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   Mitani, Hajime (January 2025, American Educational Research Journal)

   The inequitable distribution of principal effectiveness raises concern among policymakers. Principal sorting likely contributes to wider achievement and opportunity gaps between low- and high-need schools. As a possible policy tool, policymakers proposed performance-based compensation systems (PBCS). Tennessee was one of the states that supported the implementation of PBCS. This study examined the relationship between PBCS and principal job performance in the state, using longitudinal administrative data, principal evaluation data, and unique PBCS data from 2012 to 2019. The study did not find consistently significant, positive relationships between PBCS and principal job performance. However, the relationships were generally more pronounced among high-need schools. The study concludes with detailed discussions about the results, the assumptions behind PBCS, limitations, and implications. 

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4. Taming Performance Variability

   Maricq, A; Duplyakin, D; Jimenez, I; Maltzahn, C; Stutsman, R; Ricci, R (October 2018, Proceedings of the 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’18))

   The performance of compute hardware varies: software run repeatedly on the same server (or a different server with supposedly identical parts) can produce performance results that differ with each execution. This variation has important effects on the reproducibility of systems research and ability to quantitatively compare the performance of different systems. It also has implications for commercial computing, where agreements are often made conditioned on meeting specific performance targets. Over a period of 10 months, we conducted a large-scale study capturing nearly 900,000 data points from 835 servers. We examine this data from two perspectives: that of a service provider wishing to offer a consistent environment, and that of a systems researcher who must understand how variability impacts experimental results. From this examination, we draw a number of lessons about the types and magnitudes of performance variability and the effects on confidence in experiment results. We also create a statistical model that can be used to understand how representative an individual server is of the general population. The full dataset and our analysis tools are publicly available, and we have built a system to interactively explore the data and make recommendations for experiment parameters based on statistical analysis of historical data. 

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