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Mitigating Illusory Results through Preregistration in Education

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

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Partially Identified Treatment Effects for Generalizability

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.

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The Methodological Challenges of Measuring Institutional Value-added in Higher Education

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?

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Immediate and Long-Term Efficacy of a Kindergarten Mathematics Intervention

Ben Clarke, Christian Doabler, Keith Smolkowski, Evangeline Kurtz Nelson, Hank Fien, Scott K. Baker, Derek Kosty

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Early intervention can reduce the achievement gap in mathematics

More than half of elementary school students in the United States score below proficient in mathematics in fourth grade. To address this problem, educators can provide early intervention on whole number skills (e.g., counting by ones; adding two numbers to make 10; decomposing numbers). Early intervention may be integral to children’s long-term success with mathematical thinking because difficulty at school entry typically persists into later elementary grades. Persistent frustration and hardship in learning mathematics are associated with a mathematics learning disability (MLD). Students with MLD are most vulnerable to lifelong difficulty managing daily tasks that involve numbers (e.g., money management). Students with or at risk for MLD will likely benefit from intervention as early as possible to reduce adverse long-term impacts.

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Teacher Performance Ratings and Professional Improvement

Cory Koedel, Jiaxi Li, Matthew G. Springer, & Li Tan

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Do Rating Differences in Reformed Teacher Evaluation Systems Cause Teachers to Alter Their Professional Improvement Behaviors?

According to our analysis of Tennessee’s reformed teacher evaluation model, the answer is no.

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Between-School Variation in Students’ Achievement, Motivation, Affect, and Learning Strategies: Results from 81 Countries for Planning Cluster-Randomized Trials in 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.

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Exploring the Impact of Student Teaching Apprenticeships on Student Achievement and Mentor Teachers

Dan Goldhaber, John Krieg, & Roddy Theobald

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Every year there are more than 125,000 student teachers who complete apprenticeships in K-12 public schools. These apprenticeships occur in the classrooms of inservice teachers, known as mentor or cooperating teachers. Does hosting teacher candidates affect student test performance, either during the apprenticeship or in the classrooms of mentor teachers after they host a student teacher?  There is a good deal of speculation about this, but no published quantitative exploration of the impacts on students in the classrooms where student teaching has taken place.

 

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Improving the general language skills of second-language learners in kindergarten: a randomized controlled trial

Kristin Rogde, Monica Melby-Lervåg, & Arne Lervåg

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There are increasing numbers of children whose first language differs from the predominant language of instruction in their school. Entering school where the language of instruction is a student’s second language is associated with undesirable social, educational, and economic outcomes. This study investigates the efficacy of an intervention aimed at improving second-language skills of kindergarteners.

How did we test the intervention?

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Latent Profiles of Reading and Language and Their Association with Standardized Reading Outcomes in K-10th Grade

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.

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The Higher Education Enrollment Decision: Feedback on Expected Study Success and Updating Behavior

Chris van Klaveren, Karen Kooiman, Ilja Cornelisz & Martijn Meeter

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Secondary school students tend to be overly optimistic about how well they will perform in college. This overconfidence leads to suboptimal decision making. But what if secondary school students were told their likelihood of succeeding in the college program they applied to prior to their decision to enroll?  Would this influence their decision to enroll?

This study presents the results of a field experiment in which a random half of 313 secondary-school students applying to higher education received personalized predictions on study success (the other half did not receive such predictions). A comparison of the enrolment rates of the two groups of students helps us understand the effect of receiving these personalized predictions. We find that:

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Effect Sizes Larger in Developer-Commissioned Studies than in Independent Studies

Rebecca Wolf, Jennifer Morrison, Amanda Inns, Robert Slavin, and Kelsey Risman

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Rigorous evidence of program effectiveness has become increasingly important with the 2015 passage of the Every Student Succeeds Act (ESSA). One question that has not yet been addressed is whether findings from program evaluations carried out or commissioned by developers are as trustworthy as those identified in studies by independent third parties. Using study data from the What Works Clearinghouse, we found evidence of a “developer effect,” where program evaluations carried out or commissioned by developers produced average effect sizes that were substantially larger than those identified in evaluations conducted by independent parties.

Why is it important to accurately determine the effect sizes of an educational program?

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