Slips and falls are a leading cause of workplace injuries. These injuries impact the quality of life of those affected and lead to high costs because of treatment and lost wages. Friction between shoes and floors, quantified by the coefficient of friction (COF), is a major causal factor of slipping. Therefore, being able to predict the COF between a shoe and floor as well as understanding how characteristics of both affect friction can help reduce risk of slipping. Predictions of shoe-floor friction can be made using models that employ varying methods to calculate the COF. The model examined in this thesis is a hysteresis mechanics model that uses multiscale surface topography of floors and time-dependent material response of shoes along with biomechanically relevant boundary conditions to make predictions. The validity of this model is assessed by comparing model and experimental COF results measured for two datasets: one focused on varying floor surfaces and another focused on varying shoe material. The effects of other environmental and biomechanical factors on model predictions are also examined. The results indicate that the model is able to significantly predict oily shoe-floor friction and its predictive ability improves with the exclusion of a certain range of small-scale topography features. Additionally, the COF predictions from the model decrease at higher pressures and lower temperatures although these effects are nonlinear. This thesis offers a tool to improve shoe and floor design and decrease risk of slipping.