Bayesian hierarchical growth curve methods with applications in linguistics and audiological sciences
Creators: name: Sorensen, Eldon
Publisher: name: University of Iowa
Year: 2024
Subjects: Bayesian
Heterogeneous Variance
Item Response Theory
Longitudinal
Mixed Effects Model
Psycholinguistics

description: This dissertation focuses on the prospects of Bayesian hierarchical modeling as an alternative to traditional statistical modeling techniques in the field of hearing science. In this field, there is a continued motivation to link how hearing, and by extension hearing loss, is associated with the development of cognitive, communicative, and academic abilities.
description: In one aspect, Bayesian hierarchical modeling is explored in the paradigm of psychophysics and psychometrics as applied to hearing science. In these fields, an integral method to the disciplines involves charting how a person’s response pattern changes according to a continuum of stimuli. For instance, in hearing science, Visual Analogue Scaling (VAS) tasks are experiments in which listeners hear sounds across a speech continuum and give a numeric rating between 0 and 100 conveying whether the sound they heard was more like word “a” or more like word “b” (i.e., each participant is giving a continuous categorization response (CCR)). By taking all the CCRs across the speech continuum, a parametric curve model can be fit to the data and used to analyze any individual’s response pattern by speech continuum. Standard statistical modeling techniques are not able to accommodate all of the specific requirements needed to analyze these data. Thus, Bayesian hierarchical modeling techniques are proposed in order to accommodate group-level non-linear curves, individual-specific non-linear curves, continuum-level random effects, and a subject-specific variance that is predicted by other model parameters. These techniques are employed both in a cross-sectional setting analyzing a VAS study consisting of mono-lingual and bi-lingual participants and in a longitudinal setting exploring the Growing Words project, a 4-year longitudinal study tracking reading and language development in children in the Iowa City / Cedar Rapids area. Overall, the model results generally highlighted the negative association between individual slope and variance, suggesting those persons who had a more swift transition in response across the speech continuum were more consistent in their responses across trials.
description: In another aspect, Bayesian modeling techniques are explored in the context of hearing status affecting academic outcomes. This is motivated by a study of adolescents across primary school with various degrees of hearing loss whom were observed in order to map the potential linkage between hearing difficulty and academic outcomes. As part of the study, participants were evaluated across multiple testing items meant to assess reading comprehension and word reading ability. While this study lends itself well to an item response theory (IRT) modeling approach, standard implementations of IRT modeling are unable to handle all the statistical considerations of the study, which includes, correlated latent abilities, participants being measured across time, testing items being applied at different rates across time, and a non-linear latent growth trend over time. Thus, a Bayesian hierarchical model using the two-parameter logistic function as a framework is proposed which can adequately accomplish all the goals of the study. Overall, negative associations between reading, both general comprehension and word-level ability, and better ear pure tone average were witnessed.