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

•Testing noise can generate a reversion bias in addition to the attenuation bias.•The reversion bias is due to the presence of former peers in the current peer group.•The reversion bias can be corrected by exploiting peer quality variation attributable to new peers only.•The existence and correction of the reversion bias are illustrated with real-world data.

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.