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 communicationsciences. Usinglanguage sample analysis, we demonstrate the utility of these methods in answering specific...
| Publicado en: | Journal of Speech, Language & Hearing Research Vol. 64; no. 4; pp. 1256 - 1271 |
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| Autores principales: | , , |
| Formato: | Artículo |
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American Speech-Language-Hearing Association
Apr2021
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=149813923&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 149813923 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10924388 1SM jtl: Journal of Speech, Language & Hearing Research issn: 10924388 maglogo: N pubinfo: dt: Apr2021 vid: 64 iid: 4 pid: 42 pub: American Speech-Language-Hearing Association artinfo: ui: 149813923 10.1044/2020_JSLHR-20-00471 ppf: 1256 ppct: 15 formats: fmt: @attributes: type: P size: 1.3MB tig: atl: Bayesian Generalized Linear Mixed-Model Analysis of Language Samples: Detecting Patterns in Expository and Narrative Discourse of Adolescents With Traumatic Brain Injury. aug: au: Collins, Gavin Lundine, Jennifer P. Kaizar, Eloise affil: Department of Statistics, The Ohio State University, Columbus. Department of Speech & Hearing Science, The Ohio State University, Columbus. Division of Clinical Therapies and Inpatient Rehabilitation Program, Nationwide Children’s Hospital, Columbus, OH. su: Comparative grammar Vocabulary Adolescence Brain injuries Phonological awareness Narratives Speech evaluation Video recording sug: subj: Comparative grammar Vocabulary Adolescence Brain injuries Phonological awareness Narratives Speech evaluation Video recording ab: 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 communicationsciences. Usinglanguage 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: Languagesampleswerecollectedfrom55adolescents 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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