There is an enduring issue on whether student‐ and school‐level covariates should be included in value‐added school effects models, in addition to prior achievement. Proponents argue that the addition of covariates allows fairer comparisons of schools, whereas opponents argue that it excuses poorly performing schools and obscures policy‐relevant school differences. School‐level covariates are problematic statistically, but it has been argued that mean school prior achievement should be included in school effects analyses to reduce error. This article reports on school effects analyses of Australia‐wide data of approximately 1.5 million students in both primary and secondary schools that took national assessments in five achievement domains between 2013 and 2018. With appropriate controls for prior achievement, school effects are generally small and most often not statistically significant. The addition of student‐level covariates: further reduces school effects, since part of the school effects is absorbed by the effects of the covariates, which are unlikely to reflect causal social processes; reduces the proportion of schools with significant school effects; does not improve predictive power; increases the amount of missing data; and further reduces the consistency of school effects between domains and their stability over time. Mean school prior achievement did not improve consistency or stability. Incorporating covariates in school effects analyses opens a Pandora’s Box of specification and measurement issues, undermining the legitimacy of school comparisons. It is concluded that researchers and administrators of educational jurisdictions should focus mainly on simpler models based on prior achievement.

中文翻译：

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增值学校效应模型是否应包括学生和学校水平的协变量？来自澳大利亚人口评估数据的证据

关于除了先前的成就以外，是否应将学生和学校水平的协变量包括在增值的学校效果模型中，这是一个长期存在的问题。支持者认为，协变量的增加使学校的比较更为公平，而反对者则认为，这是表现不佳的学校的借口，并掩盖了与政策相关的学校差异。学校级别的协变量在统计上是有问题的，但有人认为，学校效果分析应包括学校的先前成绩，以减少错误。本文报告了对澳大利亚范围内大约150万名中小学学生的数据进行的学校效果分析，这些数据在2013年至2018年期间对五个成就领域进行了国家评估。对学校的影响通常很小，并且通常在统计上不显着。增加学生水平的协变量：进一步降低学校效应，因为部分学校效应被协变量的效应吸收，这不太可能反映因果的社会过程；减少对学校有重大影响的学校的比例；不能提高预测能力；增加丢失的数据量；并进一步降低域之间学校效应的一致性及其随时间的稳定性。意味着学校先前的成绩并没有提高一致性或稳定性。将协变量纳入学校效果分析中会打开“潘多拉盒子”中的规格和测量问题，这会破坏学校比较的合法性。