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Since the early 1990s, the federal role in education-exemplified by the controversial No Child Left Behind Act (NCLB)-has expanded dramatically. Yet states and localities have retained a central role in education policy, leading to a growing struggle for control over the direction of the nation's schools.In An Education in Politics, Jesse H. Rhodes explains the uneven development of federal involvement in education. While supporters of expanded federal involvement enjoyed some success in bringing new ideas to the federal policy agenda, Rhodes argues, they also encountered stiff resistance from proponents of local control. Built atop existing decentralized policies, new federal reforms raised difficult questions about which level of government bore ultimate responsibility for improving schools. Rhodes's argument focuses on the role played by civil rights activists, business leaders, and education experts in promoting the reforms that would be enacted with federal policies such as NCLB. It also underscores the constraints on federal involvement imposed by existing education policies, hostile interest groups, and, above all, the nation's federal system. Indeed, the federal system, which left specific policy formation and implementation to the states and localities, repeatedly frustrated efforts to effect changes: national reforms lost their force as policies passed through iterations at the state, county, and municipal levels. Ironically, state and local resistance only encouraged civil rights activists, business leaders, and their political allies to advocate even more stringent reforms that imposed heavier burdens on state and local governments. Through it all, the nation's education system made only incremental steps toward the goal of providing a quality education for every child.

In recent years, a widespread consensus has emerged about the necessity of establishing bridges between quantitative and qualitative approaches to empirical research in political science. In this article, we discuss the use of the synthetic control method as a way to bridge the quantitative/qualitative divide in comparative politics. The synthetic control method provides a systematic way to choose comparison units in comparative case studies. This systematization opens the door to precise quantitative inference in small-sample comparative studies, without precluding the application of qualitative approaches. Borrowing the expression from Sidney Tarrow, the synthetic control method allows researchers to put \"qualitative flesh on quantitative bones.\" We illustrate the main ideas behind the synthetic control method by estimating the economic impact of the 1990 German reunification on West Germany.

A wealth of recent research in comparative politics examines how spatial variation in historical conditions shapes modern political outcomes. In an article in the American Political Science Review, Homola, Pereira, and Tavits argue that Germans who live nearer to former Nazi concentration camps are more likely to display out-group intolerance. Clarifying the conceptual foundations of posttreatment bias and reviewing the historical record on postwar state creation in Germany, we argue that state-level differences confound the relationship between distance to camps and out-group intolerance. Using publicly available European Values Survey data and electoral results from 2017, we find no consistent evidence that distance to camps is related to contemporary values. Our findings have implications for literatures on historical persistence, causal inference with spatial data, Holocaust studies, and outgroup tolerance.

In this article, Timothy and Cynthia Shanahan argue that \"disciplinary literacy\"--advanced literacy instruction embedded within content-area classes such as math, science, and social studies--should be a focus of middle and secondary school settings. Moving beyond the oft-cited \"every teacher a teacher of reading\" philosophy that has historically frustrated secondary content-area teachers, the Shanahans present data collected during the first two years of a study on disciplinary literacy that reveal how content experts and secondary content teachers read disciplinary texts, make use of comprehension strategies, and subsequently teach those strategies to adolescent readers. Preliminary findings suggest that experts from math, chemistry, and history read their respective texts quite differently; consequently, both the content-area experts and secondary teachers in this study recommend different comprehension strategies for work with adolescents. This study not only has implications for which comprehension strategies might best fit particular disciplinary reading tasks, but also suggests how students may be best prepared for the reading, writing, and thinking required by advanced disciplinary coursework. (Contains 1 table.)

Using data from two freshmen cohorts at a public research university (N = 3730), this study examines the relationship between loan aid and second-year enrollment persistence. Applying a counterfactual analytical framework that relies on propensity score (PS) weighting and matching to address selection bias associated with treatment status, the study estimates that loan aid exerts a significant negative effect on persistence for students from low-income background (i.e., Pell eligible), and those taking up high amounts of loans in order to meet total cost of attendance, including students who exhausted the available amount of subsidized loan aid. However, no significant incremental effect associated with unsubsidized loan aid, net of subsidized loan aid, could be detected. The estimated effect of loan aid on persistence controls for first-year academic experience and takes into account 26 factors related to loan selection and persistence in order to match students with loan aid to a counterfactual case in covariate adjusted regression. Comparison with results from non-matched-sample analysis suggests selection bias may mask the negative effect of loans detected with matched-sample estimation. Validity of covariates determining the loan selection process and criteria for acceptable balance in the matched data are discussed, and implications for future research are addressed.

This article reports a synthesis of intervention studies conducted between 1994 and 2004 with older students (Grades 6-12) with reading difficulties. Interventions addressing decoding, fluency, vocabulary, and comprehension were included if they measured the effects on reading comprehension. Twenty-nine studies were located and synthesized. Thirteen studies met criteria for a meta-analysis, yielding an effect size (ES) of 0.89 for the weighted average of the difference in comprehension outcomes between treatment and comparison students. Word-level interventions were associated with ES = 0.34 in comprehension outcomes between treatment and comparison students. Implications for comprehension instruction for older struggling readers are described.

Policy-makers often commission formal analyses to estimate the costs, risks, and benefits of proposed projects or policies. Applications range from estimating the risks of commercial nuclear power, to setting priorities among environmental risks, to comparing technologies for generating electricity, to weighing the benefits and risks of prescription drugs. In the United States, analyses are required for all major federal regulations. Fischhoff reviews how such analyses are limited by the scientific and ethical judgments inherent in the process and require collaboration between those who generate the analyses and those who want to use them. Science , this issue p. 10.1126/science.aaa6516 Formal analyses can be valuable aids to decision-making if their limits are understood. Those limits arise from the two forms of subjectivity found in all analyses: ethical judgments, made when setting the terms of an analysis, and scientific judgments, made when conducting it. As formal analysis has assumed a larger role in policy decisions, awareness of those judgments has grown, as have methods for making them. The present review traces these developments, using examples that illustrate the issues that arise when designing, executing, and interpreting analyses. It concludes with lessons learned from the science and practice of analysis. One common thread in these lessons is the importance of collaborative processes, whereby analysts and decision-makers educate one another about their respective needs and capabilities.

The purpose of our study was twofold: (1) to review the literature on high-stakes accountability testing and English learners (ELs) in the USA, applying qualitative systematic review methodology, and (2) to draw substantive conclusions about the impact of high-stakes accountability testing on ELs, reported in studies published between 2001 and 2016. Thirty-seven studies ( n = 37) meeting the eligibility criteria were evaluated using the Methodological Quality Questionnaire. Findings indicate construct irrelevant variance (non-random factors that systematically affect ELs’ test performance) challenges the appropriateness of inferences drawn about ELs’ content knowledge based on assessment performance. Further, language policy in schools, driven by high-stakes tests, promotes increased instructional time for basic skills tutorials with fewer opportunities for problem solving and other higher-order learning activities. Moreover, theory utilization was rare ( n = 8, 22%). Among studies that utilized theory, few explicitly referenced it to discuss study findings.

We study the consequences of measurement error in the dependent variable of random-coefficients models, focusing on the particular case of quantile regression. The popular quantile regression estimator of Koenker and Bassett (1978) is biased if there is an additive error term. Approaching this problem as an errors-in-variables problem where the dependent variable suffers from classical measurement error, we present a sieve maximum likelihood approach that is robust to left-hand-side measurement error. After providing sufficient conditions for identification, we demonstrate that when the number of knots in the quantile grid is chosen to grow at an adequate speed, the sieve-maximum-likelihood estimator is consistent and asymptotically normal, permitting inference via bootstrapping. Monte Carlo evidence verifies our method outperforms quantile regression in mean bias and MSE. Finally, we illustrate our estimator with an application to the returns to education highlighting changes over time in the returns to education that have previously been masked by measurement-error bias.