Value-Added and Student Growth Percentile Models: What Drives Differences in Estimated Classroom Effects?

This study shows value-added models (VAM) and student growth percentile (SGP) models fundamentally disagree regarding estimated teacher effectiveness when the classroom distribution of test scores conditional on prior achievement is skewed (i.e., when a teacher serves a disproportionate number of high- or low-growth students). While conceptually similar, the two models differ in estimation method which can lead to sizable differences in estimated teacher effects. Moreover, the magnitude of conditional skewness needed to drive VAM and SGP models apart often by three and up to 6 deciles is within the ranges observed in actual data. The same teacher may appear weak using one model and strong with the other. Using a simulation, I evaluate the relationship under controllable conditions. I then verify that the results persist in observed student–teacher data from North Carolina.

本研究发现，当以先验学业成绩（prior achievement）为条件的班级考试分数分布呈现偏态时，即教师所带班级中高成长或低成长学生占比过高，增值模型（value-added models, VAM）与学生成长百分位模型（student growth percentile, SGP）在教师效能的估算结果上存在根本性分歧。尽管二者概念相近，但估算方法的差异会导致教师效应估值出现显著差异。此外，使两类模型估值出现差距所需的条件偏态程度（差距可达3个甚至最多6个十分位区间），处于实际观测数据的常见范围内。同一教师可能在一种模型下被判定为低效，而在另一种模型下被评定为高效。本研究通过模拟实验在可控条件下探究了二者的关联，随后利用北卡罗来纳州的观测学生-教师匹配数据验证了该结论的稳健性。