Journal article
A Bayesian hierarchical model for the analysis of visual analogue scaling tasks
Statistical methods in medical research, Vol.33(6), pp.953-965
04/04/2024
DOI: 10.1177/09622802241242319
PMID: 38573790
Abstract
In psychophysics and psychometrics, an integral method to the discipline involves charting how a person's response pattern changes according to a continuum of stimuli. For instance, in hearing science, Visual Analog Scaling 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). By taking all the continuous categorization responses 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 employed 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. In this paper, a Bayesian hierarchical model is constructed to model the data from a Visual Analog Scaling task study of mono-lingual and bi-lingual participants. Any nonlinear curve function could be used and we demonstrate the technique using the 4-parameter logistic function. Overall, the model was found to fit particularly well to the data from the study and results suggested that the magnitude of the slope was what most defined the differences in response patterns between continua.
Details
- Title: Subtitle
- A Bayesian hierarchical model for the analysis of visual analogue scaling tasks
- Creators
- Jacob Oleson - University of IowaEthan Kutlu - University of IowaBob McMurray - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Statistical methods in medical research, Vol.33(6), pp.953-965
- DOI
- 10.1177/09622802241242319
- PMID
- 38573790
- eISSN
- 1477-0334
- Grant note
- DOI: 10.13039/100000001, name: National Science Foundation, award: 2104015; DOI: 10.13039/100000002, name: National Institutes of Health, award: DC00242, DC008089, DC019081
- Language
- English
- Electronic publication date
- 04/04/2024
- Academic Unit
- Communication Sciences and Disorders; Linguistics; Psychological and Brain Sciences; Iowa Neuroscience Institute; Biostatistics; Center for Social Science Innovation; Otolaryngology
- Record Identifier
- 9984580297702771
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