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Modeling Intensive Polytomous Time-Series Eye-Tracking Data: A Dynamic Tree-Based Item Response Model

Published online by Cambridge University Press:  01 January 2025

Sun-Joo Cho ,
Sarah Brown-Schmidt ,
Paul De Boeck  and
Jianhong Shen
Sun-Joo Cho*
Affiliation:
Vanderbilt University
Sarah Brown-Schmidt
Affiliation:
Vanderbilt University
Paul De Boeck
Affiliation:
The Ohio State University KU Leuven
Jianhong Shen
Affiliation:
Vanderbilt University
*
Correspondence should be made to Sun-Joo Cho, Vanderbilt University, Nashville, USA. Email: sj.cho@vanderbilt.edu; http://www.vanderbilt.edu/psychological_sciences/bio/sun-joo-cho
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Abstract

This paper presents a dynamic tree-based item response (IRTree) model as a novel extension of the autoregressive generalized linear mixed effect model (dynamic GLMM). We illustrate the unique utility of the dynamic IRTree model in its capability of modeling differentiated processes indicated by intensive polytomous time-series eye-tracking data. The dynamic IRTree was inspired by but is distinct from the dynamic GLMM which was previously presented by Cho, Brown-Schmidt, and Lee (Psychometrika 83(3):751–771, 2018). Unlike the dynamic IRTree, the dynamic GLMM is suitable for modeling intensive binary time-series eye-tracking data to identify visual attention to a single interest area over all other possible fixation locations. The dynamic IRTree model is a general modeling framework which can be used to model change processes (trend and autocorrelation) and which allows for decomposing data into various sources of heterogeneity. The dynamic IRTree model was illustrated using an experimental study that employed the visual-world eye-tracking technique. The results of a simulation study showed that parameter recovery of the model was satisfactory and that ignoring trend and autoregressive effects resulted in biased estimates of experimental condition effects in the same conditions found in the empirical study.

Keywords

autocorrelationeye-tracking datageneralized linear mixed effect modelintensive polytomous time seriesmultinomial processing treetree-based item response modeltrend

Information

Type
Application Reviews and Case Studies (ARCS)
Information
Psychometrika , Volume 85 , Issue 1 , March 2020 , pp. 154 - 184
Copyright
Copyright © 2020 The Psychometric Society

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Footnotes

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11336-020-09694-6) contains supplementary material, which is available to authorized users.

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