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  • When Growth Isn't Really Growth, Part Two

    Written on March 31, 2014

    Last year, we published a post that included a very simple graphical illustration of what changes in cross-sectional proficiency rates or scores actually tell us about schools’ test-based effectiveness (basically nothing).

    In reality, year-to-year changes in cross-sectional average rates or scores may reflect "real" improvement, at least to some degree, but, especially when measured at the school- or grade-level, they tend to be mostly error/imprecision (e.g., changes in the composition of the samples taking the test, measurement error and serious issues with converting scores to rates using cutpoints). This is why changes in scores often conflict with more rigorous indicators that employ longitudinal data.

    In the aforementioned post, however, I wanted to show what the changes meant even if most of these issues disappeared magicallyIn this one, I would like to extend this very simple illustration, as doing so will hopefully help shed a bit more light on the common (though mistaken) assumption that effective schools or policies should generate perpetual rate/score increases.

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  • When Checking Under The Hood Of Overall Test Score Increases, Use Multiple Tools

    Written on February 24, 2014

    When looking at changes in testing results between years, many people are (justifiably) interested in comparing those changes for different student subgroups, such as those defined by race/ethnicity or income (subsidized lunch eligibility). The basic idea is to see whether increases are shared between traditionally advantaged and disadvantaged groups (and, often, to monitor achievement gaps).

    Sometimes, people take this a step further by using the subgroup breakdowns as a crude check on whether cross-sectional score changes are due to changes in the sample of students taking the test. The logic is as follows: If the increases are found when comparing advantaged and more disadvantaged cohorts, then an overall increase cannot be attributed to a change in the backgrounds of students taking the test, as the subgroups exhibited the same pattern. (For reasons discussed here many times before, this is a severely limited approach.)

    Whether testing data are cross-sectional or longitudinal, these subgroup breakdowns are certainly important and necessary, but it's wise to keep in mind that standard variables, such as eligibility for free and reduced-price lunches (FRL), are imperfect proxies for student background (actually, FRL rates aren't even such a great proxy for income). In fact, one might reach different conclusions depending on which variables are chosen. To illustrate this, let’s take a look at results from the Trial Urban District Assessment (TUDA) for the District of Columbia Public Schools between 2011 and 2013, in which there was a large overall score change that received a great deal of media attention, and break the changes down by different characteristics.

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  • Select Your Conclusions, Apply Data

    Written on February 19, 2014

    The recent release of the National Assessment of Educational Progress (NAEP) and the companion Trial Urban District Assessment (TUDA) was predictably exploited by advocates to argue for their policy preferences. This is a blatant misuse of the data for many reasons that I have discussed here many times before, and I will not repeat them.

    I do, however, want to very quickly illustrate the emptiness of this pseudo-empirical approach – finding cross-sectional cohort increases in states/districts that have recently acted policies you support, and then using the increases as evidence that the policies “work." For example, the recent TUDA results for the District of Columbia Public Schools (DCPS), where scores increased in all four grade/subject combinations, were immediately seized upon supporters of the reforms that have been enacted by DCPS as clear-cut evidence of the policy triumph. The celebrators included the usual advocates, but also DCPS Chancellor Kaya Henderson and the U.S. Secretary of Education Arne Duncan (there was even a brief mention by President Obama in his State of The Union speech).

    My immediate reaction to this bad evidence was simple (though perhaps slightly juvenile) – find a district that had similar results under a different policy environment. It was, as usual, pretty easy: Los Angeles Unified School District (LAUSD).

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  • Is Selective Admission A School Improvement Plan?

    Written on January 23, 2014

    The Washington Post reports that parents and alumni of D.C.’s Dunbar High School have quietly been putting together a proposal to revitalize what the article calls "one of the District's worst performing schools."

    Those behind the proposal are not ready to speak about it publicly, and details are still very thin, but the Post article reports that it calls for greater flexibility in hiring, spending and other core policies. Moreover, the core of the plan – or at least its most drastic element - is to make Dunbar a selective high school, to which students must apply and be accepted, presumably based on testing results and other performance indicators (the story characterizes the proposal as a whole with the term “autonomy”). I will offer no opinion as to whether this conversion, if it is indeed submitted to the District for consideration, is a good idea. That will be up to administrators, teachers, parents, and other stakeholders.

    I am, however, a bit struck by two interrelated aspects of this story. The first is the unquestioned characterization of Dunbar as a “low performing” or “struggling” school. This fateful label appears to be based mostly on the school’s proficiency rates, which are indeed dismally low – 20 percent in math and 29 percent in reading.

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  • Being Kevin Huffman

    Written on December 11, 2013

    In a post earlier this week, I noted how several state and local education leaders, advocates and especially the editorial boards of major newspapers used the results of the recently-released NAEP results inappropriately – i.e., to argue that recent reforms in states such as Tennessee and D.C. are “working." I also discussed how this illustrates a larger phenomenon in which many people seem to expect education policies to generate immediate, measurable results in terms of aggregate student test scores, which I argued is both unrealistic and dangerous.

    Mike G. from Boston, a friend whose comments I always appreciate, agrees with me, but asks a question that I think gets to the pragmatic heart of the matter. He wonders whether individuals in high-level education positions have any alternative. For instance, Mike asks, what would I suggest to Kevin Huffman, who is the head of Tennessee’s education department? Insofar as Huffman’s opponents “would use any data…to bash him if it’s trending down," would I advise him to forego using the data in his favor when they show improvement?*

    I have never held any important high-level leadership positions. My political experience and skills are (and I’m being charitable here) underdeveloped, and I have no doubt many more seasoned folks in education would disagree with me. But my answer is: Yes, I would advise him to forego using the data in this manner. Here’s why.

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  • ESEA Waivers And The Perpetuation Of Poor Educational Measurement

    Written on November 26, 2013

    Some of the best research out there is a product not of sophisticated statistical methods or complex research designs, but rather of painstaking manual data collection. A good example is a recent paper by Morgan Polikoff, Andrew McEachin, Stephani Wrabel and Matthew Duque, which was published in the latest issue of the journal Educational Researcher.

    Polikoff and his colleagues performed a task that makes most of the rest of us cringe: They read and coded every one of the over 40 state applications for ESEA flexibility, or “waivers." The end product is a simple but highly useful presentation of the measures states are using to identify “priority” (low-performing) and “focus” (schools "contributing to achievement gaps") schools. The results are disturbing to anyone who believes that strong measurement should guide educational decisions.

    There's plenty of great data and discussion in the paper, but consider just one central finding: How states are identifying priority (i.e., lowest-performing) schools at the elementary level (the measures are of course a bit different for secondary schools).

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  • Are There Low Performing Schools With High Performing Students?

    Written on October 3, 2013

    I write often (probably too often) about the difference between measures of school performance and student performance, usually in the context of school rating systems. The basic idea is that schools cannot control the students they serve, and so absolute performance measures, such as proficiency rates, are telling you more about the students a school or district serves than how effective it is in improving outcomes (which is better-captured by growth-oriented indicators).

    Recently, I was asked a simple question: Can a school with very high absolute performance levels ever actually be considered a “bad school?"

    This is a good question.

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  • Underlying Issues In The DC Test Score Controversy

    Written on October 1, 2013

    In the Washington Post, Emma Brown reports on a behind the scenes decision about how to score last year’s new, more difficult tests in the District of Columbia Public Schools (DCPS) and the District's charter schools.

    To make a long story short, the choice faced by the Office of the State Superintendent of Education, or OSSE, which oversees testing in the District, was about how to convert test scores into proficiency rates. The first option, put simply, was to convert them such that the proficiency bar was more “aligned” with the Common Core, thus resulting in lower aggregate proficiency rates in math, compared with last year’s (in other states, such as Kentucky and New York, rates declined markedly). The second option was to score the tests while "holding constant" the difficulty of the questions, in order to facilitate comparisons of aggregate rates with those from previous years.

    OSSE chose the latter option (according to some, in a manner that was insufficiently transparent). The end result was a modest increase in proficiency rates (which DC officials absurdly called “historic”).

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  • The Great Proficiency Debate

    Written on August 26, 2013

    A couple of weeks ago, Mike Petrilli of the Fordham Institute made the case that absolute proficiency rates should not be used as measures of school effectiveness, as they are heavily dependent on where students “start out” upon entry to the school. A few days later, Fordham president Checker Finn offered a defense of proficiency rates, noting that how much students know is substantively important, and associated with meaningful outcomes later in life.

    They’re both correct. This is not a debate about whether proficiency rates are at all useful (by the way, I don't read Petrilli as saying that). It’s about how they should be used and how they should not.

    Let’s keep this simple. Here is a quick, highly simplified list of how I would recommend interpreting and using absolute proficiency rates, and how I would avoid using them.

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  • Proficiency Rates And Achievement Gaps

    Written on August 20, 2013

    The change in New York State tests, as well as their results, has inevitably resulted in a lot of discussion of how achievement gaps have changed over the past decade or so (and what they look like using the new tests). In many cases, the gaps, and trends in the gaps, are being presented in terms of proficiency rates.

    I’d like to make one quick point, which is applicable both in New York and beyond: In general, it is not a good idea to present average student performance trends in terms of proficiency rates, rather than average scores, but it is an even worse idea to use proficiency rates to measure changes in achievement gaps.

    Put simply, proficiency rates have a legitimate role to play in summarizing testing data, but the rates are very sensitive to the selection of cut score, and they provide a very limited, often distorted portrayal of student performance, particularly when viewed over time. There are many ways to illustrate this distortion, but among the more vivid is the fact, which we’ve shown in previous posts, that average scores and proficiency rates often move in different directions. In other words, at the school-level, it is frequently the case that the performance of the typical student -- i.e., the average score -- increases while the proficiency rate decreases, or vice-versa.

    Unfortunately, the situation is even worse when looking achievement gaps. To illustrate this in a simple manner, let’s take a very quick look at NAEP data (4th grade math), broken down by state, between 2009 and 2011.

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