Obtaining unbiased estimates of dispersal parameters from capture-recapture data is challenging because we are unable to survey all possible range of dispersal in the field. Several modeling approaches have been proposed to address this critical issue of capture-recapture methods. However, the lack of formal comparisons among these modeling approaches has confused about which is the best practice given the available dataset. I compared the performance of three dispersal models using simulated datasets. First, a probability distribution (a dispersal kernel) was simply fitted to the capture-recapture data (the “simple dispersal model”). Second, a truncated probability distribution was used to account for the finite range of observations (the “truncated dispersal model”). Lastly, the dispersal and observation processes were coupled to consider the spatial organization of sampling designs (the “dispersal-observation model”). The simulation study provided important insights. While the simple dispersal model consistently underestimated dispersal parameters regardless of sampling designs, the other two models provided reasonable estimates. These modeling approaches (the truncated or dispersal-observation model) are preferable approaches to better understand dispersal in the field.