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The Sensitivity of Value-Added Estimates to Specification Adjustments: Evidence From School- and Teacher-Level Models in Missouri

机译:增值估算对规范调整的敏感性:来自密苏里州学校和教师一级模型的证据

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We provide a side-by-side comparison of school and teacher growth measures estimated from different value-added models (VAMs). We compare VAMs that differ in terms of which student and school-level (or teacher-level) control variables are included and how these controls are included. Our richest specification includes 3?years of prior test scores for students and the standard demographic controls; our sparsest specification conditions only on a single prior test score. For both schools and teachers, the correlations between VAM estimates across the different models are high by conventional standards (typically at or above 0.90). However, despite the high correlations overall, we show that the choice of which controls to include in VAMs, and how to include them, meaningfully influences school and teacher rankings based on model output. Models that are less aggressive in controlling for student-background and schooling-environment information systematically assign higher rankings to more-advantaged schools, and to individuals who teach at these schools.
机译:我们提供了根据不同增值模型(VAM)估算的学校和教师成长指标的并排比较。我们比较了VAM,这些VAM在包括哪些学生和学校级别(或教师级别)控制变量以及如何包括这些控件方面有所不同。我们最丰富的规范包括3年的学生先前考试成绩和标准的人口统计学控制;我们最稀少的规格条件仅基于单个先前的测试分数。对于学校和教师而言,按照传统标准,不同模型之间的VAM估算之间的相关性很高(通常为0.90或更高)。但是,尽管总体上相关性很高,但我们表明,根据模型输出,选择将哪些控件包含在VAM中以及如何将其包含在内,会显着影响学校和教师的排名。在控制学生背景和学校环境信息方面较不积极的模型会系统地将较高的排名分配给处于优势地位的学校以及在这些学校任教的个人。

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