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.
Keywordseye-tracking data generalized linear mixed effect model intensive binary time series data random item effect
We thank Dr. Paul De Boeck (Ohio State University and KU Leuven) for comments on an earlier draft and the reviewers for their constructive comments that have led to improvement on the first version of this paper. Funding The original data collection and this work were supported in part by National Science Foundation Grants BCS 12-57029 and BCS 15-56700 to Sarah Brown-Schmidt.
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