Multilevel Models: A Tutorial on Applications and Uses in Human Factors Research

Human behaviour is heterogeneous, a feature that is particularly important in Human Factors (HF) research where performance variability can have safety-critical implications. However, HF studies often focus on average effects between experimental conditions, treating individual differences as statistical noise rather than as meaningful information. Modelling behavioural heterogeneity can improve understanding of how systems affect users and support stronger theoretical development. Multilevel Models (MLMs) provide a flexible statistical framework for analysing hierarchical data structures, such as repeated measurements nested within individuals. Advances in statistical software have increased the accessibility of MLMs, yet many HF studies do not fully exploit their ability to model individual differences in experimental effects. One barrier is limited availability of practical tutorials demonstrating how MLMs can be applied to typical HF datasets. This manuscript addresses this gap by providing a practical introduction to MLMs for HF researchers. First, we review MLM principles and their relevance to HF research. We then present two worked examples. Study 1 demonstrates MLMs as an extension of linear regression for continuous predictors; Study 2 applies MLMs to factorial designs and discusses strategies for managing convergence in complex random-effects structures. Analyses are supported by example datasets and code in R, Python, and MATLAB.