A few months ago, the U.S. Department of Education (USED) released the latest data from schools that received grants via the School Improvement (SIG) program. These data — consisting solely of changes in proficiency rates — were widely reported as an indication of “disappointing” or “mixed” results. Some even went as far as proclaiming the program a complete failure.
Once again, I have to point out that this breaks almost every rule of testing data interpretation and policy analysis. I’m not going to repeat the arguments about why changes in cross-sectional proficiency rates are not policy evidence (see our posts here, here and here, or examples from the research literature here, here and here). Suffice it to say that the changes themselves are not even particularly good indicators of whether students’ test-based performance in these schools actually improved, to say nothing of whether it was the SIG grants that were responsible for the changes. There’s more to policy analysis than subtraction.
So, in some respects, I would like to come to the defense of Secretary Arne Duncan and USED right now – not because I’m a big fan of the SIG program (I’m ambivalent at best), but rather because I believe in strong, patient policy evaluation, and these proficiency rate changes are virtually meaningless. Unfortunately, however, USED was the first to portray, albeit very cautiously, rate changes as evidence of SIG’s impact. In doing so, they provided a very effective example of why relying on bad evidence is a bad idea even if it supports your desired conclusions.