In this study, survey nonresponse is researcher specified and data simulated to evaluate its impact on error and bias in attitudinal ratings. We accomplish this by scaling densities of (non)response probabilities to a survey's item metric and using these now conditional probabilities to sample from known attitudinal distributions. The illustrative context we choose for our simulations is intra-organizational surveying (within which attitudes toward constructs such as employee engagement, job satisfaction, or organizational climate are commonly polled), although the results and implications extend to any survey with metrically consonant item response scales. The simulations identify population and response pattern combinations that are either susceptible or relatively immune to bias. We then demonstrate how this procedure can be inverted and applied as a bias correction technique for dealing with nonresponse. This inversion is the only existing sample corrective procedure (known within the literature) that can be applied with no requisite knowledge of target population parameters, instead requiring an estimation of patterns of nonresponse.