Autoregressive Generalized Linear Mixed Effect Models with Crossed Random Effects: An Application to Intensive Binary Time Series Eye-Tracking Data.
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.
['Cho|Sun-Joo|SJ|', 'Brown-Schmidt|Sarah|S|', 'Lee|Woo-Yeol|WY|']
D003198:Computer Simulation D053483:Eye Movement Measurements D006801:Humans D016014:Linear Models D011578:Psycholinguistics D011594:Psychometrics D013997:Time Factors
generalized linear mixed effect model eye-tracking data intensive binary time series data random item effect