We examined factors hypothesized to influence Ruffed Grouse (Bonasa umbellus) population cycles by evaluating 13 a priori models that represented correlations between spring counts of male Ruffed Grouse drumming displays and these factors. We used AIC_c to rank the relative ability of these models to fit the data and used variance components analysis to assess the amount of temporal process variation in Ruffed Grouse spring counts explained by the best model. A hypothesis representing an interaction between winter precipitation and winter temperature was the top-ranked model. This model indicated that increased precipitation during cold winters (soft snow cover for roosting) was correlated with higher grouse population indices, but that increased precipitation during warm winters (snow crust effect) was correlated with lower spring counts. The highest ranked model (AIC_c weight = 0.45), explained only 17% of the temporal process variation. The number of migrating Northern Goshawks (Accipiter gentilis), which has been correlated with grouse cycles in previous studies, does not adequately explain, by itself, the variation in annual population indices of Ruffed Grouse. Other factors not considered in our analysis, such as endogenous mechanisms, parasites, or interactions among factors may also be important, which suggest that mechanisms mediating the Ruffed Grouse cycle still require investigation.