Using language sample analyses across English dialects: A case-based approach for preschoolers.

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Published in final edited form as: Clin Linguist Phon. 2024 Jul 4;39(6-8):808–825. doi: 10.1080/02699206.2024.2374917 Using language sample analyses across English dialects: A case-based approach for preschoolers Leslie E Kokotek a , Karla N Washington a, b , Nicole Bazzocchi b Author information Article notes Copyright and License information a Department of Communication Sciences and Disorders, University of Cincinnati, Cincinnati, Ohio, USA b Department of Speech-Language Pathology, University of Toronto, Toronto, Ontario, Canada ✉ CONTACT Leslie E. Kokotek, stokelle@ucmail.uc.edu , Department of Communication Sciences and Disorders, College of Allied Health Sciences, University of Cincinnati Medical Center, PO Box 670379, 3225 Eden Avenue Rm. 392B, Health Sciences Building, Cincinnati, OH 45267-0379 Issue date 2025 Jun-Aug. PMC Copyright notice PMCID: PMC12091638  NIHMSID: NIHMS2077511  PMID: 38965827 The publisher's version of this article is available at Clin Linguist Phon Abstract This study compared language samples from typically developing 4-year-olds who spoke African American English (AAE), Jamaican English (JE), or Mainstream American English (MAE) to assess the value of using language sample analysis (LSA) measures for characterising language use across dialects of English. Specific LSA metrics included mean length of utterance (MLU) in morphemes and in words, the Index of Productive Syntax (IPSyn), Developmental Sentence Scoring (DSS) and measures of lexical diversity. Children demonstrated diverse linguistic patterns across dialects, but a Kruskal–Wallis H test did not reveal significant differences in scores obtained through LSA measures. Notably, the IPSyn captured morphosyntactic structures in each category across dialects where prior research has highlighted limitations. This preliminary study uses a case-based approach to illustrate the applicability of LSAs in describing linguistic variations across children who speak different dialects of English. Moreover, the findings from this study underscore the potential use of LSAs in describing linguistic patterns to support the characterisation of communication profiles for culturally and linguistically diverse children. Keywords: Language sample, IPSyn, linguistic diversity Introduction In English-dominated settings such as the United States of America (USA), children who are fluent in the mainstream variety of English but are also bilingual or speak non-mainstream dialects are at an increased risk for the misdiagnosis of developmental language disorder (DLD; Paradis et al., 2021 ). This is especially true for children who are Black, Indigenous, or otherwise members of the Global Majority ( Charity, 2008 ; De Costa, 2020 ) as there is increased stigmatisation surrounding their language use ( De Costa, 2020 ; Washington et al., 2023 ). Moreover, these children’s English proficiency increases the already high likelihood that they will be evaluated using assessments standardised for White monolingual speakers of Mainstream American English (MAE) ( Pena Díaz, 2006 ; Washington et al., 2023 ). In the Western world and especially in the USA, the White mainstream demography has historically been cited as the standard for comparison in the field of speech-language pathology (SLP) and beyond ( Nair et al., 2023 ; Washington et al., 2023 ). With over 90% of SLPs identifying as being monolingual MAE speakers, the majority of whom identify as White ( American Speech-Language-Hearing Association [ASHA], 2022 ), this tendency to compare non-MAE speakers to a ‘mainstream standard’ is further exacerbated by the cultural and linguistic mismatch between SLPs and the families they serve ( Hopf et al., 2021 ). However, a growing body of research suggests that (outside the diversification of the field) using language sample analysis (LSA) for assessments and for building communication profiles may serve as one approach for mitigating challenges related to the misdiagnosis of DLD for linguistically diverse populations. However, more research documenting the applicability of LSA measures across linguistic contexts is needed ( Saban-Dülger et al., 2022 ; Washington et al., 2019 ; Wright Karem et al., 2022 ). LSA offers several benefits when considering the language use of children. Notably, it allows for the analysis of children’s natural language productions in everyday scenarios ( Overton et al., 2021 ; Spaulding et al., 2012 ). Different LSA measures also provide insight into different aspects of children’s language use such as morphology and lexical diversity to name a few ( Saban-Dülger et al., 2022 ). Despite these benefits, however, LSA is not immune to the same challenges inherent within standardised testing. Stated differently, normative standards for LSA are generally based on a limited sample size of MAE speakers (see Overton et al., 2021 for a discussion) resulting in a limited availability of psychometric evidence for informing the communication skills of MAE and non-MAE speakers. Moreover, the process for conducting in-depth LSAs has consistently been noted as time-consuming ( Finestack et al., 2020 ) across researchers and clinicians. This persistent challenge contributes to a reduced implementation of methods that could otherwise help to mitigate the challenges surrounding the misdiagnosis of DLD in non-MAE-speaking children. Yet, even with these limitations, LSA has still been championed as demonstrating less bias ( Horton-Ikard & Pittman, 2010 ; Stockman et al., 2016 ) than standardised testing and should be included as part of a comprehensive evaluation. Proponents of this approach have especially recommended the use of LSA measures for commonly occurring dialectal variations such as African American English (AAE). Likewise, similar recommendations have been made for considering the use of LSA with bilingual speakers ( Saban-Dülger et al., 2022 ; Washington et al., 2019 ; Wright Karem et al., 2022 ). For instance, recent studies have investigated the applicability of using certain LSA measures for bilingual Jamaican preschoolers who speak Jamaican English (JE) and Jamaican Creole (JC). For example, The Index of Productive Syntax (IPSyn) was found useful for characterising the grammatical development of 4- to 6-year-old bilingual speakers of Jamaican Creole and English ( Washington et al., 2019 ) and for 3- to 6-year-old bilingual speakers of Malaysian Chinese and English ( Ooi & Wong, 2012 ). Investigating a similar population, Kokotek et al. (2022) validation study of the FOCUS found LSA to be beneficial in assessing the communicative participation and expressive language skills of Jamaican Creole-English speaking children. These findings support the need for continued research in using LSAs for child speakers of varying dialects of English. Emerging evidence exists for using different LSA measures cross linguistically (i.e. monolingual non-MAE and bilingual). This is especially true across linguistic groups when transcribers have a high degree of familiarity with recognising various dialectal features. However, studies have also highlighted how children who do not use the mainstream ambient dialect, such as children who use AAE or JE, are still likely to be over-represented in special education ( Farrugia-Bernard, 2018 ; Washington et al., 2023 ). Given the increased likelihood that speakers of non-mainstream linguistic varieties or bilingual backgrounds have been inappropriately placed in special education services or misdiagnosed with a DLD ( Boerma et al., 2016 ; Fichman et al., 2017 ; Paradis et al., 2021 ), they are also at a higher risk for experiencing adverse psychosocial outcomes related to this misidentification (cf. Wright Karem et al., 2019 ). Consequently, there is an impetus for ensuring children from these linguistic groups receive unbiased assessments that document their unique communication profiles and capture the expected variations within their productions. In support of this outcome, researchers are beginning to advance efforts in computer assisted LSA by documenting the communication profiles of linguistically diverse children (see Overton et al., 2021 for a discussion on the Talk Bank Project and the Child Language Assessment Project). Such repositories of information will not only improve computer assisted LSA efforts, but they will also extend the evidence base available for informing the manual coding of transcriptions. To catalyse these efforts, research that examines children’s communication profiles through LSA measures across a variety of dialects and linguistic typologies is needed. Considerations for LSA measures When considering LSA, there are several available measures that have been used in clinical and research settings to explore different features of language production. There is not a current consensus, however, regarding which LSA metrics are most appropriate for use with a range of culturally and linguistically diverse children. None the less, there are several LSA measures that have been used throughout research with speakers from different typological backgrounds that highlight important considerations when applying different LSA approaches. Information on each LSA measure and research regarding their implementation with various linguistic profiles are provided in the following sections. Mean length of utterance Mean Length of Utterance (MLU; Brown, 1973 ) is perhaps the most commonly applied form of LSA ( Finestack & Satterlund, 2018 ). At its essence, MLU measures children’s grammatical complexity by considering utterance length, which is impacted by different morphosyntactic features ( Baron et al., 2018 ; Rice et al., 2010 ). For children who use MAE the inclusion of bound morphemes and other grammatical features that affect utterance length, such as the inclusion of auxiliary verbs, MLU is a relatively stable assessment. However, since MLU was designed for and with MAE speaking children, it often penalises the productions of children for whom certain morphosyntactic features are not obligatory. This is particularly problematic when using MLU as a diagnostic method without careful consideration for linguistic variations. In addition to diagnostic purposes, MLU has also been reported as being used for monitoring language outcomes or children’s progress over time ( Rice et al., 2010 ). However, findings from a recent study in the Jamaican context indicate that using MLU for this purpose may be more limited ( Kokotek et al., 2022 ). These mixed results suggest that there is a potential risk for MLU to be inadvertently misused to monitor linguistic assimilation rather than grammatical complexity with non-MAE speakers. Hence, more evidence about the linguistic profiles of children from diverse backgrounds is needed. For children who use AAE, bound morphemes such as those indicating pluralisation or third-person singular are not compulsory (see Charity, 2008 for an overview on features of AAE). For example, as described by Charity (2008) regarding unmarked pluralisation ‘fifty cents’ can be used as ‘fifty cent’ (p. 37). Comparably, children who speak JE may also have similar variations in their oral language due to the presence of cross-linguistic interactions where JC has been documented to influence JE productions at an average rate of 28% ( Wright Karem et al., 2022 ). To address these potential limitations, researchers have begun dichotomising MLU so that it measures grammatical complexity in utterance length using morphemes (i.e. MLU-m) and words (i.e. MLU-w). While MLU-m reflects what is traditionally understood as MLU, the inclusion of MLU-w has been offered as an alternative for considering non-MAE dialects and bilingual typologies as it is more responsive to differences in morphological rules ( Baron et al., 2018 ; Gutierrez-Clellen et al., 2012 ; Kokotek et al., 2022 ). Despite these benefits, the use of MLU-w has not been empirically established as a solution for these concerns ( Overton et al., 2021 ) and additional research is needed for understanding how MLU-m and MLU-w contribute to the characterisation of linguistically diverse speakers. The index of productive syntax Unlike MLU-m or MLU-w, the Index of Productive Syntax (IPSyn; Altenberg et al., 2018 ) does not measure grammatical complexity by utterance length. Rather, IPSyn evaluates children’s grammatical complexity based on the types of phrase structures used within a sample. With 56 different structures under consideration, an IPSyn analysis results in an IPSyn Total score as well as four sub-scale scores: Noun Phrases (NP; n = 11), Verb Phrases (VP; n = 16), Questions/Negations (QN; n = 10) and Sentence Structures (SS; n = 19). With IPSyn calculations based on the occurrence of phrase structures and not requiring the use of certain grammatical features ( Washington et al., 2019 ) there is a growing body of research exploring its utility with linguistically diverse children. Stated differently, the emphasis on documenting phrase structures when using IPSyn, may make this tool more sensitive to variation in dialect or linguistic typology as it is not dependent on the presence of certain bound morphemes or auxiliaries for calculation. Since IPSyn has been described as a relatively ‘dialect-fair’ assessment ( Overton et al., 2021 ) that does not vary based on maternal education ( Oetting et al., 2010 ), it is growing in popularity as a measure for use in diverse contexts (e.g. Washington et al., 2019 ) This has led to several studies exploring the applicability of IPSyn for investigating topics pertaining to non-MAE ( Stockman et al., 2016 ) and bilingual contexts (e.g. Chinese: Ooi & Wong, 2012 ; Jamaican Creole: Washington et al., 2019 ). Some psychometric evidence exists in favour of using IPSyn with children who speak AAE ( Oetting et al., 2010 ). However, this past work has primarily considered IPSyn Total scores ( Stockman et al., 2016 ), meaning further exploration of the applicability of IPSyn sub-scales for use with AAE speaking children is still needed. IPSyn has also been shown to provide similar results for both MAE and AAE speaking children (see Overton et al., 2021 ), but findings regarding the QN sub-scale in particular need more clarity ( Yang et al., 2022 ). Psychometric evidence has also been provided for using the IPSyn with Jamaican children in both of their spoken languages (i.e. JE and JC) by examining relationships between languages and the associations between IPSyn measures and other established LSA metrics (i.e. MLU-m and Number of Different Words; NDW). This research has since been expanded to document the occurrence of cross-linguistic interactions in Jamaican children’s language use ( Wright Karem et al., 2022 ), but not unlike other studies using IPSyn, more information is needed regarding the QN sub-scale ( Washington et al., 2019 ). Developmental sentence scoring Developmental Sentence Scoring (DSS; Lee, 1974 ) is another form of LSA that measures grammatical complexity. Instead of calculating a score based on the occurrence of phrase structures, DSS is designed to evaluate production accuracy. More specifically, the DSS examines sentence structure and applies a point system to assess the grammaticality of utterances ( Overton et al., 2021 ). The use of DSS has been featured more in the analysis of MAE as the scoring system is based on MAE syntax and it has therefore not historically been considered for use in diverse contexts ( Oetting, 2005 ; Washington et al., 2019 ). However, recent investigations have started to consider the use of DSS as an LSA for non-MAE dialects (i.e. AAE), and even for bilingual speakers. In part, this is likely occurring because although DSS is based on MAE, whether an utterance is scored as grammatical or ungrammatical is ultimately demarcated by the transcriber. In theory, if the transcriber can recognise whether structures are accurate or inaccurate based on the linguistic variety presented, then DSS should provide an accurate and informative description of children’s morphosyntactic development. In fact, in a recent clinical focus provided by Overton et al. (2021) , they explored the applicability of DSS for use with AAE speaking children. Their findings revealed that DSS does not appear to bias against AAE speaking children and therefore provides a variety-fair assessment of grammatical development. To date, it does not appear that DSS has been investigated for its use with bilingual speakers but given its promising applicability for use with AAE speaking children, it may provide new insights for bilingual Jamaican children who speak a variety of English (i.e. JE) as one of their languages. Measures of lexical diversity In addition to measures of grammatical complexity, measures of lexical diversity also play an important role when documenting the communication profiles of linguistically diverse children. As indicated by Charest et al. (2020) , different measures of lexical diversity can be used for different reasons, and thus more research is needed to understand which measures are most appropriate for a given context. Historically though, measures of lexical diversity have been used as an indicator for identifying DLD in young children, but more recent research suggests that children with DLD have deficits that are not captured within measures of lexical diversity when compared to their typically developing peers ( Charest & Skoczylas, 2019 ), potentially exasperating the issue of the misdiagnosis of DLD for linguistically diverse children (i.e. under-identification). Adding to these challenges is the ways in which measures of lexical diversity, and more specifically vocabulary size, have been used for describing the communication profiles of children from low-socioeconomic (SES) backgrounds that have emphasised reduced vocabulary sizes in this population. However, not only do these measures generally not take into consideration the use of words or non-verbal communication strategies that are culturally relevant, but the research in this area also often inaccurately conflates low-SES with the use of AAE (see Lai & Schwanenflugel, 2016 for a discussion). As such, more research is needed about using LSAs for measures of lexical diversity so that strengths-based approaches for documenting communication differences can be readily incorporated when completing comprehensive and holistic assessments for DLD for children from a range of backgrounds. For children from bilingual backgrounds who speak English (e.g. JE-speaking children) a parallel challenge also exists as they are more likely to have their vocabulary measured in English only instead of having their vocabulary skills measured based on both of their spoken languages (see Wright Karem et al., 2022 for strategies on conceptual scoring). For children who speak JE, or who come from similar cultural backgrounds, descriptive communication is considered inextricably included within the oral communication style ( Washington et al., 2019 ). By nature of this communication style, it is reasonable to suggest that lexical diversity may be an area of strength for these bilingual speakers when historically it has been under-valued when viewed without regard for children’s linguistic backgrounds. Computer-assisted LSA The time required to transcribe and calculate different LSA measures is consistently reported as a key limitation by researchers and clinicians ( Finestack & Satterlund, 2018 ; Finestack et al., 2020 ; Long et al., 2001 ). This is especially true for IPSyn and DSS calculations, where even experienced coders have been noted to require anywhere from 60 to 75 minutes to complete the coding and calculations for either IPSyn or DSS for a single transcript ( Long et al., 2001 ; Overton et al., 2021 ). To calculate both IPSyn and DSS scores for a single transcript would require a minimum of 120 min for someone well-versed in the coding process, which does not include the time required to collect and transcribe the sample or calculate other measures. Recent advancements in computerised LSA help reduce the burden of calculating multiple LSA measures. Although transcription support still demonstrates room for growth (albeit paid outsourcing options are also available and automatically generated transcripts are improving), computer-assisted LSA is now more accessible than ever ( Finestack et al., 2020 ; Pezold et al., 2020 ). Perhaps, the most widely known and commonly used computer-assisted LSA program is the Systematic Analysis of Language Transcripts (SALT; Miller & Iglesias, 2010 ). Users involved in the coding process must be aware of variations, and linguistic patterns expected across different dialects of English and typologies to ensure samples are accurately marked. Although SALT does provide scores for several LSA measures, it does not provide scores for IPSyn or DSS, which as mentioned previously are probably the two most time-consuming LSA measures to calculate by hand. In recent years, however, the analysis of IPSyn and DSS has been made considerably more feasible through the program known as Computerized Language Analysis (CLAN; MacWhinney, 2000 ). To enhance efficiency, it is possible to convert .slt files from SALT into .cha files using the ‘SALT2CHAT’ command (see Finestack et al., 2020 for a tutorial) so that CLAN can readily read through files that were initially coded in SALT. With the ‘SALT2CHAT’ command, researchers and clinicians can evaluate language samples for LSA measures that are unavailable in SALT such as IPSyn and DSS, effectively streamlining this process for those who previously transcribed and coded data within SALT. The current study The current evidence-base on LSA measures is informed by samples of MAE-speaking children, with each presented LSA measure offering different insights about children’s language productions. By building profiles for each of these children we can improve our understanding about how these children may perform on each of these assessments and by extension contribute to the expansion of evidence-based communication profiles that can be used to inform future efforts. In addition, the current study also includes a collective comparison providing needed insights about which LSA measures might be the most informative for assessing the language productions of children who speak different variations of English (i.e. AAE and JE) or who have linguistic development that differs from the monolingual standard (i.e. bilingual). This study, therefore, not only investigates the efficacy of using LSA measures to garner an understanding of the communication profiles for children who speak diverse dialects of English but to also explore how individual LSAs reflect linguistic differences across dialects. For example, it is possible that the IPSyn may be more appropriate than MLU when assessing children who speak dialects that use morphemes and auxiliaries in a markedly different way than mainstream English. To summarise, the following research aims were addressed. To investigate how children speaking different dialects of English perform on different LSA measures. To expand the evidence base of communication profiles available for informing children’s expected performance on different LSA measures. To explore if LSA measures differ across linguistic contexts for children who speak different dialects of English while also considering monolingual and bilingual typologies. Materials and methods Study approval Ethical approval was obtained from the Institutional Review Board at the University of Cincinnati and the Research Ethics Board, University of Toronto. Parents provided written consent and each child provided verbal assent. Data sources This investigation included data from two larger parent studies. Data for one child was obtained from the Jamaican Creole Language Project (cf. Washington et al., 2019 ). Data for two other children were pulled from the study on Imaging Language Function in 4-to-5-year-olds – Phase 3. Two of the children were assessed by examiners who matched their cultural and linguistic backgrounds. The AAE-speaking child was not able to be matched with an examiner from the same background and thus the examiner spent additional time visiting the child’s school and engaging in rapport building activities prior to conducting the play-based language sample (implications are discussed below). Play-based language samples were collected during each study from participants to address respective research goals unique to those individual studies. All language sample elicitations were completed with the child and the examiner, were 15 min in length and included the same or similar materials (i.e. large dollhouse, miniature play furniture and a collection of small dolls representing different racial backgrounds; see Washington et al., 2019 for a detailed description of the language sample process). This allowed for recordings of children’s language samples to be extracted and analysed for the present investigation. LSA computation The SALT program was used in the initial analysis of language samples. The SALT program provided scores across participants for MLU-m, MLU-w, Total Number of Words (TNW), NDW and Type-Token Ration (TTR). The CLAN program, however, was used to calculate IPSyn and DSS scores. This required each of the SALT (i.e. .slt) files to be converted for analysis in CLAN. The ‘SALT2CHAT’ conversion was used to convert .slt files into .cha files that could be read into the CLAN program. Once files were converted, they were analysed in CLAN for IPSyn using ‘ ipsyn +t*CHI −leng ’ and for DSS using ‘ dss +t*CHI +le +e ’ (see Finestack et al., 2020 for a tutorial and in-depth description of this process). Reliability As indicated above, participants in the present study represent a subset of data extracted from two larger investigations, each including their own reliability process. For data included from the Jamaican Creole Language Project reliability for LSA measures has been reported at >90% agreement for inter-rater reliability (see Washington et al., 2019 for a detailed description). For data included from the Imaging Language Function in 4-to-5-year-olds study, inter-rater agreement was completed for 20% of samples collected to date, resulting in >90% agreement across LSA measures. Participants A converging evidence approach ( Castilla-Earls et al., 2016 , 2020 ) was used to determine if all three children included in the present study were indeed typically developing. This two-source approach was also used to identify the typological background for each child. In addition, each child met specified inclusion criteria: (a) were between 4; 0 and 4; 11 [years; months]; (b) passed a binaural hearing screening at 25 dB for 1, 2 and 4 kHz; (c) parents reported no history of neurological or pervasive developmental disorders; and (d) child demonstrated average nonverbal intelligence as indicated by either the Primary Test of Nonverbal Intelligence ( Ehrler & McGhee, 2008 ) or the Kaufman Assessment Battery for Children – 2nd Edition Normative Update ( Kaufman, 2018 ). A case-based approach for considering LSA across diverse linguistic contexts To capture how the details offered by LSA contribute to the linguistic profiles of diverse children and to subsequently compare LSA results, it is necessary to understand the cultural and linguistic background of each child. Thus, we present three preschool cases spanning three different cultural and linguistic experiences present in the USA. Pseudonyms, Tommy, Sarah and Daniel are used for each of the children, and additional details are summarised in Table 1 . Importantly, each participant came from a community where the majority of people in their immediate environment spoke the same dialect of English as the child. Tommy and Sarah also completed the Diagnostic Evaluation of Language Variation (DELV; Pearson et al., 2014 ). The results of the DELV confirmed Tommy as an MAE speaker who is at the lowest risk of having a language impairment and Sarah as a speaker of strong variation from MAE while also being at the lowest risk of having a language impairment. As part of the Jamaican Creole Language Project, DELV was not an included assessment but based on parent and teacher report, converging evidence confirms Daniel is a bilingual speaker of both JE and JC. Therefore, Daniel completed language samples in both JE and JC, but only the JE sample is included in the present study (with consideration for language transfer from JC). Table 1. Participant demographics and language details. Child Age Race/Ethnicity Location Dialect(s) Spoken Language Typology Tommy 4;5 White/Non-Hispanic Cincinnati, OH MAE Monolingual Sarah 4;7 Black/African American Cincinnati, OH AAE Monolingual Daniel 4;4 Black/Jamaican New York, NY JE * Simultaneous Bilingual Open in a new tab All names are pseudonyms; OH = Ohio; NY = New York; MAE = Mainstream American English; AAE = African American English; JE = Jamaican English; * = this child also speaks Jamaican Creole, but only his JE language sample was considered for this study. Data analysis A comprehensive LSA consisting of the aforementioned measures was used to develop linguistic communication profiles across three different varieties of English for each of the children. Linguistic communication profiles documenting MLU-m, MLU-w, IPSyn Total and subscale scores, DSS and measures of lexical diversity (i.e. TNW, NDW, TTR) are now available for children who speak three different dialects of English inclusive of monolingual and bilingual typologies, providing needed documentation for the consideration of LSA measures across diverse contexts. In addition to expanding the evidence available for documenting LSAs across different varieties of English, children’s scores were also compared to explore the efficacy of using each measure. Due to the small sample size in the present study, a non-parametric analysis was employed using the IBM Statistical Program for the Social Sciences (SPSS) version 28 (IBM, 2020). The Kruskal–Wallis H test was implemented as it is considered the non-parametric equivalent of Analysis of Variance that can be used when conditions such as normality assumptions or minimum sample size are not met. Although the Kruskal–Wallis H test is not without its limitations (as is the case for all non-parametric analyses), it provides important preliminary information for understanding the efficacy of LSA measures in this study and offers additional insights about the direction for future research. Results Descriptive patterns and preliminary results are described in detail below with patterns available in Table 2 . Table 2. LSA results across dialects. Child MLU-m MLU-w TNW NDW TTR DSS IPSyn N IPSyn V IPSyn QN IPSyn SS IPSyn Total Tommy 4.56 * 4.19 * 495 129 .26 11.8 * 11 17 4 20 * 52 Sarah 4.00 3.79 530 * 134 * .25 9.58 12 * 19 * 7 * 19 57 * Daniel 3.11 2.85 302 99 .33 * 7.10 12 * 14 1 13 40 Open in a new tab All names are pseudonyms; *=highest score across dialects; MLU-m = mean length utterance in morphemes; MLU-w = mean length utterance in words; TNW = Total Number of Words; NDW= Number of Different Words; TTR = Type-Token-Ration; DSS = Developmental Sentence Score; IPSyn – Index of Productive Syntax; N = Noun; V = Verb; QN = Question/Negation; SS = Sentence Structures; T = Total. Tommy Tommy, who is a monolingual MAE-speaking child, received the highest scores on MLU-m, MLU-w, DSS and IPSyn SS. He did not receive the lowest score on any LSA measure. Sarah Sarah, who is a monolingual speaker of AAE, received the highest scores on TNW, NDW, IPSyn NP, IPSyn VP, IPSyn QN and IPsyn Total. Although she received the lowest score for TTR, this was only slightly below that of her MAE-speaking peer. Daniel Daniel is a bilingual speaker of JE and JC, but as mentioned previously, only his JE language sample is under consideration for this study. Although Daniel received the lowest scores of his peers across most LSA measures, he performed the highest on TTR and IPSyn Noun. Moreover, his scores were only slightly below that of his peers on most measures, but IPSyn QN only registered one occurrence of this phrase structure. LSA comparisons As noted above, the different LSA measures documented contrastive and non-contrastive features across dialects spoken, with some children receiving higher scores across different areas. However, without further investigation, the relevance of these differences is unclear. Thus, the Kruskal–Wallis H test was applied to provide further insight and revealed no significant differences across LSA measures, χ 2 (2) = 2.00, p = 0.368. Discussion In this study, communication profiles of three typically developing 4-year-olds were explored to investigate the utility of LSA measures in assessing speakers of varying English dialects. More specifically, using a case-based approach with three 4-year-old children from the three previously described linguistic backgrounds (i.e. MAE, AAE, JE-Bilingual), this study offers an examination on the application of implementing LSA measures to characterise language productions across diverse speakers of the same language. By exploring the linguistic communication profiles for three child participants with different dialectal and typological backgrounds, this investigation offers new insights for informing clinical observations when using LSAs. The results from this study provide valuable insights about the use of LSA measures for documenting linguistic patterns across dialects of English that also reflect different typological backgrounds (i.e. monolingual/bilingual). By extension, the findings also serve to expand the evidence available for informing the linguistic communication profiles of diverse speakers. In turn, these findings offer some response to earlier calls (e.g. Overton et al., 2021 ), for developing evidence-based profiles that can be used to inform future research, for building repositories of information and for exploring the efficacy of LSA measures across a diverse range of speakers. This study also offers an in-depth examination on the application of LSA measures for characterising language use across diverse speakers of the same language. By exploring the linguistic communication profiles for three child participants with different dialectal and typological backgrounds, this investigation offers new insights for informing clinical observations when using LSAs. For example, the monolingual MAE-speaking child, Tommy, received the highest scores across most traditionally used measures of grammatical complexity (i.e. MLU-m and MLU-w). However, the monolingual AAE-speaking child, Sarah, received the highest scores in nearly all areas of IPSyn analysis and for most measures of lexical diversity. In addition, she also demonstrated the highest score for IPSyn QN, suggesting that more research for this subscale with AAE-speaking children is warranted ( Yang et al., 2022 ). Although these patterns suggest that the LSA measures included in the current study are relatively ‘dialect-fair’ the bilingual JE-speaking child consistently received lower scores across most LSA measures with exception TTR and IPSyn Noun. This suggests that some LSA measures that capture the descriptive nature of JE may highlight children’s strengths, while other measures of grammatical complexity may present lower for children where cross-linguistic influence is prevalent ( Wright Karem et al., 2022 ). The JE speaker, Daniel, is a bilingual speaker of both JE and JC with code-mixing expected (cf Wright Karem & Washington, 2021 ; Wright Karem et al., 2022 ). As such, the JE produced would also be influenced by features of JC such as the observation that bound morphemes are not included on root verbs. For example, Daniel’s scores were the lowest across LSAs, including MLU-m (2.85 JE, 4.00 AAE, 4.56 MAE), which relies on the consideration of bound and free morphemes (e.g. using ‘play’ for ‘played’ or ‘it’ for ‘it’s’). Table 2 highlights the differences in scores across all speakers, with the Appendix providing some production examples for each speaker. Alternatively, Daniel received the highest score on a measure of lexical diversity (TTR) which may capture the descriptive nature of the JE language (see Table 2 for an example) and highlights a strength in Daniel’s language skills. Thus, it is important to consider lexical diversity within this population and ensure that LSA measures intended for informing grammatical complexity are sensitive to cross-linguistic influence. Measures of lexical diversity When considering lexical diversity there are different aspects that need to be considered to appreciate the richness of children’s expressive vocabulary since both word density and word variety influence this construct. TNW for instance may reflect children’s lexical diversity differently depending on the condition of the language sample. For example, when children’s language samples are time-based and not matched for the number of utterances, TNW more strongly reflects children’s speaking rate rather than their degree of talkativeness ( Miller & Chapman, 1981 ). Alone, TNW scores have been reported as difficult to interpret ( Pezold et al., 2020 ), but when used in tandem with other measures, TNW can help provide insights that inform a more comprehensive understanding of children’s linguistic profiles. Another LSA that contributes to children’s lexical diversity is NDW ( Watkins et al., 1995 ). Unlike its token-based counterpart (i.e. TNW), NDW measures the number of different word types present in a language sample ( Miller & Chapman, 1981 ; Pezold et al., 2020 ). Although NDW is a measure that considers each of the unique words a child produces within a language sample ( Pezold et al., 2020 ), it can be influenced by sample length, with some scores reaching a ceiling effect as more words are repeated within longer samples ( Pezold et al., 2020 ). Challenges such as this underscore the need to include individual measures of lexical diversity in relation to other metrics to help provide a holistic representation of children’s communication. For example, the inclusion of TTR ( Templin, 1957 ) provides a means of capturing both word density and diversity. TTR has also been reported to ‘account for samples of varying length’ ( Pezold et al., 2020 , p. 105). This makes TTR especially useful for investigating time-based samples, despite it being influenced by longer sample lengths ( Owen & Leonard, 2002 ; Pezold et al., 2020 ). TTR also holds a high degree of clinical relevance as clinicians have previously reported primarily using TTR in their infrequent LSA protocols. In concert with the literature on lexical diversity, the findings by Finestack and Satterlund (2018) further highlight the need for including TTR when developing children’s linguistic profiles while also underscoring the importance of examining TTR within a broader perspective (see Finestack & Satterlund, 2018 for further discussion). The findings of this study are in line with this previous literature, with TTR being one of the only LSA measures that captured the strength of the JE speaking participant. This emphasises the importance for the careful interpretation of these scores, especially when evaluating a bilingual speaker in the English context. Subjectively, it is also important to note that each of the children included in this study appeared happy, open and comfortable during their play-based language sample, suggesting that reluctance to participate was not a likely factor in their performance. Implications for use of LSA measures The differences observed in linguistic patterns do not necessarily suggest that LSA measures are biased or insensitive in detecting intended linguistic features across dialects. Rather, variations in the individual children’s linguistic communication profiles did not rise to the level of statistical significance when examined collectively across LSA measures in this study. However, further analysis of these differences can offer relevant qualitative data on using LSA measures for dialectal speakers of mainstream English. Additionally, these differences may reveal patterns of statistical significance when explored in larger sample sizes taking various variables into account. As such, these preliminary findings offer clinically meaningful directions for future research. For example, previous research exploring the use of IPSyn analysis with bilingual Jamaican preschoolers showed it to be a promising tool for characterising grammatical productions, but limitations with the IPSyn QN were noted ( Washington et al., 2019 ). The results of the current study echo these findings suggesting that further research on the efficacy of the IPSyn QN is needed for bilingual Jamaican preschoolers. Prior efforts have also sought to explore the applicability of MLU-w as a means for calculating MLU in a way that does not penalise speakers for whom certain bound morphemes are non-obligatory ( Baron et al., 2018 ; Gutierrez-Clellen et al., 2012 ). This study both supports and opposes this previous research. Although there were no significant differences in children’s performance across LSA measures, this study found an observable hierarchy in performance on MLU-m and MLU-w with the MAE-speaking child receiving the highest scores, the AAE-speaking child receiving the next highest scores, and JE-speaking child receiving the lowest scores. We emphasise here that because there was no statistically significant difference, it is possible that these measures do not penalise the performance of non-MAE speakers, but we underscore the sentiment expressed by Overton et al. (2021) that modifications to MLU scoring may not be sufficient for addressing concerns. Likewise, this same score hierarchy was also observed for DSS, and although Overton et al. (2021) found DSS to be a dialect-fair assessment for AAE-speaking children, the presence of a hierarchal pattern (in any area) that favours MAE-speakers necessitates further inquiry when considering it for use in diverse contexts. In summary, the LSA measures illustrate differing linguistic patterns across dialects spoken. Some preliminary evidence is also now available for using LSA measures that have previously been under-investigated for use with diverse speakers. Specifically, the IPSyn demonstrated results that appear to be the least bias across speakers, although there were no significant differences in LSA scores. Limitations and future research The sample size included within the present study allowed for an insightful characterisation of children’s linguistic communication profiles using multiple LSA measures across dialects of English. However, while there are strengths to a Case Study approach, a limitation of the study was the small sample size. This limitation in sample size restricted the statistical evaluation to a non-parametric analysis that may have had limited power for detecting statistically significant differences. We note, however, that this was a preliminary study and future research that considers an expanded sample size of diverse participants is anticipated. Additionally, specific factors regarding SES and maternal education were not available for all participants; however, with this being a descriptive preliminary study other potential confounding variables (e.g. gender) were also not explored. None the less, future studies with larger sample sizes should consider the possible implications of other variables such as SES, gender, age, etc. If considering SES, it is especially important that future studies ensure children from a range of backgrounds are included within low-, mid- and high-SES groups. Another potential limitation of this study relates to the differences between the examiner and the AAE-speaking participant. Although members of the research team who are themselves speakers of AAE were involved in the larger transcription and coding process, the examiner was a White speaker who, although bidialectal (Appalachian English and MAE), was not a speaker of AAE. This limitation was addressed by providing opportunities for rapport building between the child and the examiner, and ensuring the examiner was a familiar presence within the child’s school. However, it is still possible that due to the observable difference in race and dialect that the child felt obligated to attempt code-switching, which may have resulted in lower LSA scores. Although this is a possibility, we note that the bilingual JE-Speaking child was evaluated by a bilingual JE-speaking adult and thus matched for race/ethnicity and linguistic background, but generally received scores below that of their AAE-speaking peer. While we recognise other factors that likely influenced the LSA results (i.e. the sensitive and specificity of the measures themselves), we mention the cultural and linguistic mismatch between the examiner and the AAE-speaking child as an area that should be addressed in future studies. We therefore recommend that future studies seeking to replicate this work construct their sampling condition so that there is cultural and linguistic alignment between the examiner and participant. By extension, this underscores the previously mentioned limitation regarding the primarily White monolingual demography of SLPs and highlights the impetus for supporting and expanding the diversification of the field ( ASHA, 2022 ; Hopf et al., 2021 ; Washington et al., 2023 ). Clinical implications and conclusions The present study offers some promising and preliminary evidence-based information for characterising the language use of diverse children, in particular, those who speak different dialects of English and who have different typological backgrounds. With this new information available, researchers and clinicians can be motivated in their efforts to inform linguistic communication profiles for children who speak these dialects of English while also exploring how these profiles may be observationally different. The evidence obtained regarding the utility of LSA across dialects of the same language offers necessary documentation as researchers and clinicians need resources useful in highlighting linguistic variations that help to reduce the potential for misdiagnosis and provide culturally responsive assessment that holistically represent children’s communication abilities. Our study also demonstrates that although non-statistically significant variations are present, some LSA measures may better capture certain skills across dialects. As the IPSyn analysis is growing in popularity and is increasingly more accessible thanks to automated software such as CLAN, these findings further demonstrate its usefulness as a mainstream analysis for diverse speakers. This is particularly beneficial for non-MAE speaking children as the IPSyn demonstrated efficacy and cultural responsivity across all dialects by revealing the presence of morphosyntactic structures across speakers. Such an observation offers important insights not only for the current study but also suggests the IPSyn analysis may be an innovative measure for evaluating language use cross-linguistically. Supplementary Material Appendix A NIHMS2077511-supplement-Appendix_A.pdf (69.7KB, pdf) Acknowledgments The authors would like to thank the families, children, and educators whose support made this study possible. We would also like to extend our gratitude to research team members Gabby Matz and Brittany Fletcher for their transcription and coding efforts, to Kereisha Biggs for her support as a research assistant, to our colleagues at the Jamaican Language Unit, and to Professors Laura and Richard Kretschmer for their ongoing support. Lastly, we would especially like to thank Jamie Linert who is a doctoral student at the University of Minnesota for her expert guidance on using CLAN. Funding This work was supported in part by the NIH – National Institute on Deafness and Other Communication Disorders (NIDCD) [7R21DC018170] as well as [5R01DC019337]. Footnotes Disclosure statement No potential conflict of interest was reported by the author(s). 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