring the line between language difference and disorder. Method We provide a tutorial on the use of free software (Computerized Language Analysis [CLAN]) enabled by the ongoing National Institute on Deafness and Other Communication Disorders–funded “Child Language Assessment Project.” CLAN harnesses the advanced computational power of the Child Language Data Exchange System archive ( www.childes.talkbank.org ), with an aim to develop and test fine-grained and potentially language variety–sensitive benchmarks for a range of LSA measures. Using retrospective analysis of data from AAE-speaking children, we demonstrate how CLAN LSA can facilitate dialect-fair assessment and therapy goal setting. Results Using data originally collected to norm the Diagnostic Evaluation of Language Variation, we suggest that Developmental Sentence Scoring does not appear to bias against children who speak AAE but does identify children who have language impairment (LI). Other LSA measure scores were depressed in the group of AAE-speaking children with LI but did not consistently differentiate individual children as LI. Furthermore, CLAN software permits rapid, in-depth analysis using Developmental Sentence Scoring and the Index of Productive Syntax that can identify potential intervention targets for children with developmental language disorder.

Sampling Utterances and Grammatical Analysis Revised (SUGAR): Quantitative Values for Language Sample Analysis Measures in 7- to 11-Year-Old Children

Purpose The purpose of this study was to document whether mean length of utterance SUGAR (MLU S ), total number of words (TNW), clauses per sentence (CPS), and/or words per sentence (WPS) demonstrated age-related changes in children with typically developing language, aged 7;0–10;11 (years;months). Method Participants were 132 typically developing children (aged 7;0–10;11), with a final sample size of 112 participants (57 boys and 55 girls). Fifty utterance conversational language samples were collected using a language sampling protocol. Four language sample analysis metrics (i.e., MLU S , TNW, CPS, and WPS) were calculated from the samples. Results Results indicated statistically significant age-related increases in three (MLU S , TNW, and WPS) of the four metrics. Conclusions MLU S , TNW, CPS, and WPS may be used with other assessment data to document age-related language changes in children aged 7;0–10;11. When combined with previous data from younger (aged 3;0–7;11) children (Pavelko & Owens, 2017), the data suggest that these metrics offer a set of measures that can be used to assess children's conversational language skills from preschool through late elementary school.

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

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