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Autoregressive Generalized Linear Mixed Effect Models with Crossed Random Effects: An Application to Intensive Binary

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This study introduces an autoregressive generalized linear mixed effect model (GLMM) to address limitations in psycholinguistics research. The new model accurately accounts for serial dependence in person and item effects, improving analysis of time-series data.

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Area of Science:

  • Psycholinguistics
  • Statistical Modeling
  • Cognitive Science

Background:

  • Generalized linear mixed effect models (GLMMs) with crossed random effects are common in psycholinguistics.
  • These models face limitations in handling serial dependence across participants and items.
  • Lag effects, or the influence of previous responses, are often overlooked.

Purpose of the Study:

  • To present a novel autoregressive GLMM with crossed random effects.
  • To account for variability in lag effects across participants and items.
  • To improve the analysis of intensive binary time series data, such as eye-tracking data.

Main Methods:

  • Development of an autoregressive GLMM incorporating crossed random effects.
  • Application of the model to binary time series eye-tracking data.
  • Conducting a simulation study to evaluate model performance and the impact of ignoring lag effects.

Main Results:

  • The proposed autoregressive GLMM effectively handles serial dependence in psycholinguistic data.
  • The model allows for the detection of experimental condition effects while controlling for previous responses.
  • Simulation results indicate that ignoring lag effects can lead to biased estimates and underestimated standard errors.

Conclusions:

  • The autoregressive GLMM offers a more accurate approach for analyzing psycholinguistic time series data.
  • Accounting for lag effects is crucial for obtaining reliable estimates of experimental effects.
  • This methodology enhances the analysis of eye-tracking and similar intensive longitudinal data.