Numerous studies have demonstrated that dispersal is dependent on both disperser phenotype and the local environment. However, there is substantial variability in the observed strength and direction of phenotype- and environment-dependent dispersal. This has been hypothesized to be the result of interactive effects among the multiple phenotypic and environmental factors that influence dispersal. Here, our goal was to test the hypothesis that these interactions are responsible for generating variation in dispersal behaviour. We achieved these goals by conducting a large, 2-year, mark-release-recapture study of the backswimmer Notonecta undulata in an array of 36 semi-natural ponds. We measured the effects of multiple phenotypic (sex and body size) and environmental (population density and sex ratio) factors, on both dispersal probability and dispersal distance. We found support for the hypothesis that interactive effects influence dispersal and produce variability in phenotype- and environment-dependent dispersal: dispersal probability was dependent on the three-way interaction between sex, body mass and population density. Small males displayed strong, positive density dependence in their dispersal behaviour, while large males and females overall did not respond strongly to density. Small notonectids, regardless of sex, were more likely to disperse, but this effect was strongest at high population densities. Finally, the distance dispersed by backswimmers was a negative function of population density, a pattern which we hypothesize could be related to: (a) individuals from high and low density patches having different dispersal strategies, or (b) the effect of density on dispersal capacity. These results suggest that phenotype-by-environment interactions strongly influence dispersal. Since phenotype- and environment-dependent dispersal has different consequences for ecological and evolutionary dynamics (e.g. metapopulation persistence and local adaptation) than random dispersal, interactive effects may have wide-reaching impacts on populations and communities. We therefore argue that more investment should be made into estimating the effects of multiple, interacting factors on dispersal and determining whether similar interactive effects are acting across systems.