Abstract Linear mixed effects models (LMEMs) are rapidly advancing as a candidate to replace ANOVA as a standard for inferential analyses in psycholinguistics and associated fields. However, because of the relative novelty of this approach, there are few clear standards regarding its correct use, as well as much uncertainty about whether it truly offers an advantage over traditional approaches. In this paper, we argue that many of the traditional standards in accounting for observational dependencies in the design also apply to the correct use of LMEMs. We argue that valid statistical inferences using LMEMs require maximal random-effects structures wherever possible-that is, including condition-specific random effects by subjects/items for every fixed effect of theoretical interest that is measured in mor
