As the State Board of Education works towards a revised Achievement Index, it is taking a close look at the relationship between poverty and proficiency compared to the relationship between poverty and growth. Our analysis of the first simulations of Revised Index data shows that the relationship between poverty and proficiency is much stronger than the relationship between poverty and growth. We looked at this relationship both for reading and math results.
Proficiency and Poverty
Comparing math proficiency to reading proficiency shows that reading proficiency is generally higher than math proficiency for all students. The comparison shows that there is far more variation in math proficiency. This is true for both proficiency and growth. While the reasons for that variation cannot be explained solely by the graphs below, there is something happening within the schools, the lives of the students, or the testing that causes these differing levels of math proficiency.
Looking at the proficiency graphs, the angle of the trend-line shows that there is a stronger relationship between poverty and reading than poverty and math. Thus, the socioeconomic background of the student may have less of an impact on math proficiency than reading proficiency. Poverty accounts for 37.2% of the variance in reading proficiency but only 28.9% of the variance in math proficiency.
Growth and Poverty
From comparing the growth charts to the proficiency charts, it is clear that the relationship between poverty and proficiency is much stronger than the relationship of poverty to growth. Poverty only accounts for 4% of variance of growth in reading and 2.5% of variance of growth in math. That correlation of poverty to growth is much weaker than the 37.2% of reading proficiency and 28.9% of math proficiency that are accounted for by poverty.
The revised Achievement Index data tells us that schools with high proficiency do not always produce high rates of growth. By including growth, the Board is able to identify schools where most of the students are meeting academic standard, but are growing at relatively low levels. Under the previous Achievement Index, these schools would simply be identified as having high proficiency even if the students were not growing quickly.
Through the revised Index, the Board can now identify schools that are producing the highest rates of growth in the state, regardless of their proficiency levels. Before the addition of growth to the Index, schools with low levels of proficiency would have been labeled failing even if they were truly creating a learning environment where students have high levels of growth.
Including growth in the revised Achievement Index strengthen the standards for schools and students. For the first time, we can set targets not only for proficiency, but also growth. This sends an important message: all students are expected to grow.
This data analysis gives us a few key takeaways. First, growth is much less correlated to poverty than proficiency. This is good news in the sense that incorporating growth into the Index creates a more “even playing field” – students in high-poverty schools are essentially just as well positioned to ‘grow’ as their wealthier counterparts.
Second, the predictive ability of this model decreases as poverty increases. One can see this by looking at how scattered the school data-points are in the right-hand side of the reading proficiency to poverty graph or by looking at the even more dramatic scattering of schools in the math proficiency to poverty graph.
Finally, in both the proficiency and growth data, there is greater variation in math than in reading. The reasons for this are not known and could be attributable to a variety of factors, but it’s an issue worth further exploration.