TY - JOUR

T1 - Best practice guidance for linear mixed-effects models in psychological science

AU - Meteyard, Lotte

AU - Davies, Robert

N1 - This is the author’s version of a work that was accepted for publication in Journal of Memory and Language. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Memory and Language, 112, 2020 DOI: 10.1016/j.jml.2020.104092

PY - 2020/6/30

Y1 - 2020/6/30

N2 - 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. Here we examine this diversity using 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 complements the survey, showing variation in how the models are built, how effects are evaluated and, most worryingly, how models are reported. Using these data as our departure point, we present a set of best practice guidance, focusing on the reporting of LMMs. It is the authors’ intention that the paper supports a step-change in the reporting of LMMs across the psychological sciences, preventing a trajectory in which findings reported today cannot be transparently understood and used tomorrow.

AB - 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. Here we examine this diversity using 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 complements the survey, showing variation in how the models are built, how effects are evaluated and, most worryingly, how models are reported. Using these data as our departure point, we present a set of best practice guidance, focusing on the reporting of LMMs. It is the authors’ intention that the paper supports a step-change in the reporting of LMMs across the psychological sciences, preventing a trajectory in which findings reported today cannot be transparently understood and used tomorrow.

KW - linear mixed effects models

KW - hierarchical models

KW - multilevel models

UR - https://osf.io/bfq39/

U2 - 10.1016/j.jml.2020.104092

DO - 10.1016/j.jml.2020.104092

M3 - Journal article

VL - 112

JO - Journal of Memory and Language

JF - Journal of Memory and Language

SN - 0749-596X

M1 - 104092

ER -