Many epidemiologic studies have shown that measurement error (ME) in exposures can distort the observed exposure-outcome associations. Predominantly in nutrition epidemiology, dietary intakes from self-report tend to be subject to both systematic and random (unbiased) ME. For some nutrients, recovery biomarkers that have unbiased (classical) ME exist. Regression calibration (RC) can largely correct bias in risk estimates from outcome regression models induced by exposure ME, through application of a calibration model. Although it is well-known that misspecifying regression models results in biased parameter estimates, the robustness of RC to misspecification of the outcome model or calibration model has been less studied. In this study we have investigated how misspecification of either the calibration model or outcome model affects the performance of RC. We conducted extensive simulations under systematic ME and classical ME, and a logistic outcome model, examining the effects of omitting a covariate from 1) the calibration model, 2) the outcome model or 3) both, under varying scenarios of correlation between the omitted covariate and other predictors.