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Methodology, measurement and data
The estimation of test score “gaps” and gap trends plays an important role in monitoring educational inequality. Researchers decompose gaps and gap changes into within- and between-school portions to generate evidence on the role schools play in shaping these inequalities. However, existing decomposition methods assume an equal-interval test scale and are a poor fit to coarsened data such as proficiency categories. This leaves many potential data sources ill-suited for decomposition applications. We develop two decomposition approaches that overcome these limitations: an extension of V, an ordinal gap statistic, and an extension of ordered probit models. Simulations show V decompositions have negligible bias with small within-school samples. Ordered probit decompositions have negligible bias with large within-school samples but more serious bias with small within-school samples. More broadly, our methods enable analysts to (1) decompose the difference between two groups on any ordinal outcome into portions within- and between some third categorical variable, and (2) estimate scale-invariant between-group differences that adjust for a categorical covariate.
For years Georgia's HOPE Scholarship program provided full tuition scholarships to high achieving students. State budgetary shortfalls reduced its generosity in 2011. Under the new rules, only students meeting more rigorous merit-based criteria would retain the original scholarship covering full tuition, now called Zell Miller, with other students seeing aid reductions of approximately 15 percent. We exploit the fact that two of the criteria were high school GPA and SAT/ACT score, which students could not manipulate when the change took place. We compare already-enrolled students just above and below these cutoffs, making use of advances in multi-dimensional regression discontinuity, to estimate effects of partial aid loss. We show that, after the changes, aid flowed disproportionately to wealthier students, and find no evidence that the financial aid reduction affected persistence or graduation for these students. The results suggest that high-achieving students, particularly those already in college, may be less price sensitive than their peers.
Researchers are rarely satisfied to learn only whether an intervention works, they also want to understand why and under what circumstances interventions produce their intended effects. These questions have led to increasing calls for implementation research to be included in high quality studies with strong causal claims. Of critical importance is determining whether an intervention can be delivered with adherence to a standardized protocol, and the extent to which an intervention protocol can be replicated across sessions, sites, and studies. When an intervention protocol is highly standardized and delivered through verbal interactions with participants, a set of natural language processing (NLP) techniques termed semantic similarity can be used to provide quantitative summary measures of how closely intervention sessions adhere to a standardized protocol, as well as how consistently the protocol is replicated across sessions. Given the intense methodological, budgetary and logistical challenges for conducting implementation research, semantic similarity approaches have the benefit of being low-cost, scalable, and context agnostic for use. In this paper, we demonstrate how semantic similarity approaches may be utilized in an experimental evaluation of a coaching protocol on teacher pedagogical skills in a simulated classroom environment. We discuss strengths and limitations of the approach, and the most appropriate contexts for applying this method.
Recent interest to promote and support replication efforts assume that there is well-established methodological guidance for designing and implementing these studies. However, no such consensus exists in the methodology literature. This article addresses these challenges by describing design-based approaches for planning systematic replication studies. Our general approach is derived from the Causal Replication Framework (CRF), which formalizes the assumptions under which replication success can be expected. The assumptions may be understood broadly as replication design requirements and individual study design requirements. Replication failure occurs when one or more CRF assumptions are violated. In design-based approaches to replication, CRF assumptions are systematically tested to evaluate the replicability of effects, as well as to identify sources of effect variation when replication failure is observed. In direct replication designs, replication failure is evidence of bias or incorrect reporting in individual study estimates, while in conceptual replication designs, replication failure occurs because of effect variation due to differences in treatments, outcomes, settings, and participant characteristics. The paper demonstrates how multiple research designs may be combined in systematic replication studies, as well as how diagnostic measures may be used to assess the extent to which CRF assumptions are met in field settings.
Virtual charter schools provide full-time, tuition-free K-12 education through internet-based instruction. Although virtual schools offer a personalized, content-appropriate experience, most research suggests these schools are negatively associated with achievement. Few studies account for differential rates of student mobility, which may produce biased estimates if mobility is jointly associated with virtual school enrollment and subsequent test scores. We account for student mobility in an evaluation of a single, large, anonymous virtual charter school. We estimate treatment effects of the virtual school on student achievement using a hybrid of exact and nearest-neighbor propensity score matching. Relative to their matched peers, we estimate that virtual students produce similar ELA scores and significantly worse math scores after one year. Among a limited sample of students observed for four years, we estimate that virtual students ultimately produce higher ELA scores and similar math scores relative to matched peers. We argue these findings are more reliable indicators of the independent effect of virtual schooling on student achievement because the match on student mobility is a proxy for otherwise unobservable negative selection factors.
The Community Eligibility Provision (CEP) is a policy change to the federally-administered National School Lunch Program that allows schools serving low-income populations to classify all students as eligible for free meals, regardless of individual circumstances. This has implications for the use of free and reduced-price meal (FRM) data to proxy for student disadvantage in education research and policy applications, which is a common practice. We document empirically how the CEP has affected the value of FRM eligibility as a proxy for student disadvantage. At the individual student level, we show that there is essentially no effect of the CEP. However, the CEP does meaningfully change the information conveyed by the share of FRM-eligible students in a school. It is this latter measure that is most relevant for policy uses of FRM data.
Note: Portions of this paper were previously circulated under the title “Using Free Meal and Direct Certification Data to Proxy for Student Disadvantage in the Era of the Community Eligibility Provision.” We have since split the original paper into two parts. This is the first part.
International assessments are important to benchmark the quality of education across countries. However, on low-stakes tests, students’ incentives to invest their maximum effort may be minimal. Research stresses that ignoring students’ effort when interpreting results from low-stakes assessments can lead to biased interpretations of test performance across groups of examinees. We use data from the Programme for International Student Assessment (PISA), a low-stakes test, to analyze the extent to which student effort helps to explain test scores heterogeneity across countries and by gender groups. Our results highlight the importance of accounting for differences in student effort to understand cross-country heterogeneity in performance and variations in gender achievement gaps across nations. We find that, once we account for differential student effort across gender groups, the estimated gender achievement gap in math and science could be up to 12 and 6 times wider, respectively, and up to 49 percent narrower in reading, in favor of boys. In math and science, the gap widens in most countries, even among some of the top 20 most gender-equal countries. Altogether, our effort measures on average explain between 36 and 40 percent of the cross-country variation in test scores.
State testing programs regularly release previously administered test items to the public. We provide an open-source recipe for state, district, and school assessment coordinators to combine these items flexibly to produce scores linked to established state score scales. These would enable estimation of student score distributions and achievement levels. We discuss how educators can use resulting scores to estimate achievement distributions at the classroom and school level. We emphasize that any use of such tests should be tertiary, with no stakes for students, educators, and schools, particularly in the context of a crisis like the COVID-19 pandemic. These tests and their results should also be lower in priority than assessments of physical, mental, and social–emotional health, and lower in priority than classroom and district assessments that may already be in place. We encourage state testing programs to release all the ingredients for this recipe to support low-stakes, aggregate-level assessments. This is particularly urgent during a crisis where scores may be declining and gaps increasing at unknown rates.
The worldwide school closures in early 2020 led to losses in learning that will not easily be made up for even if schools quickly return to their prior performance levels. These losses will have lasting economic impacts both on the affected students and on each nation unless they are effectively remediated.
While the precise learning losses are not yet known, existing research suggests that the students in grades 1-12 affected by the closures might expect some 3 percent lower income over their entire lifetimes. For nations, the lower long-term growth related to such losses might yield an average of 1.5 percent lower annual GDP for the remainder of the century. These economic losses would grow if schools are unable to re-start quickly.
The economic losses will be more deeply felt by disadvantaged students. All indications are that students whose families are less able to support out-of-school learning will face larger learning losses than their more advantaged peers, which in turn will translate into deeper losses of lifetime earnings.
The present value of the economic losses to nations reach huge proportions. Just returning schools to where they were in 2019 will not avoid such losses. Only making them better can. While a variety of approaches might be attempted, existing research indicates that close attention to the modified re-opening of schools offers strategies that could ameliorate the losses. Specifically, with the expected increase in video-based instruction, matching the skills of the teaching force to the new range of tasks and activities could quickly move schools to heightened performance. Additionally, because the prior disruptions are likely to increase the variations in learning levels within individual classrooms, pivoting to more individualised instruction could leave all students better off as schools resume.
As schools move to re-establish their programmes even as the pandemic continues, it is natural to focus considerable attention on the mechanics and logistics of safe re-opening. But the long-term economic impacts also require serious attention, because the losses already suffered demand more than the best of currently considered re-opening approaches.