Association for Public Policy Analysis & Management

A growing number of students choose to “opt out” from taking regularly administered standardized tests in California and across the country. There is evidence that this phenomenon has seen a recent surge in popularity among parents for a variety of reasons, including concerns surrounding common core standards, the amount of instructional time spent on standardized testing, and the perceived narrow focus of testing. The growth of the opt out movement may have a direct impact on our ability to use student data in multiple important ways. For example, in California, the ability to use Smarter Balanced summative assessments as a school accountability metric and to use interim assessments to support teaching and learning throughout the year are susceptible to growth in the opt out movement. Even new, broader measures of student performance often include multiple measures of academic progress susceptible to bias in the presence of student opt outs, such as yearly student growth and high school readiness measures. This is particularly important because, as of the 2013 legislative session, these types of evaluations can be used to set statewide performance goals, and elements of the new accountability system can be used to justify intervention into chronically underperforming schools and districts.

In this paper I evaluate the impact of opting out on multiple school districts in California on a range of teacher and school level accountability metrics. I utilize a panel of student level administrative data that includes teacher and school characteristics to simulate the impact of various opt scenarios, based on existing opt out patterns seen in California and in other large states (e.g. New York & Pennsylvania). I use linear regression and student fixed effects models (similar to those used in teacher and school accountability measures) to identify specifically how different proportions of students opting out would impact these measures, both in an absolute and ordinal sense. Further analyses identify which schools are most likely to fail to meet the minimum threshold for accountability (95% of all eligible students), as well as to recognize which teachers and schools are most likely to fall just below various accountability cut-offs as a result of a growth in opting out in their classrooms and schools. Finally, these data are used to determine if our ability to estimate achievement gaps is harmed by the non-random self-removal of certain student types from the testing data.

Preliminary evidence suggests that, were the opt out movement to grow in California in a similar manner to that seen recently in New York, with respect to both the overall magnitude of opting out (20%) as well as the over-representation of certain student subgroups among those choosing to opt out, this would have measurable impacts on both teacher and school level accountability systems. Even more troubling perhaps is the fact that the absence of a disproportionate number of high achieving students may underestimate our estimates of true achievement gaps with respect to both race and income, undermining a central measure of educational equity and progress.