DOI: 10.5061/dryad.v15dv41v8
title: Expository and narrative discourse summary statistics and demographic information for adolescents with and without traumatic brain injury

description: 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.
description: 55 subjects, age 13-18 years, 52% female, of varying
 socioeconomic status (SES; measured using data from a census-track)
 verbally summarized three short lectures about a fictional nation called
 “Lifeland.” One of the lectures was compare-contrast (CC), one was
 cause-effect (CE), and the other was a story, i.e. a narrative
 (N). The lectures were presented in a random order for each subject, via
 video on a computer monitor. The subjects summarized the lectures
 immediately after viewing each one. Each lecture was written at about the
 same reading level, and contained about the same number of main/supporting
 ideas, sentences, and words. Each lecture was read in front of the same
 neutral background, by the same speaker. The CC and CE lectures were of an
 expository nature, while the N lecture was narrative. Five adolescents had
 experienced a traumatic brain injury (TBI) meeting certain specifications,
 while the remaining 50 subjects had a history of typical development
 (TD). The audio and video of each discourse sample was
 recorded, and later transcribed using Systematic Analysis of Language
 Transcripts (SALT) software. SALT was also used to record the
 microstructural discourse statistics of interest. Each discourse summary
 contained a number of utterances; each utterance consists of an
 independent clause and its accompanying dependent clauses; and each clause
 is made up of a number of words. With this structure in mind, for each
 discourse (CC, CE, N) delivered by each participant (1, …, 55), we
 recorded four statistics that together provide an informative picture of
 the discourse as a whole: the total number of utterances
 (<i>U</i><i>)</i>, clauses (<i>C</i>),
 words (<i>W</i>), and distinct words (<i>D</i>)
 spoken by each participant, giving each lecture. Proper
 checking was done to ensure accurate transcription and recording of
 relevant variables, as described in the corresponding
 manuscript. 
description: The dataset is a .csv file consisting of 10 columns:
 "subject" is the number assigned to each of the adolescents in
 the study, 1,...,55; "lecture_type" is
 either <em>cc</em> (compare-contrast), <em>ce</em> (cause-effect), or <em>n</em> (narrative), and each of the 55 subjects have a row for each lecture type; "development_type" is collected at the subject level, and is either <em>TD </em>(typically developing) or <em>TBI</em> (traumatic brain injury); "sex," (Male/Female) "age," (13-19) and "ses" (a summary of socioeconomic status; a standardized "z-value") are also collected at the subject level; "U" (&gt;=1) is the total number of utterances in the discourse; "C" (&gt;=U) is the total number of clauses in the discourse; "W" (&gt;=C) is the total number of words in the discourse; and "D" (&lt;=W) is the total number of distinct words in the discourse.