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

Sun-Joo Cho (), Sarah Brown-Schmidt and Woo-yeol Lee
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Sun-Joo Cho: Vanderbilt University
Sarah Brown-Schmidt: Vanderbilt University
Woo-yeol Lee: Vanderbilt University

Psychometrika, 2018, vol. 83, issue 3, No 11, 771 pages

Abstract: 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 data; generalized linear mixed effect model; intensive binary time series data; random item effect (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11336-018-9604-2

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