Modeling Intensive Polytomous Time-Series Eye-Tracking Data: A Dynamic Tree-Based Item Response Model.
Published in Psychometrika, 2020
Recommended citation: Cho S.-J., Brown-Schmidt S., De Boeck P., Shen J. (2020). Modeling Intensive Polytomous Time-Series Eye-Tracking Data: A Dynamic Tree-Based Item Response Model. Psychometrika, 85, 154–184. doi:10.1007/s11336-020-09694-6
https://doi.org/10.1007/s11336-020-09694-6
This paper presents a dynamic generalized linear mixed effects model (GLMM) approach to modeling a multinomial processing tree for intensive polytomous time series eye tracking data. The dynamic multinomial processing tree GLMM was illustrated using an experimental study that employed the visual world eye tracking technique.