School Effects

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 Case Against Assigning Single Ratings To Schools

The new breed of school rating systems, some of which are still getting off the ground, will co-exist with federal proficiency targets in many states, and they are (or will be) used for a variety of purposes, including closure, resource allocation and informing parents and the public (see our posts on the systems in IN, FL, OH, CO, NYC).*

The approach that most states are using, in part due to the "ESEA flexibility" guidelines set by the U.S. Department of Education, is to combine different types of measures, often very crudely, into a single grade or categorical rating for each school. Administrators and media coverage usually characterize these ratings as measures of school performance - low-rated schools are called "low performing," while those receiving top ratings are characterized as "high performing." That's not accurate - or, at best, it's only partially true.

Some of the indicators that comprise the ratings, such as proficiency rates, are best interpreted as (imperfectly) describing student performance on tests, whereas other measures, such as growth model estimates, make some attempt to isolate schools’ contribution to that performance. Both might have a role to play in accountability systems, but they're more or less appropriate depending on how you’re trying to use them.

So, here’s my question: Why do we insist on throwing them all together into a single rating for each school? To illustrate why I think this question needs to be addressed, let’s take a quick look at four highly-simplified situations in which one might use ratings.

When You Hear Claims That Policies Are Working, Read The Fine Print

When I point out that raw changes in state proficiency rates or NAEP scores are not valid evidence that a policy or set of policies is “working," I often get the following response: “Oh Matt, we can’t have a randomized trial or peer-reviewed article for everything. We have to make decisions and conclusions based on imperfect information sometimes."

This statement is obviously true. In this case, however, it's also a straw man. There’s a huge middle ground between the highest-quality research and the kind of speculation that often drives our education debate. I’m not saying we always need experiments or highly complex analyses to guide policy decisions (though, in general, these are always preferred and sometimes required). The point, rather, is that we shouldn’t draw conclusions based on evidence that doesn't support those conclusions.

This, unfortunately, happens all the time. In fact, many of the more prominent advocates in education today make their cases based largely on raw changes in outcomes immediately after (or sometimes even before) their preferred policies were implemented (also see here, here, here, here, here, and here). In order to illustrate the monumental assumptions upon which these and similar claims ride, I thought it might be fun to break them down quickly, in a highly simplified fashion. So, here are the four “requirements” that must be met in order to attribute raw test score changes to a specific policy (note that most of this can be applied not only to claims that policies are working, but also to claims that they're not working because scores or rates are flat):

The Structural Curve In Indiana's New School Grading System

The State of Indiana has received a great deal of attention for its education reform efforts, and they recently announced the details, as well as the first round of results, of their new "A-F" school grading system. As in many other states, for elementary and middle schools, the grades are based entirely on math and reading test scores.

It is probably the most rudimentary scoring system I've seen yet - almost painfully so. Such simplicity carries both potential advantages (easier for stakeholders to understand) and disadvantages (school performance is complex and not always amenable to rudimentary calculation).

In addition, unlike the other systems that I have reviewed here, this one does not rely on explicit “weights," (i.e., specific percentages are not assigned to each component). Rather, there’s a rubric that combines absolute performance (passage rates) and proportions drawn from growth models (a few other states use similar schemes, but I haven't reviewed any of them).

On the whole, though, it's a somewhat simplistic variation on the general approach most other states are taking -- but with a few twists.

Which State Has "The Best Schools?"

** Reprinted here in the Washington Post

I’ve written many times about how absolute performance levels – how highly students score – are not by themselves valid indicators of school quality, since, most basically, they don’t account for the fact that students enter the schooling system at different levels. One of the most blatant (and common) manifestations of this mistake is when people use NAEP results to determine the quality of a state's schools.

For instance, you’ll often hear that Massachusetts has the “best” schools in the U.S. and Mississippi the “worst," with both claims based solely on average scores on the NAEP (though, technically, Massachusetts public school students' scores are statistically tied with at least one other state on two of the four main NAEP exams, while Mississippi's rankings vary a bit by grade/subject, and its scores are also not statistically different from several other states').

But we all know that these two states are very different in terms of basic characteristics such as income, parental education, etc. Any assessment of educational quality, whether at the state or local level, is necessarily complicated, and ignoring differences between students precludes any meaningful comparisons of school effectiveness. Schooling quality is important, but it cannot be assessed by sorting and ranking raw test scores in a spreadsheet.

The Stability And Fairness Of New York City's School Ratings

New York City has just released the new round of results from its school rating system (they're called “progress reports"). It relies considerably more on student growth (60 out of 100 points) than absolute performance (25 points), and there are efforts to partially adjust most of the measures via peer group comparisons.*

All of this indicates that the city's system is more focused on school rather than student test-based performance, compared with many other systems around the U.S.

The ratings are high-stakes. Schools receiving low grades – a D or F in any given year, or a C for three consecutive years – enter a review process by which they might be closed. The number of schools meeting these criteria jumped considerably this year.

There is plenty of controversy to go around about the NYC ratings, much of it pertaining to two important features of the system. They’re worth discussing briefly, as they are also applicable to systems in other states.

Does It Matter How We Measure Schools' Test-Based Performance?

In education policy debates, we like the "big picture." We love to say things like “hold schools accountable” and “set high expectations." Much less frequent are substantive discussions about the details of accountability systems, but it’s these details that make or break policy. The technical specs just aren’t that sexy. But even the best ideas with the sexiest catchphrases won’t improve things a bit unless they’re designed and executed well.

In this vein, I want to recommend a very interesting CALDER working paper by Mark Ehlert, Cory Koedel, Eric Parsons and Michael Podgursky. The paper takes a quick look at one of these extremely important, yet frequently under-discussed details in school (and teacher) accountability systems: The choice of growth model.

When value-added or other growth models come up in our debates, they’re usually discussed en masse, as if they’re all the same. They’re not. It's well-known (though perhaps overstated) that different models can, in many cases, lead to different conclusions for the same school or teacher. This paper, which focuses on school-level models but might easily be extended to teacher evaluations as well, helps illustrate this point in a policy-relevant manner.

The Louisiana Voucher Accountability Sweepstakes

The situation with vouchers in Louisiana is obviously quite complicated, and there are strong opinions on both sides of the issue, but I’d like to comment quickly on the new “accountability” provision. It's a great example of how, too often, people focus on the concept of accountability and ignore how it is actually implemented in policy.

Quick and dirty background: Louisiana will be allowing students to receive vouchers (tuition to attend private schools) if their public schools are sufficiently low-performing, according to their "school performance score" (SPS). As discussed here, the SPS is based primarily on how highly students score, rather than whether they’re making progress, and thus tells you relatively little about the actual effectiveness of schools per se. For instance, the vouchers will be awarded mostly to schools serving larger proportions of disadvantaged students, even if many of those schools are compelling large gains (though such progress cannot be assessed adequately using year-to-year changes in the SPS, which, due in part to its reliance on cross-sectional proficiency rates, are extremely volatile).

Now, here's where things get really messy: In an attempt to demonstrate that they are holding the voucher-accepting private schools accountable, Louisiana officials have decided that they will make these private schools ineligible for the program if their performance is too low (after at least two years of participation in the program). That might be a good idea if the state measured school performance in a defensible manner. It doesn't.

Schools Aren't The Only Reason Test Scores Change

In all my many posts about the interpretation of state testing data, it seems that I may have failed to articulate one major implication, which is almost always ignored in the news coverage of the release of annual testing data. That is: raw, unadjusted changes in student test scores are not by themselves very good measures of schools' test-based effectiveness.

In other words, schools can have a substantial impact on performance, but student test scores also increase, decrease or remain flat for reasons that have little or nothing to do with schools. The first, most basic reason is error. There is measurement error in all test scores - for various reasons, students taking the same test twice will get different scores, even if their "knowledge" remains constant. Also, as I've discussed many times, there is extra imprecision when using cross-sectional data. Often, any changes in scores or rates, especially when they’re small in magnitude and/or based on smaller samples (e.g., individual schools), do not represent actual progress (see here and here). Finally, even when changes are "real," other factors that influence test score changes include a variety of non-schooling inputs, such as parental education levels, family's economic circumstances, parental involvement, etc. These factors don't just influence how highly students score; they are also associated with progress (that's why value-added models exist).

Thus, to the degree that test scores are a valid measure of student performance, and changes in those scores a valid measure of student learning, schools aren’t the only suitors at the dance. We should stop judging school or district performance by comparing unadjusted scores or rates between years.

Colorado's Questionable Use Of The Colorado Growth Model

I have been writing critically about states’ school rating systems (e.g., Ohio, Florida, Louisiana), and I thought I would find one that is, at least in my (admittedly value-laden) opinion, more defensibly designed. It didn't quite turn out as I had hoped.

One big starting point in my assessment is how heavily the systems weight absolute performance (how highly students score) versus growth (how quickly students improve). As I’ve argued many times, the former (absolute level) is a poor measure of school performance in a high-stakes accountability system. It does not address the fact that some schools, particularly those in more affluent areas, serve  students who, on average, enter the system at a higher-performing level. This amounts to holding schools accountable for outcomes they largely cannot control (see Doug Harris' excellent book for more on this in the teacher context). Thus, to whatever degree testing results can be used to judge actual school effectiveness, growth measures, while themselves highly imperfect, are to be preferred in a high-stakes context.

There are a few states that assign more weight to growth than absolute performance (see this prior post on New York City’s system). One of them is Colorado's system, which uses the well-known “Colorado Growth Model” (CGM).*

In my view, putting aside the inferential issues with the CGM (see the first footnote), the focus on growth in Colorado's system is in theory a good idea. But, looking at the data and documentation reveals a somewhat unsettling fact: There is a double standard of sorts, by which two schools with the same growth score can receive different ratings, and it's mostly their absolute performance levels determining whether this is the case.