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Background Researchers have been utilizing linear mixed models (LMMs) for different hierarchical study designs and under different names, which emphasizes the need for a standard in reporting such models [1, 2]. Mixed effects models, multilevel data, contextual analysis, hierarchical studies, longitudinal studies, panel data and repeatedmeasures designs are some of the different names used when referring to study designs and/or analytical tools for correlated data. In addition, there is usually no distinction made between having a data structure that is multilevel, and having a research question that requires a multilevel analysis. There are multiple excellent tutorials on multilevel analyses [3–5]. However, there is inconsistency in how the results of LMMs are reported in the literature [6]. Casals et al. conducted a systematic review of how various LMMs were reported in the medical literature, and found that important aspects were not reported in most cases [6]. As an example, a cohort study of children that selects a sample of schools, then selects students within schools, and conducts multiple measurements over time in the same students, would be a 3-level dataset: with school as the highest level (Level 3), student as a lower level (Level 2), and time-point as the lowest level (Level 1). Repeated measurements of a variable over time within a student are likely to be similar, i.e. positively correlated. Also, values of a variable measured on students of a particular school may be more similar to each other than to the

LEVEL (Logical Explanations & Visualizations of Estimates in Linear mixed models): recommendations for reporting multilevel data and analyses