Analyzing individual differences in sentence processing performance using multilevel models

The use of multilevel models is increasingly common in the behavioral sciences for analyzing hierarchically structured data, including repeated measures data. These models are flexible and easily implemented via a variety of commercially available statistical software programs. We consider their application in the context of an eye-movement experiment testing readers' responses to a semantic plausibility manipulation in temporarily ambiguous sentences. Multilevel models were used to study the relationship between working memory capacity and the extent to which readers were disrupted by syntactic misanalysis. This represented a cross-level interaction between an individual difference measure and a sentence-level characteristic. We compare a multilevel modeling approach to a standard approach based on ANOVA.