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Education Research

  • New Evidence On Teaching Quality And The Achievement Gap

    Written on November 17, 2016

    It is an extensively documented fact that low-income students score more poorly on standardized tests than do their higher income peers. This so-called “achievement gap” has persisted for generations and is still one of the most significant challenges confronting the American educational system.

    Some people tend to overstate -- while others tend to understate -- the degree to which this gap is attributable to differences in teacher (and school) effectiveness between lower and higher income students (with income usually defined in terms of students’ eligibility for subsidized lunch assistance). As discussed below, the evidence thus far suggests that lower income students are a more likely than higher income students to have less “effective” teachers -- with effectiveness defined in terms of the ability to help raise student test scores, or value-added, although the magnitude of these discrepancies varies by study. There are also some compelling theories as to the possible mechanisms behind these (often modest) discrepancies, most notably the fact that schools in low-income neighborhoods tend to have fewer resources, as well as more trouble recruiting and retaining highly qualified, experienced teachers.

    The Mathematica Policy Research organization recently released a very large, very important study that addresses these issues directly. It focuses on shedding additional light on the magnitude of any measurable differences in access to effective teaching among students of different incomes (the “Effective Teaching Gap”), as well as the way in which hiring, mobility, and retention might contribute to these gaps. The analysis uses data on teachers in grades 4-8 or 6-8 (depending on data availability) over five years (2008-09 to 2012-13) in 26 districts across the nation.

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  • The Details Matter In Teacher Evaluations

    Written on September 22, 2016

    Throughout the process of reforming teacher evaluation systems over the past 5-10 years, perhaps the most contentious, discussed issue was the importance, or weights, assigned to different components. Specifically, there was a great deal of debate about the proper weight to assign to test-based teacher productivity measures, such estimates from value-added and other growth models.

    Some commentators, particularly those more enthusiastic about test-based accountability, argued that the new teacher evaluations somehow were not meaningful unless value-added or growth model estimates constituted a substantial proportion of teachers’ final evaluation ratings. Skeptics of test-based accountability, on the other hand, tended toward a rather different viewpoint – that test-based teacher performance measures should play little or no role in the new evaluation systems. Moreover, virtually all of the discussion of these systems’ results, once they were finally implemented, focused on the distribution of final ratings, particularly the proportions of teachers rated “ineffective.”

    A recent working paper by Matthew Steinberg and Matthew Kraft directly addresses and informs this debate. Their very straightforward analysis shows just how consequential these weighting decisions, as well as choices of where to set the cutpoints for final rating categories (e.g., how many points does a teacher need to be given an “effective” versus “ineffective” rating), are for the distribution of final ratings.

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  • An Alternative Income Measure Using Administrative Education Data

    Written on September 16, 2016

    The relationship between family background and educational outcomes is well documented and the topic, rightfully, of endless debate and discussion. A students’ background is most often measured in terms of family income (even though it is actually the factors associated with income, such as health, early childhood education, etc., that are the direct causal agents).

    Most education analyses rely on a single income/poverty indicator – i.e., whether or not students are eligible for federally-subsidized lunch (free/reduced-price lunch, or FRL). For instance, income-based achievement gaps are calculated by comparing test scores between students who are eligible for FRL and those who are not, while multivariate models almost always use FRL eligibility as a control variable. Similarly, schools and districts with relatively high FRL eligibility rates are characterized as “high poverty.” The primary advantages of FRL status are that it is simple and collected by virtually every school district in the nation (collecting actual income would not be feasible). Yet it is also a notoriously crude and noisy indicator. In addition to the fact that FRL eligibility is often called “poverty” even though the cutoff is by design 85 percent higher than the federal poverty line, FRL rates, like proficiency rates, mask a great deal of heterogeneity. Families of two students who are FRL eligible can have quite different incomes, as could two families of students who are not eligible. As a result, FRL-based estimates such as achievement gaps might differ quite a bit from those calculated using actual family income from surveys.

    A new working paper by Michigan researchers Katherine Michelmore and Susan Dynarski presents a very clever means of obtaining a more accurate income/poverty proxy using the same administrative data that states and districts have been collecting for years.

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  • On Focus Groups, Elections, and Predictions

    Written on August 11, 2016

    Focus groups, a method in which small groups of subjects are questioned by researchers, are widely used in politics, marketing, and other areas. In education policy, focus groups, particularly those comprised of teachers or administrators, are often used to design or shape policy. And, of course, during national election cycles, they are particularly widespread, and there are even television networks that broadcast focus groups as a way to gauge the public’s reaction to debates or other events.

    There are good reasons for using focus groups. Analyzing surveys can provide information regarding declaratory behaviors and issues’ rankings at a given point in time, and correlations between these declarations and certain demographic and social variables of interest. Focus groups, on the other hand, can help map out the issues important to voters (which can inform survey question design), as well investigate what reactions certain presentations (verbal or symbolic) evoke (which can, for example, help frame messages in political or informational campaigns).

    Both polling/surveys and focus groups provide insights that the other method alone could not. Neither of them, however, can answer questions about why certain patterns occur or how likely they are to occur in the future. That said, having heard some of the commentary about focus groups, and particularly having seen them being broadcast live and discussed on cable news stations, I feel strongly compelled to comment, as I do whenever data are used improperly or methodologies are misinterpreted.

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  • A Myth Grows In The Garden State

    Written on July 15, 2016

    New Jersey Governor Chris Christie’s recently announced a new "fairness funding" plan to provide every school district in his state roughly the same amount of per-pupil state funding. This would represent a huge change from the current system, in which more state funds are allocated to the districts that serve a larger proportion of economically disadvantaged students. Thus, the Christie proposal would result in an increase in state funding for middle class and affluent districts, and a substantial decrease in money for poorer districts. According to the Governor, the change would reduce the property tax burden on many districts by replacing some of their revenue with state money.

    This is a very bad idea. For one thing, NJ state funding of education is already about 7-8 percent lower than it was in 2008 (Leachman et al. 2015). And this plan would, most likely, cut revenue in the state’s poorest districts by dramatic amounts, absent an implausible increase in property tax rates. It is perfectly reasonable to have a discussion about how education money is spent and allocated, and/or about tax structure. But it is difficult to grasp how serious people could actually conceive of this particular idea. And it’s actually a perfect example of how dangerous it is when huge complicated bodies of empirical evidence are boiled down to talking points (and this happens on all “sides” of the education debate).

    Pu simply, Governor Christie believes that “money doesn’t matter” in education. He and his advisors have been told that how much you spend on schools has little real impact on results. This is also a talking point that, in many respects, coincides with an ideological framework of skepticism toward government and government spending, which Christie shares.

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  • New Research Report: Are U.S. Schools Inefficient?

    Written on June 7, 2016

    At one point or another we’ve all heard some version of the following talking points: 1) “Spending on U.S. education has doubled or triped over the past few decades, but performance has remained basically flat; or 2) “The U.S. spends more on education than virtually any other nation and yet still gets worse results.” If you pay attention, you will hear one or both of these statements frequently, coming from everyone from corporate CEOs to presidential candidates.

    The purpose of both of these statements is to argue that U.S. education is inefficient - that is, gets very little bang for the buck – and that spending more money will not help.

    Now, granted, these sorts of pseudo-empirical talking points almost always omit important nuances yet, in some cases, they can still provide important information. But, putting aside the actual relative efficiency of U.S. schools, these particular statements about U.S. education spending and performance are so rife with oversimplification that they fail to provide much if any useful insight into U.S. educational efficiency or policy that affects it. Our new report, written by Rutgers University Professor Bruce D. Baker and Rutgers Ph.D. student Mark Weber, explains why and how this is the case. Baker and Weber’s approach is first to discuss why the typical presentations of spending and outcome data, particularly those comparing nations, are wholly unsuitable for the purpose of evaluating U.S. educational efficiency vis-à-vis that of other nations. They then go on to present a more refined analysis of the data by adjusting for student characteristics, inputs such as class size, and other factors. Their conclusions will most likely be unsatisfying for all “sides” of the education debate.

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  • Are U.S. Schools Resegregating?

    Written on May 23, 2016

    Last week, the U.S. Government Accountability Office (GAO) issued a report, part of which presented an analysis of access to educational opportunities among the nation’s increasingly low income and minority public school student population. The results, most generally, suggest that the proportion of the nation's schools with high percentages of lower income (i.e., subsidized lunch eligible) and Black and Hispanic students increased between 2000 and 2013.

    The GAO also reports that these schools, compared to those serving fewer lower income and minority students, tend to offer fewer math, science, and college prep courses, and also to suspend, expel, and hold back ninth graders at higher rates.

    These are, of course, important and useful findings. Yet the vast majority of the news coverage of the report focused on the interpretation of these results as showing that U.S. schools are “resegregating.” That is, the news stories portrayed the finding that a larger proportion of schools serve more than 75 percent Black and Hispanic students as evidence that schools became increasingly segregated between the 2000-01 and 2013-14 school years. This is an incomplete, somewhat misleading interpretation of the GAO findings. In order to understand why, it is helpful to discuss briefly how segregation is measured.

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  • New Research Brief: Teacher Segregation In Los Angeles And New York City

    Written on May 5, 2016

    The current attention being given to the state of teacher diversity, including ASI’s recent report on the subject, is based on the idea that teacher diversity is a resource that profits everyone, and that policymakers and administrators should try to increase this resource. We agree.

    There is already a fair amount of research to indicate the significance and potential implications of teacher diversity (e.g., Dee 2004; Gershenson et al., 2015; Mueller et al. 1999). It’s important to bear in mind, however, that the benefits of diversity, like those of any resource, are dependent not just on how much is available, but also how it is distributed across schools and districts.

    Unfortunately, research on the distribution of teacher diversity or teacher segregation has, thus far, been virtually non-existent. A new ASI research brief begins to help fill this void. The brief, written with my colleagues Matt Di Carlo and Esther Quintero, presents a descriptive analysis of teacher segregation within the two largest school districts in the nation – Los Angeles and New York City. We find that teachers in these two districts, while quite diverse overall, relative to the U.S. teacher workforce as a whole, are rather segregated across schools by race and ethnicity, according to multiple different measures of segregation. In other words, teachers tend to work in schools with disproportionate numbers of colleagues of their own race and/or ethnicity.

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  • Charter Schools And Longer Term Student Outcomes

    Written on April 28, 2016

    An important article in the Journal of Policy Analysis and Management presents results from one of the published analyses to look at the long term impact of attending charter schools.

    The authors, Kevin Booker, Tim Sass, Brian Gill, and Ron Zimmer, replicate part of their earlier analysis of charter schools in Florida and Chicago (Booker et al. 2011), which found that students attending charter high schools had a substantially higher chance of graduation and college enrollment (relative to students that attended charter middle schools but regular public high schools). For this more recent paper, they extend the previous analysis, including the addition of two very important, longer term outcomes – college persistence and labor market earnings.

    The limitations of test scores, the current coin of the realm, are well known; similarly, outcomes such as graduation may fail to capture meaningful skills. This paper is among the first to extend the charter school effects literature, which has long relied almost exclusively on test scores, into the longer term postsecondary and even adulthood realms, representing a huge step forward for this body of evidence. It is a development that is likely to become more and more common, as longitudinal data hopefully become available from other locations. And this particular paper, in addition to its obvious importance for the charter school literature, also carries some implications regarding the use of test-based outcomes in education policy evaluation.

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  • Evaluating The Results Of New Teacher Evaluation Systems

    Written on March 24, 2016

    A new working paper by researchers Matthew Kraft and Allison Gilmour presents a useful summary of teacher evaluation results in 19 states, all of which designed and implemented new evaluation systems at some point over the past five years. As with previous evaluation results, the headline result of this paper is that only a small proportion of teachers (2-5 percent) were given the low, “below proficiency” ratings under the new systems, and the vast majority of teachers continue to be rated as satisfactory or better.

    Kraft and Gilmour present their results in the context of the “Widget Effect,” a well-known 2009 report by the New Teacher Project showing that the overwhelming majority of teachers in the 12 districts for which they had data received “satisfactory” ratings. The more recent results from Kraft and Gilmour indicate that this hasn’t changed much due to the adoption of new evaluation systems, or, at least, not enough to satisfy some policymakers and commentators who read the paper.

    The paper also presents a set of findings from surveys of and interviews with observers (e.g., principals). These are in many respects more interesting and important results from a research and policy perspective, but let’s nevertheless focus a bit on the findings on the distribution of teachers across rating categories, as they caused a bit of a stir. I have several comments to make about them, but will concentrate on three in particular (all of which, by the way, pertain not to the paper’s discussion, which is cautious and thorough, but rather to some of the reaction to it in our education policy discourse).

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