Best practice guidance for linear mixed-effects models in psychological science

The use of Linear Mixed Effects Models (LMMs) is set to dominate statistical analyses in psychological science and may become the default approach to analyzing quantitative data. The rapid growth in adoption of LMMs has been matched by a proliferation of differences in practice. Unless this diversity is recognized, and checked, the field shall reap enormous difficulties in the future when attempts are made to consolidate or synthesize research findings. The proposed article examines the diversity in modeling practice by two methods – a survey of researchers (n=163) and a quasi-systematic review of papers using LMMs (n=400). The survey reveals substantive concerns among psychologists using or planning to use LMMs and an absence of agreed standards. The review of papers complement the survey, showing variation in how the models are built, how effects are evaluated and, most worryingly, how models are reported. Using this data as our departure point, we present a set of best practice guidance for reporting LMMS. It is the authors’ intention that the paper supports a step-change in the reporting of LMMS across the psychological sciences, preventing a future void in which data reported today cannot be transparently understood and used tomorrow.