Control Predictors in a (Maximal) GLMM

Christoph Scheepers (University of Glasgow)
Control Predictors in a (Maximal) GLMM

Since psycholinguistic experiments often require control of potential confound variables (e.g. lexical frequency in a word-recognition experiment), the last session will specifically focus on how to handle control predictors (sometimes referred to as covariates) in a maximal GLMM. I will present results from data simulations showing that simple ‘matching’ of confound variables between, say, different groups of items in the stimulus set is often not enough to avoid anticonservative inferences. Using appropriate examples, I will illustrate how to tackle this problem while at the same time keeping model complexity at a tolerable level to avoid convergence problems.

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