Whenever nonexperimental methods are used to test a hypothesis and 1 or more predictor (independent) variables that may affect the criterion (dependent) variable are omitted from the analyses, it is possible that the estimates of the effects of the predictors are biased or that the omitted variable could account entirely for the effects attributed to one or more of the predictors. In this article, a technique is developed for determining when a variable omitted from a linear model can account for the effects attributed to a predictor included in that model.