I conducted this study to better understand the greater roadrunner’s (Geococcyx californianus) spatial use, nest site selection, and sexual morphometrics. Data were first collected from a roadrunner population in Fisher County, Texas. I trapped, measured, and removed feathers for sex confirmation through DNA PCR analysis. I then fit the roadrunners with a radio transmitter and released them at the capture site. I captured a total of 10 birds (1 male and 9 females) and triangulated the location of each bird 2-4 times per week from February to August 2011 during the roadrunner nesting season. The data produced a mean minimum convex polygon nesting range of 43.01-ha, a 50% core utilization distributions of 11.88-ha, and an overlap index of 33.05 percent. Habitat selection ratios showed a preference for ridge as well as grassland habitat and avoidance of bare ground and flatland habitats. The location data from west Texas was further analyzed to build a predictive logistic regression model to understand the significant site characteristics in roadrunner land use during the nesting season. It was determined that percent rock (-0.12, P = 0.0001) and percent litter (-0.05, P = 0.0052) were the best predictors in determining actual from random locations. Rock references open area for hunting, transportation as well as ridge habitat. Litter alludes to region below dense stands of shrubs and is used by roadrunners for evasion from threats and as shade for thermoregulation. The measurements from the roadrunners in west Texas as well as museum specimens from across their natural range were combined to develop an easy, inexpensive, and field-relevant sex identification model. The strongest model consisted of bill depth (0.79, P< 0.0001) and bill tip to the back of the head (0.05, P = 0.1573) which were both found to be larger in males. The final portion of this study concerned roadrunner nest site selection. Data were collected in Wilbarger County, Texas from May 2006 to October 2009. A predictive logistic regression model of nesting locations determined that mesquite nesting tree (-0.89, P = 0.0064), oval tree shape (1.58, P = 0.0118), mesquite density (-0.004, P = 0.0080), and topographic edge (1.37, P = 0.0027) were the best predictors in determining actual nest sites from random locations.