Population genetics-based approaches can provide robust and cost-effective ways to assess the effects of potential barriers, including dams and road-stream crossings, on the passage and population connectivity of aquatic organisms. Determining the best way to apply and modify genetic tools for different species and situations is essential for making these genetics-based approaches broadly applicable to fisheries and aquatic habitat management. Here, we used multiple genetic approaches to assess the movement and population structure of Slimy Sculpin Cottus cognatus at two road-stream crossings in Michigan and one dam in Massachusetts, USA. We captured and genotyped individual sculpin and assessed movement and population connectivity by using (1) a sibship-based approach, where the presence and proportional distribution of siblings on either side of a barrier indicates population connectivity and the possible direction of movement (i.e., presumed movement from higher to lower proportions), and (2) two Bayesian genetic assignment approaches (STRUCTURE and BayesAss) to identify migrants across potential barriers based on individual population assignment probabilities. We also used traditional genetic metrics to assess within-population genetic variation and among-population genetic divergence. At all three locations, we found evidence for sculpin movement across the potential barrier based on sibship reconstruction, but small family sizes limited the ability of this approach to provide robust estimates of the rate and direction of movement. At two sites, a lack of genetic differentiation between above- and below-barrier populations limited the effectiveness of the genetic assignment methods for identifying possible migrants. At the third site, reduced upstream allelic diversity and effective number of breeders resulted in high genetic differentiation (FST) between above- and below-barrier populations, and both sibship and genetic assignment methods provided strong evidence of limited connectivity and bias against upstream movement. Overall, combining approaches and metrics may help overcome the limitations of any one method and maximize the value of datasets for genetics-based monitoring and assessment.