Value-Added

The Persistent Misidentification Of "Low Performing Schools"

In education, we hear the terms “failing school” and “low-performing school” quite frequently. Usually, they are used in soundbyte-style catchphrases such as, “We can’t keep students trapped in ‘failing schools.’” Sometimes, however, they are used to refer to a specific group of schools in a given state or district that are identified as “failing” or “low-performing” as part of a state or federal law or program (e.g., waivers, SIG). There is, of course, interstate variation in these policies, but one common definition is that schools are “failing/low-performing” if their proficiency rates are in the bottom five percent statewide.

Putting aside the (important) issues with judging schools based solely on standardized testing results, low proficiency rates (or low average scores) tell you virtually nothing about whether or not a school is “failing.” As we’ve discussed here many times, students enter their schools performing at different levels, and schools cannot control the students they serve, only how much progress those students make while they’re in attendance (see here for more).

From this perspective, then, there may be many schools that are labeled “failing” or “low performing” but are actually of above average effectiveness in raising test scores. And, making things worse, virtually all of these will be schools that serve the most disadvantaged students. If that’s true, it’s difficult to think of anything more ill-advised than closing these schools, or even labeling them as “low performing.” Let’s take a quick, illustrative look at this possibility using the “bottom five percent” criterion, and data from Colorado in 2013-14 (note that this simple analysis is similar to what I did in this post, but this one is a little more specific; also see Glazerman and Potamites 2011; Ladd and Lauen 2010; and especially Chingos and West 2015).

Multiple Measures And Singular Conclusions In A Twin City

A few weeks ago, the Minneapolis Star Tribune published teacher evaluation results for the district’s public school teachers in 2013-14. This decision generated a fair amount of controversy, but it’s worth noting that the Tribune, unlike the Los Angeles Times and New York City newspapers a few years ago, did not publish scores for individual teachers, only totals by school.

The data once again provide an opportunity to take a look at how results vary by student characteristics. This was indeed the focus of the Tribune’s story, which included the following headline: “Minneapolis’ worst teachers are in the poorest schools, data show." These types of conclusions, which simply take the results of new evaluations at face value, have characterized the discussion since the first new systems came online. Though understandable, they are also frustrating and a potential impediment to the policy process. At this early point, “the city’s teachers with the lowest evaluation ratings” is not the same thing as “the city’s worst teachers." Actually, as discussed in a previous post, the systematic variation in evaluation results by student characteristics, which the Tribune uses to draw conclusions about the distribution of the city’s “worst teachers," could just as easily be viewed as one of the many ways that one might assess the properties and even the validity of those results.

So, while there are no clear-cut "right" or "wrong" answers here, let’s take a quick look at the data and what they might tell us.

The Great Teacher Evaluation Evaluation: New York Edition

A couple of weeks ago, the New York State Education Department (NYSED) released data from the first year of the state's new teacher and principal evaluation system (called the “Annual Professional Performance Review," or APPR). In what has become a familiar pattern, this prompted a wave of criticism from advocates, much of it focused on the proportion of teachers in the state to receive the lowest ratings.

To be clear, evaluation systems that produce non-credible results should be examined and improved, and that includes those that put implausible proportions of teachers in the highest and lowest categories. Much of the commentary surrounding this and other issues has been thoughtful and measured. As usual, though, there have been some oversimplified reactions, as exemplified by this piece on the APPR results from Students First NY (SFNY).

SFNY notes what it considers to be the low proportion of teachers rated “ineffective," and points out that there was more differentiation across rating categories for the state growth measure (worth 20 percent of teachers’ final scores), compared with the local “student learning” measure (20 percent) and the classroom observation components (60 percent). Based on this, they conclude that New York’s "state test is the only reliable measure of teacher performance" (they are actually talking about validity, not reliability, but we’ll let that go). Again, this argument is not representative of the commentary surrounding the APPR results, but let’s use it as a springboard for making a few points, most of which are not particularly original. (UPDATE: After publication of this post, SFNY changed the headline of their piece from "the only reliable measure of teacher performance" to "the most reliable measure of teacher performance.")

A Quick Look At The ASA Statement On Value-Added

Several months ago, the American Statistical Association (ASA) released a statement on the use of value-added models in education policy. I’m a little late getting to this (and might be repeating points that others made at the time), but I wanted to comment on the statement, not only because I think it's useful to have ASA add their perspective to the debate on this issue, but also because their statement seems to have become one of the staple citations for those who oppose the use of these models in teacher evaluations and other policies.

Some of these folks claimed that the ASA supported their viewpoint – i.e., that value-added models should play no role in accountability policy. I don’t agree with this interpretation. To be sure, the ASA authors described the limitations of these estimates, and urged caution, but I think that the statement rather explicitly reaches a more nuanced conclusion: That value-added estimates might play a useful role in education policy, as one among several measures used in formal accountability systems, but this must be done carefully and appropriately.*

Much of the statement puts forth the standard, albeit important, points about value-added (e.g., moderate stability between years/models, potential for bias, etc.). But there are, from my reading, three important takeaways that bear on the public debate about the use of these measures, which are not always so widely acknowledged.

Lost In Citation

The so-called Vergara trial in California, in which the state’s tenure and layoff statutes were deemed unconstitutional, already has its first “spin-off," this time in New York, where a newly-formed organization, the Partnership for Educational Justice (PEJ), is among the organizations and entities spearheading the effort.

Upon first visiting PEJ’s new website, I was immediately (and predictably) drawn to the “Research” tab. It contains five statements (which, I guess, PEJ would characterize as “facts”). Each argument is presented in the most accessible form possible, typically accompanied by one citation (or two at most). I assume that the presentation of evidence in the actual trial will be a lot more thorough than that offered on this webpage, which seems geared toward the public rather than the more extensive evidentiary requirements of the courtroom (also see Bruce Baker’s comments on many of these same issues surrounding the New York situation).

That said, I thought it might be useful to review the basic arguments and evidence PEJ presents, not really in the context of whether they will “work” in the lawsuit (a judgment I am unqualified to make), but rather because they're very common, and also because it's been my observation that advocates, on both “sides” of the education debate, tend to be fairly good at using data and research to describe problems and/or situations, yet sometimes fall a bit short when it comes to evidence-based discussions of what to do about them (including the essential task of acknowledging when the evidence is still undeveloped). PEJ’s five bullet points, discussed below, are pretty good examples of what I mean.

The Language Of Teacher Effectiveness

There is a tendency in education circles these days, one that I'm sure has been discussed by others, and of which I myself have been "guilty," on countless occasions. The tendency is to use terms such “effective/ineffective teacher” or “teacher performance” interchangeably with estimates from value-added and other growth models.

Now, to be clear, I personally am not opposed to the use of value-added estimates in teacher evaluations and other policies, so long as it is done cautiously and appropriately (which, in my view, is not happening in very many places). Moreover, based on my reading of the research, I believe that these estimates can provide useful information about teachers’ performance in the classroom. In short, then, I am not disputing whether value-added scores should be considered to be one useful proxy measure for teacher performance and effectiveness (and described as such), both formally and informally.

Regardless of one's views on value-added and its policy deployment, however, there is a point at which our failure to define terms can go too far, and perhaps cause confusion.

The Proportionality Principle In Teacher Evaluations

Our guest author today is Cory Koedel, Assistant Professor of Economics at the University of Missouri.

In a 2012 post on this blog, Dr. Di Carlo reviewed an article that I coauthored with colleagues Mark Ehlert, Eric Parsons and Michael Podgursky. The initial article (full version here, or for a shorter, less-technical version, see here) argues for the policy value of growth models that are designed to force comparisons to be between schools and teachers in observationally-similar circumstances.

The discussion is couched within the context of achieving three key policy objectives that we associate with the adoption of more-rigorous educational evaluation systems: (1) improving system-wide instruction by providing useful performance signals to schools and teachers; (2) eliciting optimal effort from school personnel; and (3) ensuring that current labor-market inequities between advantaged and disadvantaged schools are not exacerbated by the introduction of the new systems.

We argue that a model that forces comparisons to be between equally-circumstanced schools and teachers – which we describe as a “proportional” model – is best-suited to achieve these policy objectives. The conceptual appeal of the proportional approach is that it fully levels the playing field between high- and low-poverty schools. In contrast, some other growth models have been shown to produce estimates that are consistently associated with the characteristics of students being served (e.g., Student Growth Percentiles).

Expectations For Student Performance Under NCLB Waivers

A recent story in the Chicago Tribune notes that Illinois’ NCLB waiver plan sets lower targets for certain student subgroups, including minority and low-income students. This, according to the article, means that “Illinois students of different backgrounds no longer will be held to the same standards," and goes on to quote advocates who are concerned that this amounts to lower expectations for traditionally lower-scoring groups of children.

The argument that expectations should not vary by student characteristics is, of course, valid and important. Nevertheless, as Chad Aldeman notes, the policy of setting different targets for different groups of students has been legally required since the enactment of NCLB, under which states must “give credit to lower-performing groups that demonstrate progress." This was supposed to ensure, albeit with exceedingly crude measures, that schools weren't punished due to the students they serve, and how far behind were those students upon entry into the schools.

I would take that a step further by adding two additional points. The first is quite obvious, and is mentioned briefly in the Tribune article, but too often is obscured in these kinds of conversations: Neither NCLB nor the waivers actually hold students to different standards. The cut scores above which students are deemed “proficient," somewhat arbitrary though they may be, do not vary by student subgroup, or by any other factor within a given state. All students are held to the same exact standard.