Abstract

Abstract Linear mixed-effects models (LMEMs) have become increasingly prominent in psycholinguistics and related areas. However, there is currently little understanding of how different random effects structures affect generalizability. Here, we argue that researchers using LMEMs for confirmatory hypothesis testing should minimally adhere to the standards that have been in place for many decades. Through theoretical arguments and Monte Carlo simulation, we show that LMEMs generalize best when they include the maximal random effects structure justified by the design. In contrast, LMEMs including the maximal random