Contrast coding choices in a decade of mixed models

•Contrast coding in regression models, including mixed-effect models, determines whether or not model terms should be interpreted as main effects.•We believe that this is not well-understood in the field of psycholinguistics.•A primer using simulated data in R showcases the problem.•A meta-analytic study demonstrates that the majority of the psycholinguistic literature does not transparently describe contrast coding choices. Contrast coding in regression models, including mixed-effect models, changes what the terms in the model mean. In particular, it determines whether or not model terms should be interpreted as main effects. This paper highlights how opaque descriptions of contrast coding have affected the field of psycholinguistics. We begin with a reproducible example in R using simulated data to demonstrate how incorrect conclusions can be made from mixed models; this also serves as a primer on contrast coding for statistical novices. We then present an analysis of 3384 papers from the field of psycholinguistics that we coded based upon whether a clear description of contrast coding was present. This analysis demonstrates that the majority of the psycholinguistic literature does not transparently describe contrast coding choices, posing an important challenge to reproducibility and replicability in our field.