Timothy Lycurgus, Ben B. Hansen, and Mark White

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Many efficacy trials are conducted only after careful vetting in national funding competitions. As part of these competitions, applications must justify the intervention’s theory of change: how and why do the desired improvements in outcomes occur? In scenarios with repeated measurements on participants, some of the measurements may be more likely to manifest a treatment effect than others; the theory of change may provide guidance as to which of those observations are most likely to be affected by the treatment.

Figure 1: Power for the various methods across increasing effect sizes when the theory of change is correct.  

Daniel Litwok, Austin Nichols, Azim Shivji, and Robert Olsen

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Experimental studies of educational interventions are rarely designed to produce impact evidence, justified by statistical inference, that generalizes to populations of interest to education policymakers.  This simulation study explores whether formal sampling strategies for selecting districts and schools improve the generalizability of impact evidence from experimental studies.

Which selection strategies produced samples with the greatest generalizability to the target population?

Peter A. Edelsbrunner, Maja Flaig, Michael Schneider

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How can we best tell how many different learning patterns there are in our data?

Latent transition analysis is used to describe different learner patterns. However, it is often hard to tell how many patterns there are. Is there a pattern of learners who have little knowledge, another pattern of learners with a specific misconception, and another pattern of learners who have properly understood everything that we tried to teach them? Or are there some of these patterns but not all, or even additional ones? This is really hard to tell, and different indicators (called “relative fit indices”) are available for helping us determinate how many patterns there really are. We compare the performance of several relative fit indices. We find that the Bayesian information criterion (BIC), which is commonly used to determine the number of learning patterns, is not very accurate in finding the right number of patterns in comparison to other indices.

Summary by: Wen Wen

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What are “state-” and “trait-” math achievements in early education?

Interventions can boost early math skills, but the role of these early skills on later math achievement is unclear. Consider that students who demonstrate stronger early math skills tend to demonstrate stronger later math achievement, yet some interventions that improve early math skills do not improve later math achievement – that is, the early benefits fade substantially after 2 or 3 years.

Daniela Alvarez-Vargas, Sirui Wan, Lynn S. Fuchs, Alice Klein, & Drew H. Bailey

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Despite policy relevance, long term evaluations of educational interventions are rare relative to the amount of end of treatment evaluations. A common approach to this problem is to use statistical models to forecast the long-term effects of an intervention based on the estimated shorter term effects. Such forecasts typically rely on the correlation between children’s early skills (e.g., preschool numeracy) and medium-term outcomes (e.g., 1^st grade math achievement), calculated from longitudinal data available outside the evaluation. This approach sometimes over- or under-predicts the longer-term effects of early academic interventions, raising concerns about how best to forecast the long-term effects of such interventions. The present paper provides a methodological approach to assessing the types of research design and analysis specifications that may reduce biases in such forecasts.

What did we do?

Sam Sims, Jake Anders, Matthew Inglis, Hugues Lortie-Forgues

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Randomized controlled trials (RCTs) have proliferated in education, in part because they provide an unbiased estimator for the causal impact of interventions. Yet RCTs are only unbiased in expectation (on average across many RCTs).

Estimates of the effect size from specific RCTs will in general diverge from the true effect due to chance differences between the treatment and control group. In suitably powered trials, this imbalance tends to be small and statistical inference helps to control erroneous findings.

Mark White, Bridget Maher, Brian Rowan

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Many educational interventions seek to directly shift instructional practice. Observation systems are used to measure changes in instructional practice resulting from such interventions. However, the complexity of observation systems creates the risk of instrumentation biases. Instrumentation bias is bias resulting from changes to the ways that an instrument functions across conditions (e.g., from pre-test to post-test or between control and intervention conditions). For example, teachers could intentionally show off intervention-specific practices whenever they are observed, but not otherwise use those practices. Alternatively, an instructional intervention could shift instruction in ways that increase observation scores without impacting the underlying instructional dynamics that support student learning.

This conceptual paper with a case study exemplar provides a validity framework for using observation systems to evaluate the impact of interventions. Inferences about an intervention’s impact generally involve determining whether a teaching practice has changed within some setting. Observation scores, the evidence for these conclusions, are specific raters’ views of how a rubric would describe observed lessons. The conclusions are far more generalized than the observation scores. The framework (see Figure below) systematically breaks down the processes necessary to operationalize an aspect of teaching practice and sample from a setting to obtain observation scores that can be generalized to draw conclusions.

Adrian Simpson

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Effect size and policy

Standardized effect size estimates are commonly used by the ‘evidence-based education’ community as a key metric for judging relative importance, effectiveness, or practical significance of interventions across a set of studies: larger effect sizes indicate more effective interventions. However, this argument applies rarely; only when linearly equatable outcomes, identical comparison treatments and equally representative samples are used in every study.

Kaitlyn G. Fitzgerald & Elizabeth Tipton

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What type of data do clearinghouses communicate?

As the body of scientific evidence about what works in education grows, so does the need to effectively communicate that evidence to policy-makers and practitioners. Clearinghouses, such as the What Works Clearinghouse (WWC), have emerged to facilitate the evidence-based decision-making process and have taken on the non-trivial task of distilling often complex research findings to non-researchers. Among other things, this involves reporting effect sizes, statistical uncertainty, and meta-analytic summaries. This information is often reported visually. However, existing visualizations often do not follow data visualization best practices or take the statistical cognition of the audience into consideration.

James Soland & Yeow Meng Thum

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Introduction

Interim achievement tests are often used to monitor student and school performance over time. Unlike end-of-year achievement tests used for accountability, interim tests are administered multiple times per year (e.g., Fall, Winter, and Spring) and vary across schools in terms of when in the school year students take them. As a result, scores reflect seasonal patterns in achievement, including summer learning loss. Despite the prevalence of interim tests, few statistical models are designed to answer questions commonly asked with interim test data (e.g., Do students whose achievement grows the most over several years, tend to experience below-average summer loss?). In this study we compare the properties of three growth models that can be used to examine interim test data.

Summary by: Lily An

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Traditional methods of estimating the returns to community college remain imprecise.

Historically, to estimate the labor market returns to a community college degree, researchers have compared the earnings of students who completed a degree to those who did not, at a single point in time, while controlling for background characteristics. With the expansion of longitudinal data sets, researchers have begun to consider how earnings before and during community college can affect returns to community college. However, even improved econometric analyses overlook some temporal influences on predicted earnings growth, such as the time between graduation and measured earnings, instead estimating averaged returns over time. These influences are particularly salient for community college students, who vary in their time-to-degree completion and often enter college with pre-existing or concurrent work experiences.

Summary by: Lindsay Gomes

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The definition of school readiness in the contexts of educational research, practice, and policy has changed considerably over the past 60 years. After a long period of prioritizing academic skills (e.g., letter-shape knowledge), many researchers now emphasize the extent to which young children can control their emotions and behaviors as key to school readiness. This capacity is commonly referred to as self-regulation, which is often defined in terms of volitional, cognitively-mediated processes such as executive functions. In this paper, we assert that understanding children’s parasympathetic function is essential to providing a holistic understanding of self-regulation in the classroom and for informing how the classroom environment can be tailored to most effectively promote young children’s development.

What is parasympathetic function and why is it important?

Kylie L. Anglin

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Many education policy decisions are made at the local level. School districts make policies regarding hiring, resource allocation, and day-to-day operations. However, collecting data on local policy decisions has traditionally been expensive and time-consuming, sometimes leading researchers to leave important research questions unanswered.

This paper presents a framework for efficiently identifying and processing local policy documents posted online – documents like staff manuals, union contracts, and school improvement plans – using web-scraping and natural language processing.

Summary by: Claire Chuter

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Good researchers thoroughly analyze their data, right? Practices like testing the right covariates, running your analyses in multiple ways to find the best fitting model, screening for outliers, and testing for mediation or moderation effects are indeed important practices… but with a massive caveat. The aggregation of many of these rigorous research practices (as well as some more dubious ones) can lead to what the authors call “illusory results” – results that seem real but are unlikely to be reproduced. In other words, implementation of these common practices (see Figure 1 in the article), often leads researchers to run multiple analytic tests which may unwittingly inflate their chances of stumbling upon a significant finding by chance.

Potential Solutions

Wendy Chan

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Will this intervention work for me?

This is one of the questions that make up the core of generalization research. Generalizations focus on the extent to which the findings of a study apply to people in a different context, in a different time period, or in a different study altogether. In education, one common type of generalization involves examining whether the results of an experiment (e.g., the estimated effect of an intervention) apply to a larger group of people, or a population.

Tatiana Melguizo, Gema Zamarro, Tatiana Velasco, and Fabio J. Sanchez

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Assessing the quality of higher education is hard but there is growing pressure for governments to create a ranking system for institutions that can be used for assessment and funding allocations.  Such a system, however, would require a reliable methodology to fairly assess colleges using a wide variety of indicators. Countries with centralized governance structures have motivated researchers to develop “value-added” metrics of colleges’ contributions to student outcomes that can be used for summative assessment (Coates, 2009; Melguizo & Wainer, 2016; Shavelson et al. 2016). Estimating the “value-added” of colleges and programs, however, is methodologically challenging: first, high- and low-achieving students tend to self-select into different colleges– a behavior that if not accounted for, may yield to estimates that capture students’ prior achievement rather than colleges’ effectiveness at raising achievement; second, measures considering gains in student learning outcomes (SLOs) as indicators at the higher education level are scant. In our paper, we study these challenges and compare the methods used for obtaining value-added metrics in the context of higher education in Colombia.

How to best estimate value-added models in higher education?

Martin Brunner, Uli Keller, Marina Wenger, Antoine Fischbach & Oliver Lüdtke

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Does an educational intervention work?

When planning an evaluation, researchers should ensure that it has enough statistical power to detect the expected intervention effect. The minimally detectable effect size, or MDES, is the smallest true effect size a study is well positioned to detect. If the MDES is too large, researchers may erroneously conclude that their intervention does not work even when it does. If the MDES is too small, that is not a problem per se, but it may mean increased cost to conduct the study.  The sample size, along with several other factors, known as design parameters, go into calculating the MDES. Researchers must estimate these design parameters. This paper provides an empirical bases for estimating design parameters in 81 countries across various outcomes.

Barbara R Foorman, Yaacov Petscher, Christopher Stanley, & Adrea Truckenmiller

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Differentiated instruction involves tailoring instruction to individual student’s learning needs. While critical to effective teaching, an understudied first step in differentiated instruction is understanding students’ learning profiles – that is, their strengths and weaknesses in knowledge and skills.  It is only after a student’s learning profile is understood that a teacher can individualize instruction. But how can educators best measure learning profiles to facilitate differentiated instruction?

Descriptive approaches such as informal reading inventories lack the psychometric rigor required for purposes of classification, placement, and monitoring growth.  However, quantitative approaches to classifying and clustering (i.e., grouping) students by skill classes and validating the clusters by relating them to standardized tests is a reliable tool for creating profiles. The objective of this study was twofold. First, to determine the profiles of reading and language skills that characterized 7,752 students in kindergarten through 10th grade. Second, to relate the profiles to standardized reading outcomes.

Luke Miratrix, Jane Furey, Avi Feller, Todd Grindal, and Lindsay Page

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Estimating program effects for subgroups is hard. Estimating effects for types of people who exist in theory, but whom we can’t always identify in practice (i.e., latent subgroups) is harder. These challenges arise often, with noncompliance being a primary example. Another is estimating effects on groups defined by “counterfactual experience,” i.e., by what opportunities would have been available absent treatment access. This paper tackles this difficult problem. We find that if one can predict, with some accuracy, latent subgroup membership, then bounding is a nice evaluation approach, relying on weak assumptions. This is in contrast to many alternatives that are tricky, often unstable, and/or rely on heroic assumptions.

What are latent subgroups again?

Howard Bloom, Steve Raudenbush, Michael Weiss, & Kristin Porter

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Multisite randomized trials are experiments where individuals are randomly assigned to alternative experimental arms within each of a collection of sites (e.g., schools).  They are used to estimate impacts of educational interventions. However, little attention has been paid to using them to quantify and report cross-site impact variation. The present paper, which received the 2017 JREE Outstanding Article Award, provides a methodology that can help to fill this gap.

Why and how is knowledge about cross-site impact variation important?