Models of isolation-by-distance formalize the effects of genetic drift and gene flow in a spatial context where gene dispersal is spatially limited. These models have been used to show that, at an appropriate spatial scale, dispersal parameters can be inferred from the regression of genetic differentiation against geographic distance between sampling locations. This approach is compelling because it is relatively simple and robust, and has rather low sampling requirements. In continuous populations, dispersal can be inferred from isolation-by-distance patterns using either individuals or groups as sampling units. Intrigued by empirical findings where individual samples seemed to provide more power, here we used simulations to compare the performances of the two methods in a range of situations with different dispersal distributions. We found that sampling individuals provides more power in a range of dispersal conditions that is narrow but fits many realistic situations. These situations were characterized not only by the general steepness of isolation-by-distance but also by the intrinsic shape of the dispersal kernel. The performances of the two approaches are otherwise similar, suggesting that the choice of a sampling unit is globally less important than other settings such as a study's spatial scale.