Understanding L.O.V.E. (left out variables error): A method for estimating the effects of omitted variables.

Whenever nonexperimental methods are used to test a hypothesis and 1 or more predictor (inde-pendent) 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. Social scientists are rarely able to obtain information on all of the factors that may affect their criterion (outcome, dependent) variables. Whenever researchers use nonexperimental meth-ods and fail to account for all of the variables that affect a criterion, their inferences about the effects of the predictors (independent variables) on that criterion may be biased.1 Whenever a relevant variable is neither held constant nor en-tered into an analysis, that variable could account entirely for the effects attributed to one or more of the predictor variables