Education Reporting

Five Recommendations For Reporting On (Or Just Interpreting) State Test Scores

From my experience, education reporters are smart, knowledgeable, and attentive to detail. That said, the bulk of the stories about testing data – in big cities and suburbs, in this year and in previous years – could be better.

Listen, I know it’s unreasonable to expect every reporter and editor to address every little detail when they try to write accessible copy about complicated issues, such as test data interpretation. Moreover, I fully acknowledge that some of the errors to which I object – such as calling proficiency rates “scores” – are well within tolerable limits, and that news stories need not interpret data in the same way as researchers. Nevertheless, no matter what you think about the role of test scores in our public discourse, it is in everyone’s interest that the coverage of them be reliable. And there are a few mostly easy suggestions that I think would help a great deal.

Below are five such recommendations. They are of course not meant to be an exhaustive list, but rather a quick compilation of points, all of which I’ve discussed in previous posts, and all of which might also be useful to non-journalists.

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.

The Unfortunate Truth About This Year's NYC Charter School Test Results

There have now been several stories in the New York news media about New York City’s charter schools’ “gains” on this year’s state tests (see here, here, here, here and here). All of them trumpeted the 3-7 percentage point increase in proficiency among the city’s charter students, compared with the 2-3 point increase among their counterparts in regular public schools. The consensus: Charters performed fantastically well this year.

In fact, the NY Daily News asserted that the "clear lesson" from the data is that "public school administrators must gain the flexibility enjoyed by charter leaders," and "adopt [their] single-minded focus on achievement." For his part, Mayor Michael Bloomberg claimed that the scores are evidence that the city should expand its charter sector.

All of this reflects a fundamental misunderstanding of how to interpret testing data, one that is frankly a little frightening to find among experienced reporters and elected officials.

How Often Do Proficiency Rates And Average Scores Move In Different Directions?

New York State is set to release its annual testing data today. Throughout the state, and especially in New York City, we will hear a lot about changes in school and district proficiency rates. The rates themselves have advantages – they are easy to understand, comparable across grades and reflect a standards-based goal. But they also suffer severe weaknesses, such as their sensitivity to where the bar is set and the fact that proficiency rates and the actual scores upon which they’re based can paint very different pictures of student performance, both in a given year as well as over time. I’ve discussed this latter issue before in the NYC context (and elsewhere), but I’d like to revisit it quickly.

Proficiency rates can only tell you how many students scored above a certain line; they are completely uninformative as to how far above or below that line the scores might be. Consider a hypothetical example: A student who is rated as proficient in year one might make large gains in his or her score in year two, but this would not be reflected in the proficiency rate for his or her school – in both years, the student would just be coded as “proficient” (the same goes for large decreases that do not “cross the line”). As a result, across a group of students, the average score could go up or down while proficiency rates remained flat or moved in the opposite direction. Things are even messier when data are cross-sectional (as public data lmost always are), since you’re comparing two different groups of students (see this very recent NYC IBO report).

Let’s take a rough look at how frequently rates and scores diverge in New York City.

Low-Income Students In The CREDO Charter School Study

A recent Economist article on charter schools, though slightly more nuanced than most mainstream media treatments of the charter evidence, contains a very common, somewhat misleading argument that I’d like to address quickly. It’s about the findings of the so-called "CREDO study," the important (albeit over-cited) 2009 national comparison of student achievement in charter and regular public schools in 16 states.

Specifically, the article asserts that the CREDO analysis, which finds a statistically discernible but very small negative impact of charters overall (with wide underlying variation), also finds a significant positive effect among low-income students. This leads the Economist to conclude that the entire CREDO study “has been misinterpreted," because it’s real value is in showing that “the children who most need charters have been served well."

Whether or not an intervention affects outcomes among subgroups of students is obviously important (though one has hardly "misinterpreted" a study by focusing on its overall results). And CREDO does indeed find a statistically significant, positive test-based impact of charters on low-income students, vis-à-vis their counterparts in regular public schools. However, as discussed here (and in countless textbooks and methods courses), statistical significance only means we can be confident that the difference is non-zero (it cannot be chalked up to random fluctuation). Significant differences are often not large enough to be practically meaningful.

And this is certainly the case with CREDO and low-income students.

The Busy Intersection Of Test-Based Accountability And Public Perception

Last year, the New York City Department of Education (NYCDOE) rolled out its annual testing results for the city’s students in a rather misleading manner. The press release touted the “significant progress” between 2010 and 2011 among city students, while, at a press conference, Mayor Michael Bloomberg called the results “dramatic." In reality, however, the increase in proficiency rates (1-3 percentage points) was very modest, and, more importantly, the focus on the rates hid the fact that actual scale scores were either flat or decreased in most grades. In contrast, one year earlier, when the city's proficiency rates dropped due to the state raising the cut scores, Mayor Bloomberg told reporters (correctly) that it was the actual scores that "really matter."

Most recently, in announcing their 2011 graduation rates, the city did it again. The headline of the NYCDOE press release proclaims that “a record number of students graduated from high school in 2011." This may be technically true, but the actual increase in the rate (rather than the number of graduates) was 0.4 percentage points, which is basically flat (as several reporters correctly noted). In addition, the city's "college readiness rate" was similarly stagnant, falling slightly from 21.4 percent to 20.7 percent, while the graduation rate increase was higher both statewide and in New York State's four other large districts (the city makes these comparisons when they are favorable).*

We've all become accustomed to this selective, exaggerated presentation of testing data, which is of course not at all limited to NYC. And it illustrates the obvious fact that test-based accountability plays out in multiple arenas, formal and informal, including the court of public opinion.

Three Important Distinctions In How We Talk About Test Scores

In education discussions and articles, people (myself included) often say “achievement” when referring to test scores, or “student learning” when talking about changes in those scores. These words reflect implicit judgments to some degree (e.g., that the test scores actually measure learning or achievement). Every once in a while, it’s useful to remind ourselves that scores from even the best student assessments are imperfect measures of learning. But this is so widely understood - certainly in the education policy world, and I would say among the public as well - that the euphemisms are generally tolerated.

And then there are a few common terms or phrases that, in my personal opinion, are not so harmless. I’d like to quickly discuss three of them (all of which I’ve talked about before). All three appear many times every day in newspapers, blogs, and regular discussions. To criticize their use may seem like semantic nitpicking to some people, but I would argue that these distinctions are substantively important and may not be so widely-acknowledged, especially among people who aren’t heavily engaged in education policy (e.g., average newspaper readers).

So, here they are, in no particular order.

Measuring Journalist Quality

Journalists play an essential role in our society. They are charged with informing the public, a vital function in a representative democracy. Yet, year after year, large pockets of the electorate remain poorly-informed on both foreign and domestic affairs. For a long time, commentators have blamed any number of different culprits for this problem, including poverty, education, increasing work hours and the rapid proliferation of entertainment media.

There is no doubt that these and other factors matter a great deal. Recently, however, there is growing evidence that the factors shaping the degree to which people are informed about current events include not only social and economic conditions, but journalist quality as well. Put simply, better journalists produce better stories, which in turn attract more readers. On the whole, the U.S. journalist community is world class. But there is, as always, a tremendous amount of underlying variation. It’s likely that improving the overall quality of reporters would not only result in higher quality information, but it would also bring in more readers. Both outcomes would contribute to a better-informed, more active electorate.

We at the Shanker Institute feel that it is time to start a public conversation about this issue. We have requested and received datasets documenting the story-by-story readership of the websites of U.S. newspapers, large and small. We are using these data in statistical models that we call “Readers-Added Models," or “RAMs."

Dispatches From The Nexus Of Bad Research And Bad Journalism

In a recent story, the New York Daily News uses the recently-released teacher data reports (TDRs) to “prove” that the city’s charter school teachers are better than their counterparts in regular public schools. The headline announces boldly: New York City charter schools have a higher percentage of better teachers than public schools (it has since been changed to: "Charters outshine public schools").

Taking things even further, within the article itself, the reporters note, “The newly released records indicate charters have higher performing teachers than regular public schools."

So, not only are they equating words like “better” with value-added scores, but they’re obviously comfortable drawing conclusions about these traits based on the TDR data.

The article is a pretty remarkable display of both poor journalism and poor research. The reporters not only attempted to do something they couldn’t do, but they did it badly to boot. It’s unfortunate to have to waste one’s time addressing this kind of thing, but, no matter your opinion on charter schools, it's a good example of how not to use the data that the Daily News and other newspapers released to the public.

Reign Of Error: The Publication Of Teacher Data Reports In New York City

Late last week and over the weekend, New York City newspapers, including the New York Times and Wall Street Journal, published the value-added scores (teacher data reports) for thousands of the city’s teachers. Prior to this release, I and others argued that the newspapers should present margins of error along with the estimates. To their credit, both papers did so.

In the Times’ version, for example, each individual teacher’s value-added score (converted to a percentile rank) is presented graphically, for math and reading, in both 2010 and over a teacher’s “career” (averaged across previous years), along with the margins of error. In addition, both papers provided descriptions and warnings about the imprecision in the results. So, while the decision to publish was still, in my personal view, a terrible mistake, the papers at least make a good faith attempt to highlight the imprecision.

That said, they also published data from the city that use teachers’ value-added scores to label them as one of five categories: low, below average, average, above average or high. The Times did this only at the school level (i.e., the percent of each school’s teachers that are “above average” or “high”), while the Journal actually labeled each individual teacher. Presumably, most people who view the databases, particularly the Journal's, will rely heavily on these categorical ratings, as they are easier to understand than percentile ranks surrounded by error margins. The inherent problems with these ratings are what I’d like to discuss, as they illustrate important concepts about estimation error and what can be done about it.