Revisiting The "Best Evidence" Theory Of Charter School Performance

Among the more persistent arguments one hears in the debate over charter schools is that the “best evidence” shows charters are more effective. I have discussed this issue before (as have others), but it seems to come up from time to time, even in mainstream media coverage.

The basic point is that we should essentially dismiss – or at least regard with extreme skepticism - the two dozen or so high-quality “non-experimental” studies, which, on the whole, show modest or no differences in test-based effectiveness between charters and comparable regular public schools. In contrast, “randomized controlled trials” (RCTs), which exploit the random assignment of admission lotteries to control for differences between students, tend to yield positive results. Since, so the story goes, the “gold standard” research shows that charters are superior, we should go with that conclusion.

RCTs, though not without their own limitations, are without question powerful, and there is plenty of subpar charter research out there. That said, however, the “best evidence” argument is not particularly compelling (and it's also a distraction from the positive shift away from obsessing about whether charters do or don't work toward an examination of why). A full discussion of the methodological issues in the charter school literature would be long and burdensome, but it might be helpful to lay out three very basic points to bear in mind when you hear this argument.

Why Did Florida Schools' Grades Improve Dramatically Between 1999 and 2005?

** Reprinted here in the Washington Post

Former Florida Governor Jeb Bush was in Virginia last week, helping push for a new law that would install an “A-F” grading system for all public schools in the commonwealth, similar to a system that has existed in Florida for well over a decade.

In making his case, Governor Bush put forth an argument about the Florida system that he and his supporters use frequently. He said that, right after the grades went into place in his state, there was a drop in the proportion of D and F schools, along with a huge concurrent increase in the proportion of A schools. For example, as Governor Bush notes, in 1999, only 12 percent of schools got A's. In 2005, when he left office, the figure was 53 percent. The clear implication: It was the grading of schools (and the incentives attached to the grades) that caused the improvements.

There is some pretty good evidence (also here) that the accountability pressure of Florida’s grading system generated modest increases in testing performance among students in schools receiving F's (i.e., an outcome to which consequences were attached), and perhaps higher-rated schools as well. However, putting aside the serious confusion about what Florida’s grades actually measure, as well as the incorrect premise that we can evaluate a grading policy's effect by looking at the simple distribution of those grades over time, there’s a much deeper problem here: The grades changed in part because the criteria changed.

A New Twist On The Skills "Blame Game"

It is conventional wisdom that the United States is suffering from a severe skills shortage, for which low-performing public schools and inadequate teachers must shoulder part of the blame (see here and here, for example).  Employers complain that they cannot fill open slots because there are no Americans skilled enough to fill them, while pundits and policymakers – President Barack Obama and Bill Gates, among them – respond by pushing for unproven school reform proposals, in a desperate effort to rebuild American economic competitiveness.

But, what if these assumptions are all wrong?

What if the deficiencies of our educational system have little to do with our current competitiveness woes? A fascinating new book by Peter Cappelli, Why Good People Can't Get Jobs: The Skills Gap and What Companies Can Do About It , builds a strong case that common business practices - failure to invest adequately in on-the-job training, offering noncompetitive wages and benefits, and relying on poorly designed computer algorithms to screen applicants –are to blame, not failed schools or poorly prepared applicants.

A Few Quick Fixes For School Accountability Systems

Our guest authors today are Morgan Polikoff and Andrew McEachin. Morgan is Assistant Professor in the Rossier School of Education at the University of Southern California. Andrew is an Institute of Education Science postdoctoral fellow at the University of Virginia.

In a previous post, we described some of the problems with the Senate's Harkin-Enzi plan for reauthorizing the No Child Left Behind Act, based on our own analyses, which yielded three main findings. First, selecting the bottom 5% of schools for intervention based on changes in California’s composite achievement index resulted in remarkably unstable rankings. Second, identifying the bottom 5% based on schools' lowest performing subgroup overwhelmingly targeted those serving larger numbers of special education students. Third and finally, we found evidence that middle and high schools were more likely to be identified than elementary schools, and smaller schools more likely than larger schools.

None of these findings was especially surprising (see here and here, for instance), and could easily have been anticipated. Thus, we argued that policymakers need to pay more attention to the vast (and rapidly expanding) literature on accountability system design.

Why Nobody Wins In The Education "Research Wars"

** Reprinted here in the Washington Post

In a recent post, Kevin Drum of Mother Jones discusses his growing skepticism about the research behind market-based education reform, and about the claims that supporters of these policies make. He cites a recent Los Angeles Times article, which discusses how, in 2000, the San Jose Unified School District in California instituted a so-called “high expectations” policy requiring all students to pass the courses necessary to attend state universities. The reported percentage of students passing these courses increased quickly, causing the district and many others to declare the policy a success. In 2005, Los Angeles Unified, the nation's second largest district, adopted similar requirements.

For its part, the Times performed its own analysis, and found that the San Jose pass rate was actually no higher in 2011 compared with 2000 (actually, slightly lower for some subgroups), and that the district had overstated its early results by classifying students in a misleading manner. Mr. Drum, reviewing these results, concludes: “It turns out it was all a crock."

In one sense, that's true – the district seems to have reported misleading data. On the other hand, neither San Jose Unified's original evidence (with or without the misclassification) nor the Times analysis is anywhere near sufficient for drawing conclusions - "crock"-based or otherwise - about the effects of this policy. This illustrates the deeper problem here, which is less about one “side” or the other misleading with research, but rather something much more difficult to address: Common misconceptions that impede deciphering good evidence from bad.

The Cartography Of High Expectations

In October of last year, the education advocacy group ConnCAN published a report called “The Roadmap to Closing the Gap” in Connecticut. This report says that the state must close its large achievement gaps by 2020 – that is, within eight years – and they use to data to argue that this goal is “both possible and achievable."

There is value in compiling data and disaggregating them by district and school. And ConnCAN, to its credit, doesn't use this analysis as a blatant vehicle to showcase its entire policy agenda, as advocacy organizations often do. But I am compelled to comment on this report, mostly as a springboard to a larger point about expectations.

However, first things first – a couple of very quick points about the analysis. There are 60-70 pages of district-by-district data in this report, all of it portrayed as a “roadmap” to closing Connecticut’s achievement gap. But it doesn't measure gaps and won't close them.

Are Charter Schools Better Able To Fire Low-Performing Teachers?

Charter schools, though they comprise a remarkably diverse sector, are quite often subject to broad generalizations. Opponents, for example, promote the characterization of charters as test prep factories, though this is a sweeping claim without empirical support. Another common stereotype is that charter schools exclude students with special needs. It is often (but not always) true that charters serve disproportionately fewer students with disabilities, but the reasons for this are complicated and vary a great deal, and there is certainly no evidence for asserting a widespread campaign of exclusion.

Of course, these types of characterizations, which are also leveled frequently at regular public schools, don't always take the form of criticism. For instance, it is an article of faith among many charter supporters that these schools, thanks to the fact that relatively few are unionized, are better able to aggressively identify and fire low-performing teachers (and, perhaps, retain high performers). Unlike many of the generalizations from both "sides," this one is a bit more amenable to empirical testing.

A recent paper by Joshua Cowen and Marcus Winters, published in the journal Education Finance and Policy, is among the first to take a look, and some of the results might be surprising.

A Few Points About The Instability Of Value-Added Estimates

One of the most frequent criticisms of value-added and other growth models is that they are "unstable" (or, more accurately, modestly stable). For instance, a teacher who is rated highly in one year might very well score toward the middle of the distribution – or even lower – in the next year (see here, here and here, or this accessible review).

Some of this year-to-year variation is “real." A teacher might get better over the course of a year, or might have a personal problem that impedes their job performance. In addition, there could be changes in educational circumstances that are not captured by the models – e.g., a change in school leadership, new instructional policies, etc. However, a great deal of the the recorded variation is actually due to sampling error, or idiosyncrasies in student testing performance. In other words, there is a lot of “purely statistical” imprecision in any given year, and so the scores don’t always “match up” so well between years. As a result, value-added critics, including many teachers, argue that it’s not only unfair to use such error-prone measures for any decisions, but that it’s also bad policy, since we might reward or punish teachers based on estimates that could be completely different the next year.

The concerns underlying these arguments are well-founded (and, often, casually dismissed by supporters and policymakers). At the same time, however, there are a few points about the stability of value-added (or lack thereof) that are frequently ignored or downplayed in our public discourse. All of them are pretty basic and have been noted many times elsewhere, but it might be useful to discuss them very briefly. Three in particular stand out.

When Growth Isn't Really Growth

Let’s try a super-simple thought experiment with data. Suppose we have an inner-city middle school serving grades 6-8. Students in all three grades take the state exam annually (in this case, we’ll say that it’s at the very beginning of the year). Now, for the sake of this illustration, let’s avail ourselves of the magic of hypotheticals and assume away many of the sources of error that make year-to-year changes in public testing data unreliable.

First, we’ll say that this school reports test scores instead of proficiency rates, and that the scores are comparable between grades. Second, every year, our school welcomes a new cohort of sixth graders that is the exact same size and has the exact same average score as preceding cohorts – 30 out of 100, well below the state average of 65. Third and finally, there is no mobility at this school. Every student who enters sixth grade stays there for three years, and goes to high school upon completion of eighth grade. No new students are admitted mid-year.

Okay, here’s where it gets interesting: Suppose this school is phenomenally effective in boosting its students’ scores. In fact, each year, every single student gains 20 points. It is the highest growth rate in the state. Believe it or not, using the metrics we commonly use to judge schoolwide “growth” or "gains," this school would still look completely ineffective. Take a look at the figure below.

A Simple Choice Of Words Can Help Avoid Confusion About New Test Results

In 1998, the National Institutes of Health (NIH) lowered the threshold at which people are classified as “overweight." Literally overnight, about 25 million Americans previously considered as having a healthy weight were now overweight. If, the next day, you saw a newspaper headline that said “number of overweight Americans increases," you would probably find that a little misleading. America’s “overweight” population didn’t really increase; the definition changed.

Fast forward to November 2012, during which Kentucky became the first state to release results from new assessments that were aligned with the Common Core Standards (CCS). This led to headlines such as, "Scores Drop on Kentucky’s Common Core-Aligned Tests" and "Challenges Seen as Kentucky’s Test Scores Drop As Expected." Yet, these descriptions unintentionally misrepresent what happened. It's not quite accurate - or at least highly imprecise - to say that test scores “dropped," just as it would have been wrong to say that the number of overweight Americans increased overnight in 1998 (actually, they’re not even scores, they’re proficiency rates). Rather, the state adopted different tests, with different content, a different design, and different standards by which students are deemed “proficient."

Over the next 2-3 years, a large group of states will also release results from their new CCS-aligned tests. It is important for parents, teachers, administrators, and other stakeholders to understand what the results mean. Most of them will rely on newspapers and blogs, and so one exceedingly simple step that might help out is some polite, constructive language-policing.

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