Multilevel Modeling in Traffic Safety Research: Two Empirical Examples Illustrating the Consequences of Ignoring Hierarchies

Abstract

Objectives: This commentary illustrates the advantages of multilevel modeling compared to statistical techniques that ignore hierarchies, based on two empirical traffic safety examples. Methods: The common concept shared by different definitions of multilevel modeling is identified and illustrated. Each definition defines multilevel modeling in its own way but they all refer to hierarchies. Conceptual issues inherently related to hierarchies are then pointed out and illustrated. Results: Broadly speaking there are two important consequences of ignoring a hierarchical structure in the data. The first consequence, underestimation of standard errors, is illustrated with data from an observational study on seatbelt behavior. Two effects that were significant at the 5% level in a single-level model were no longer found to be significant in a two-level model. The single-level model is therefore bound to lead to erroneous conclusions regarding variables that could have an impact on seatbelt use and, ultimately, on increasing the level of traffic safety. The second consequence, related to contextual information, is illustrated with data from a roadside survey on drink driving. Of particular interest is the relationship between Traffic Count, an aggregated level 2 explanatory variable and Odds of drink driving, an individual level 1 dependent variable. Conclusions: Like every statistical technique, multilevel models should be used with caution and reservation. However, given certain limitations, multilevel modeling is very useful and valuable to traffic safety research.