Expository and narrative discourse summary statistics and demographic information for adolescents with and without traumatic brain injury

Purpose: Generalized linear mixed models (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 (LSA), 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 (TD). Methods:
Language samples were collected from 55 adolescents ages 13-18, 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 TD), 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 TD, substantial differences are detected in productivity
and lexical diversity, while differences in syntactic complexity are more
moderate. Females exhibited greater syntactic complexity, while males
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 LSA.