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Autoregressive Generalized Linear Mixed Effect Models with Crossed Random Effects: An Application to Intensive Binary Time Series Eye-Tracking Data

Published online by Cambridge University Press:  01 January 2025

Sun-Joo Cho ,
Sarah Brown-Schmidt  and
Woo-yeol Lee
Sun-Joo Cho*
Affiliation:
Vanderbilt University
Sarah Brown-Schmidt
Affiliation:
Vanderbilt University
Woo-yeol Lee
Affiliation:
Vanderbilt University
*
Correspondence should be made to Sun-Joo Cho, Vanderbilt University, Nashville, TN, USA. Email: sj.cho@vanderbilt.edu; URL: http://www.vanderbilt.edu/psychological_sciences/bio/sun-joo-cho
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Abstract

As a method to ascertain person and item effects in psycholinguistics, a generalized linear mixed effect model (GLMM) with crossed random effects has met limitations in handing serial dependence across persons and items. This paper presents an autoregressive GLMM with crossed random effects that accounts for variability in lag effects across persons and items. The model is shown to be applicable to intensive binary time series eye-tracking data when researchers are interested in detecting experimental condition effects while controlling for previous responses. In addition, a simulation study shows that ignoring lag effects can lead to biased estimates and underestimated standard errors for the experimental condition effects.

Keywords

eye-tracking datageneralized linear mixed effect modelintensive binary time series datarandom item effect
Type
Original Paper
Information
Psychometrika , Volume 83 , Issue 3 , September 2018 , pp. 751 - 771
Copyright
Copyright © The Psychometric Society 2018

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