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.

中文翻译：

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在预测方程中调整群截距和斜率偏差

评估构造中测量不变性的方法已引起广泛关注，因为不变性对于准确的组比较至关重要。在预测方程中，对不变性来源的识别和校正的关注较少。这项工作为常见的回归方程式开发了结构截距和斜率偏差的校正因子，以解决文献中对复兴试验偏差研究的需求。我们在包含68个国家和地区（组）的大型国际数据集的背景下，在回归分析中展示了校正因子。数学成绩得分是由数学自我效能感得分预测的，这表明各组之间缺乏不变性。提出的校正因子可显着校正组间的结构截距和斜率偏差。校正因子的影响对偏差量最大的群体影响最大。讨论了对实践和方法扩展的影响。