The effect of summer on value-added assessments of teacher and school performance

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Abstract

This study examines the effects of including the summer period on value-added assessments (VAA) of teacher and school performance at the early grades. The results indicate that 40-62% of the variance in VAA estimates originates from the summer period, depending on the outcome. Furthermore, when summer is omitted from the VAA model, 51-61% of the teachers and 58-61% of the schools change performance quintiles, with many changing 2-3 quintiles. Extensive statistical controls for student background and classroom and school context reduce these differences, but 36-47% of the teachers and 42-49% of the schools are still in different quintiles. Besides misclassifying teachers and schools, including summer creates biases in VAA estimates against schools with concentrated poverty. These results suggest that removing summer effects from VAA estimates will likely require biannual achievement assessments (i.e., fall and spring).

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How to Cite
Palardy, G. J., & Peng, L. (2015). The effect of summer on value-added assessments of teacher and school performance. Education Policy Analysis Archives, 23, 92. https://doi.org/10.14507/epaa.v23.1997
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Author Biographies

Gregory J Palardy, University of California, Riverside

Dr. Palardy is on the faculty of the Graduate School of Education at the University of California, Riverside.  His research focuses on teacher and school effectiveness with an emphasis on understanding how educational practices, policies, and contexts contribute to student outcomes and to educational opportunity.  Recent studies have examined the following topics: the effects of socioeconomic and ethnic segregation in schools on student attainment, learning, and school behaviors; the effects of inequitable access to qualified and effective teachers on achievement gaps; and the effects of social mechanisms in schools, such as peer influences, social capital, and organizational habitus, on student outcomes.

Luyao Peng, University of California, Riverside

Luyao Peng is a doctoral student in Educational Psychology and a Master’s student in Applied Statistics.  Her research interests are in educational measurement and testing and applications of multilevel and item response theory models.  She is currently studying the efficacy of the deterministic gated IRT model for detecting cheating on test items.