Using PAALSS for the manual analysis of language samples of individuals who use aided AAC in Spanish: A pilot study

This pilot study describes the application of the Protocol for the Analysis of Aided Language Samples in Spanish (PAALSS), specifically designed for the manual analysis of language samples from individuals in the early stages of Spanish aided language development. Data were collected from 22 language samples from 16 individuals who use aided AAC and are at the earlier stages of Spanish language development. The primary objective of this study was to explore the feasibility of using PAALSS as an analytical tool to describe various aspects of the language samples, including lexical productivity, lexical diversity, morphology, grammatical complexity, and syntax. Results are presented according to four different groupings, based on the language samples' grammatical complexity scores. The study provides preliminary evidence of the potential of PAALSS as a useful tool for the manual analysis of language samples from users of AAC in Spanish. However, future studies are needed to establish its formal psychometric and measurement properties.

Clinical practices in Swedish speech-language pathology for children with (developmental) language disorder

In Sweden, treatment for children with (developmental) language disorder ((D)LD) is traditionally carried out at a speech-language pathology (SLP) clinic, and based on formal language tests, which may not entirely represent the child's everyday language and communication skills. SLP services that include video recordings have shown positive outcomes in terms of providing information about children's linguistic and communicative abilities in everyday life, but little is known about the use of video in clinical practice. The aim of this study is therefore to investigate how Swedish SLPs link their clinical practices (assessment, treatment, and evaluation of treatment outcome) to the everyday language and communication abilities of children with (D)LD. A further aim is to explore SLPs' utilisation of video recordings as a part of their clinical practices with the target group. A web-based questionnaire was distributed to SLPs in Sweden, who work with children with (D)LD. Results demonstrate that Swedish SLPs perceive that their intervention is in alignment with children's everyday language and communication needs to a fairly high degree. However, an exception is assessment, which is considered to have a weaker alignment with children's everyday communication abilities. The use of video recordings for clinical purposes is very limited. It is suggested here that incorporating video recordings from children's everyday life would be an easy and time-efficient way to strengthen the ecological validity of SLP practices for children with (D)LD.

How to Use the Index of Productive Syntax to Select Goals and Monitor Progress in Preschool Children

PURPOSE

Syntax assessment and treatment is a major focus of speech-language pathologists who work with young children with language needs. The Index of Productive Syntax (IPSyn) provides an efficient method for the assessment of syntax of preschool-age children. The purpose of this tutorial is to illustrate how to hand-score an IPSyn from language sample transcripts and use this information to formulate appropriate syntactic goals.

METHOD

We describe general methods and tips for facilitating hand-scoring. Two case study transcripts of a late talker at 36 and 48 months are provided. One case study transcript is fully scored in IPSyn with detailed explanations; the second case study is provided as a practice example with an IPSyn answer key. Relevant sample probes are provided to assist in the goal of obtaining a representative set of language structures.

RESULTS

We show how the IPSyn can be scored, subscale by subscale, and can highlight developmental stages of syntax following Brown's stages of grammatical development. We illustrate how the visual snapshot developmental nature of the instrument makes it ideal for deriving goals.

CONCLUSION

The IPSyn is a practical and valuable tool for the assessment of syntax of preschool-age children.

Bilingual Supports for Clinicians: Where Do We Go From Here?

Purpose The purpose of this study was to identify and define the support needs of bilingual speech-language pathologists working in one urban school district and to investigate the experiences of the clinicians engaged in a professional learning community (PLC) format within their public school system. Method Twenty-three bilingual speech-language pathologists met in a PLC over the course of 5 months, with a total of four sessions that were a mix of in person and virtual. The participants engaged in group discussions and activities alongside their peers to address self-identified areas of support and need. Additionally, initial and final focus group and exit slip data following the PLC were collected to inform the discussion topics of the PLC and gather final impressions. Constant comparative analysis was utilized to analyze focus group and exit slip data. Results When looking across the experiences of the clinicians in the district, the three themes that emerged from the data were systemic obstacles, professional obstacles, and supports. The three themes that emerged from the speech-language pathologists' experiences in the PLC included their positive experiences, the challenges with participation, and the positive outcomes from exposure to new techniques and earning platforms. Conclusion The findings of this study suggest that bilingual clinicians benefit from a PLC model when addressing specific areas of need.

Revisiting Reliability: Using Sampling Utterances and Grammatical Analysis Revised (SUGAR) to Compare 25- and 50-Utterance Language Samples

Purpose The goal of this study was to determine whether the results obtained from a 25-utterance conversational language sample were as reliable as those obtained from a 50-utterance sample. Method Robust conversational language samples from 220 children with typically developing language (106 boys, 114 girls) ranging in age from 3;2 to 7;10 (years;months) were collected. The language samples were randomly assigned to one of two conditions: a 25-utterance condition and a 50-utterance condition. Transcripts were examined for three metrics, including mean length of utterance_SUGAR, words per sentence, and clauses per sentence. Results Data were analyzed using two methods. A linear mixed-model analysis was used to assess absolute and relative reliability, and the Bland-Altman procedure was used to assess absolute reliability and clinical acceptability. Results of the mixed-model analysis indicated that mean length of utterance_SUGAR and words per sentence demonstrated relative reliability; however, none of the metrics demonstrated absolute reliability. In contrast, results of the Bland-Altman scatter plots indicated that all three metrics demonstrated absolute reliability because 94%-96% of participants' scores fell within the limits of agreement. Taken together, the results suggested that the statistically significant differences indicated by the mixed-model analysis were not clinically significant. Conclusion These results highlighted the importance of using different methods of analysis in studies of reliability. The findings indicated that reliable language sample results can be obtained from 25-utterance samples. Furthermore, by including practices already in use (e.g., collecting samples ≤ 50 utterances) and including only minimal changes to current practices, the methods used in this study are feasible for school-based clinicians, could be easily integrated into clinical practice, and could increase the use of evidence-based assessment practices in schools.

Using Computer Programs for Language Sample Analysis

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

SUGAR (Sampling Utterances and Grammatical Analysis Revised): Breaking Tradition

Purpose

In this letter, the authors respond to Guo, Eisenberg, Ratner, and MacWhinney's (2018) letter to the editor, which expressed concerns regarding the clinical applicability of the Sampling Utterances and Grammatical Analysis Revised (SUGAR; Pavelko & Owens, 2017 ) language sample analysis (LSA) method.

Method

The authors respond to concerns regarding transcription and morpheme segmentation and language sample elicitation contexts. The results of two published research studies are described, which detail the impact of this new LSA method.

Results

Published research studies have documented that the SUGAR method of LSA can accurately distinguish between children developing language typically and children with language impairment and can also aid clinicians in developing intervention goals.

Conclusion

SUGAR is a clinically applicable method of LSA that clinicians can use as one part of a comprehensive evaluation to efficiently and accurately identify children with language impairment. Further, clinicians can complete subanalysis of a language sample to identify appropriate therapy targets.

Diagnostic Accuracy of the Sampling Utterances and Grammatical Analysis Revised (SUGAR) Measures for Identifying Children With Language Impairment

Purpose

The purpose of this study was twofold: (a) to determine the diagnostic accuracy of the four Sampling Utterances and Grammatical Analysis Revised (SUGAR) metrics, including total number of words, mean length of utterance SUGAR , words per sentence, and clauses per sentence in differentiating children with language impairment (LI) from those with typical language development, and (b) to compare the average time to collect, transcribe, and analyze 50-utterance language samples for children with LI to those with typical language development.

Method

Participants were 306 children (LI, 36; typical language development, 270) who ranged in age from 3;0 (years;months) to 7;11. Fifty-utterance conversational language samples were obtained using a conversational protocol. The four SUGAR metrics were calculated from the samples.

Results

Cut scores of −1 SD for mean length of utterance SUGAR and −1.25 cut score for clauses per sentence resulted in sensitivity of 97.22%, specificity of 82.96%, a positive likelihood ratio of 5.71, and a negative likelihood ratio of 0.03. On average, it took a total time of 20:20 min ( SD = 4:37, range: 13:11–30:25) to collect, transcribe, and analyze language samples for children with LI. Children with LI took significantly less time to produce 50 utterances, when compared to their typically developing peers. There were no significant differences in the time to transcribe and analyze language samples of children with LI compared to their typically developing peers.

Conclusions

The SUGAR metrics, in combination with other data sources (e.g., standardized testing, dynamic assessment, observation), can be used to identify preschool- and early elementary–aged children with LI. Furthermore, for children with LI, language sampling and analysis using the SUGAR method can be completed in approximately 20 min. The results of this study indicated the SUGAR measures can effectively and efficiently help in identifying LI.

Supplemental Material

https://doi.org/10.23641/asha.7728638