Methods to assess measurement invariance in constructs have received much attention, as invariance is critical for accurate group comparisons. Less attention has been given to the identification and correction of the sources of non-invariance in predictive equations. This work developed correction factors for structural intercept and slope bias in common regression equations to address calls in the literature to revive test bias research. We demonstrated the correction factors in regression analyses within the context of a large international dataset containing 68 countries and regions (groups). A mathematics achievement score was predicted by a math self-efficacy score, which exhibited a lack of invariance across groups. The proposed correction factors significantly corrected structural intercept and slope bias across groups. The impact of the correction factors was greatest for groups with the largest amount of bias. Implications for both practice and methodological extensions are discussed.