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首页> 外文期刊>Prevention science: the official journal of the Society for Prevention Research >Pretest Measures of the Study Outcome and the Elimination of Selection Bias: Evidence from Three Within Study Comparisons
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Pretest Measures of the Study Outcome and the Elimination of Selection Bias: Evidence from Three Within Study Comparisons

机译：预测研究结果和消除选择偏差的措施：从学习比较中的三个证据

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Abstract This paper examines how pretest measures of a study outcome reduce selection bias in observational studies in education. The theoretical rationale for privileging pretests in bias control is that they are often highly correlated with the outcome, and in many contexts, they are also highly correlated with the selection process. To examine the pretest’s role in bias reduction, we use the data from two within study comparisons and an especially strong quasi-experiment, each with an educational intervention that seeks to improve achievement. In each study, the pretest measures are consistently highly correlated with post-intervention measures of themselves, but the studies vary the correlation between the pretest and the process of selection into treatment. Across the three datasets with two outcomes each, there are three cases where this correlation is low and three where it is high. A single wave of pretest always reduces bias across the six instances examined, and it eliminates bias in three of them. Adding a second pretest wave eliminates bias in two more instances. However, the pattern of bias elimination does not follow the predicted pattern—that more bias reduction ensues as a function of how highly the pretest is correlated with selection. The findings show that bias is more complexly related to the pretest’s correlation with selection than we hypothesized, and we seek to explain why.
机译：摘要本文研究了研究结果对学习研究中的选择偏差有何预测试。偏差控制中的特权预测的理论基本原理是它们通常与结果高度相关，并且在许多情况下，它们也与选择过程高度相关。为了检查预测在偏差减少中的角色，我们在研究比较中的两个数据和一个特别强大的准实验中的数据，每个都有一种教育干预，寻求改善成就。在每项研究中，预先采取的措施与本身的干预措施始终如一，但研究改变了预测试和选择过程之间的相关性。在每个结果中的三个数据集中，每个结果都有三个情况，这种相关性低，三个是高的。一波预测试始终减少六个检查实例的偏差，并且它消除了其中三个偏差。添加第二个预测试波消除了两个实例中的偏差。然而，偏差消除模式不遵循预测的模式 - 随着预测的偏差，随着预测性与选择相关的函数而言，可能会产生更多偏差。调查结果表明，与我们假设的选择的预测试的相关性更复杂，我们试图解释原因。

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