The role of testing noise in the estimation of achievement-based peer effects

I demonstrate that in the value-added estimation of peer effects using lagged peer achievement, testing noise may generate another bias in addition to the well-known attenuation bias. Such a bias, which I refer to as the “reversion bias,” may arise when some of a student's current peers happen to be his/her former peers whose performances in the baseline test were subject to the same common testing noise as the student's own. I propose a solution to overcome this problem by exploiting only the variation in the new peers’ portion of the overall peer quality. Using real-world data, I illustrate the existence of this bias and demonstrate the proposed solution.