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Conducting a rigorous quasi-experimental evaluation using a school district's existing student database

机译:使用学区现有的学生数据库进行严格的准实验评估

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摘要

Often, when an evaluation is needed, there is always a tendency to jump into data collection without first considering what might be already available. This paper demonstrates how an existing student database from a large school district could be used to provide some rigorous evidence regarding the impact of a supplementary teacher professional development programme on student achievement. Randomization was not possible in this study but to ensure rigour, matching of the treatment and the comparison groups was done on variables that were considered highly predictive of future achievement. The two groups were matched at the class level on course type, grade level and prior achievement. Next, hierarchical linear modelling was used to further control for selection bias between the two groups. According to the statistical theory on selection bias, this two-part process of matching and modelling removes all the observed bias. This illustration shows that performing a rigorous quasi-experimental study is possible in education even when there are limiting circumstances to conducting a randomized experiment.
机译:通常,当需要评估时,总是有跳入数据收集的趋势,而没有先考虑可能已有的数据。本文演示了如何使用来自大型学区的现有学生数据库来提供一些有关补充教师专业发展计划对学生成绩影响的严格证据。在这项研究中不可能进行随机分组,但是为了确保严谨性,对治疗和对照组的匹配是在被认为可以高度预测未来成就的变量上进行的。两组在课程类型,年级水平和先前成绩方面在班级进行匹配。接下来,使用分层线性建模来进一步控制两组之间的选择偏差。根据选择偏倚的统计理论,此由两部分组成的匹配和建模过程会消除所有观察到的偏倚。该图说明,即使在进行随机实验的条件有限的情况下,也可以在教育中进行严格的准实验研究。

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