Lotze, nice tool; flot looks versatile. What technologies are other folks using to create their graphs?
Nathan, your use of line graphs is useful when demonstrating change in rank over time, but I don't see the value in using this over a scatterplot if the intent is to demonstrate correlation.
I have limited myself to the original SAT information to save time looking for the perfect data set. My report addresses the same issue Lotze identifies (i.e. participation rates must be accounted for before comparing SAT scores) by attempting to find subgroups where participation rates are no longer a factor and then ranking within those groups.
The effect of class size is a hot topic; I am not sure if there is a proven link to student performance. Exploring this relationship at the level of the state is likely to result in misleading findings at best. Consider the schools in hypothetical state A (student-to-teacher ratio, mean SAT scores) {(50:1, 500) (40:1, 400) (30:1, 300)} versus those in state B {(30:1, 550), (25:1, 500), (20:1, 450)}. At the level of the state we find A having a mean student-to-teacher ratio of 40:1 and mean SAT score of 400, and for state B, 25:1 and 500. Based the state level we would assume that decreasing the student-to-teacher ratio improves SAT results, but within each of the states the exact opposite trend is evident.
The following is my submission. I attempted to achieve a very content-driven and high data-density visualization that allows for sound, albeit limited comparisons and conclusions to be reached. The details on how the report was produced is available at http://pushindatalikeweight.com/ .