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

    Written on January 23, 2013

    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.

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  • A Few Points About The Instability Of Value-Added Estimates

    Written on January 17, 2013

    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.

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

    Written on November 19, 2012

    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 hereherehereherehere, 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):

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  • The Impact Of Race To The Top Is An Open Question (But At Least It's Being Asked)

    Written on September 13, 2012

    You don’t have to look very far to find very strong opinions about Race to the Top (RTTT), the U.S. Department of Education’s (USED) stimulus-funded state-level grant program (which has recently been joined by a district-level spinoff). There are those who think it is a smashing success, while others assert that it is a dismal failure. The truth, of course, is that these claims, particularly the extreme views on either side, are little more than speculation.*

    To win the grants, states were strongly encouraged to make several different types of changes, such as adoption of new standards, the lifting/raising of charter school caps, the installation of new data systems and the implementation of brand new teacher evaluations. This means that any real evaluation of the program’s impact will take some years and will have to be multifaceted – that is, it is certain that the implementation/effects will vary not only by each of these components, but also between states.

    In other words, the success or failure of RTTT is an empirical question, one that is still almost entirely open. But there is a silver lining here: USED is at least asking that question, in the form of a five-year, $19 million evaluation program, administered through the National Center for Education Evaluation and Regional Assistance, designed to assess the impact and implementation of various RTTT-fueled policy changes, as well as those of the controversial School Improvement Grants (SIGs).

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  • Do Top Teachers Produce "A Year And A Half Of Learning?"

    Written on September 11, 2012

    One claim that gets tossed around a lot in education circles is that “the most effective teachers produce a year and a half of learning per year, while the least effective produce a half of a year of learning."

    This talking point is used all the time in advocacy materials and news articles. Its implications are pretty clear: Effective teachers can make all the difference, while ineffective teachers can do permanent damage.

    As with most prepackaged talking points circulated in education debates, the “year and a half of learning” argument, when used without qualification, is both somewhat valid and somewhat misleading. So, seeing as it comes up so often, let’s very quickly identify its origins and what it means.

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  • Examining Principal Turnover

    Written on July 16, 2012

    Our guest author today is Ed Fuller, Associate Professor in the Education Leadership Department at Penn State University. He is also the Director of the Center for Evaluation and Education Policy Analysis as well as the Associate Director for Policy of the University Council for Educational Administration.

    “No one knows who I am," exclaimed a senior in a high-poverty, predominantly minority and low-performing high school in the Austin area. She explained, “I have been at this school four years and had four principals and six algebra I teachers."

    Elsewhere in Texas, the first school to be closed by the state for low performance was Johnston High School, which was led by 13 principals in the 11 years preceding closure. The school also had a teacher turnover rate greater than 25 percent for almost all of the years and greater than 30 percent for 7 of the years.

    While the above examples are rather extreme cases, they do underscore two interconnected issues – teacher and principal turnover - that often plague low-performing schools and, in the case of principal turnover, afflict a wide range of schools regardless of performance or school demographics.

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  • Low-Income Students In The CREDO Charter School Study

    Written on July 10, 2012

    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.

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  • The Data Are In: Experiments In Policy Are Worth It

    Written on July 9, 2012

    Our guest author today is David Dunning, professor of psychology at Cornell University, and a fellow of both the American Psychological Society and the American Psychological Association. 

    When I was a younger academic, I often taught a class on research methods in the behavioral sciences. On the first day of that class, I took as my mission to teach students only one thing—that conducting research in the behavioral sciences ages a person. I meant that in two ways. First, conducting research is humbling and frustrating. I cannot count the number of pet ideas I have had through the years, all of them beloved, that have gone to die in the laboratory at the hands of data unwilling to verify them.

    But, second, there is another, more positive way in which research ages a person. At times, data come back and verify a cherished idea, or even reveal a more provocative or valuable one that no one has never expected. It is a heady experience in those moments for the researcher to know something that perhaps no one else knows, to be wiser—more aged if you will—in a small corner of the human experience that he or she cares about deeply.

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  • Gender Pay Gaps And Educational Achievement Gaps

    Written on June 13, 2012

    There is currently an ongoing rhetorical war of sorts over the gender wage gap. One “side” makes the common argument that women earn around 75 cents on the male dollar (see here, for example).

    Others assert that the gender gap is a myth, or that it is so small as to be unimportant.

    Often, these types of exchanges are enough to exasperate the casual observer, and inspire claims such as “statistics can be made to say anything." In truth, however, the controversy over the gender gap is a good example of how descriptive statistics, by themselves, say nothing. What matters is how they’re interpreted.

    Moreover, the manner in which one must interpret various statistics on the gender gap applies almost perfectly to the achievement gaps that are so often mentioned in education debates.

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  • Quality Control In Charter School Research

    Written on May 18, 2012

    There's a fairly large body of research showing that charter schools vary widely in test-based performance relative to regular public schools, both by location as well as subgroup. Yet, you'll often hear people point out that the highest-quality evidence suggests otherwise (see here, here and here) - i.e., that there are a handful of studies using experimental methods (randomized controlled trials, or RCTs) and these analyses generally find stronger, more uniform positive charter impacts.

    Sometimes, this argument is used to imply that the evidence, as a whole, clearly favors charters, and, perhaps by extension, that many of the rigorous non-experimental charter studies - those using sophisticated techniques to control for differences between students - would lead to different conclusions were they RCTs.*

    Though these latter assertions are based on a valid point about the power of experimental studies (the few of which we have are often ignored in the debate over charters), they are dubiously overstated for a couple of reasons, discussed below. But a new report from the (indispensable) organization Mathematica addresses the issue head on, by directly comparing estimates of charter school effects that come from an experimental analysis with those from non-experimental analyses of the same group of schools.

    The researchers find that there are differences in the results, but many are not statistically significant and those that are don't usually alter the conclusions. This is an important (and somewhat rare) study, one that does not, of course, settle the issue, but does provide some additional tentative support for the use of strong non-experimental charter research in policy decisions.

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