ethanporcaro/eric-fulltext-meta huggingface dataset:
A School Staff Opinion Survey Predicts Student Achievement in Victoria, Australia: Evidence from a Structural Equation Modeling Analysis
Author: Clark, Tedra F.
Date: 2016-00-00
Type: Reports - Research
Subjects: Foreign Countries,School Personnel,Opinions,Surveys,Academic Achievement,Structural Equation Models,National Competency Tests,Elementary Schools,Correlation,Educational Environment,Leadership,Professional Development,School Safety,Well Being,Predictive Validity

Effective school reform requires communication with and buy-in from key stakeholders. In recent years, stakeholder surveys have become increasingly prominent tools for promoting better understanding of how school factors contribute to student success. The key purpose of this study was to investigate the relation between Victorian school scores on the School Staff Opinion Survey and student achievement as measured by The National Assessment Program-Literacy and Numeracy (NAPLAN). Surveys were administered by Mid-Continent Research for Education and Learning (McREL) via an online survey platform to staff members in 64 schools throughout the state of Victoria. To conduct this study with sufficient representation of the Victorian primary schools, McREL researchers purposefully over-sampled primary schools during the trial of the new Staff Opinion Survey. As a result, the data for this study are comprised of a stratified random sample of 62 primary schools in Victoria that are representative of the population of primary schools in Victoria in terms of size and socioeconomics. The findings of the predictive validity study indicate that the School Climate, School Leadership, Professional Learning, and Staff-safety and Well-being scores were highly interrelated and positively correlated with student NAPLAN outcomes. Tables and a figure are appended.
Between-District Test Score Variation, 2009-2012
Author: Fahle, Erin
Date: 2016-00-00
Type: Reports - Research
Subjects: School Districts,Scores,Comparative Analysis,School Effectiveness,Educational Planning,Models,Correlation,Data Analysis,Statistical Distributions,Statistical Studies,School Statistics

Describing the variation in test scores between and within school districts is critical for: (1) for policy-related and descriptive work that investigates the sorting of students among districts and the differential effectiveness of those districts; and (2) for methodological work planning future experiments or interventions. Intraclass Correlations (ICCs) and Coefficients of Variation (CVs) are two complementary ways to describe test score variation. ICCs describe the proportion of variance in test scores that is between (rather than within) school districts or schools. CVs describe the extent of heteroscedasticity in district (or school) test score distributions. The most straightforward method of calculating ICCs and CVs is to use student-level data to directly estimate the means and variances of district or school test score distributions. In this study the authors investigate three interrelated research questions: (1) how much between-district variation exists across U.S. states?; (2) what are they key patterns across grades, subject, and years (within or across states) in the between-district variation?; and (3) what state-level factors are associated with a state having more-or-less observed between-district variation? This study leverages the use of ordered probit models to recover distributional information from coarsened test score data proposed by Reardon et al. This method was applied to a large data set provided by the National Center of Education Statistics (NCES) through a restricted data license. Under the No Child Left Behind (NCLB) legislation, states are required to report aggregated test score results to the U.S. Department of Education, through a system called EdFacts. The authors found substantial variation in ICCs and CVs across states. One table and 2 figures are appended.
Actionable Intelligence about Early Childhood Risks in Philadelphia
Author: LeBoeuf, Whitney A.
Date: 2016-00-00
Type: Reports - Research
Subjects: Early Childhood Education,Risk,Early Experience,Incidence,Body Weight,Prenatal Care,Early Parenthood,Parent Background,Educational Attainment,Homeless People,Child Health,Geographic Distribution,Longitudinal Studies

&quot;Early childhood risks&quot; are markers of early childhood experiences that extensive research has shown to be detrimental to later academic and behavioral outcomes. In Philadelphia, evidence indicates that seven early childhood risks tracked by public agencies have negative effects on early school outcomes. These risks include low birthweight or preterm birth, inadequate prenatal care, teen mother, low maternal education, homelessness, lead exposure, and child maltreatment. The purpose of this research was to understand the early risk experiences of the entire population of children age 5 years in Philadelphia between 2008 and 2012. The specific questions were: (1) What are the prevalence rates of known early childhood risks, including low birthweight or preterm birth, inadequate prenatal care, teen mother, low maternal education, homelessness, lead exposure, and child maltreatment?; (2) What percentage of young children experienced multiple early childhood risks?; and (3) What is the geographic distribution of children experiencing multiple early childhood risks in Philadelphia? The Data Management Office (DMO) in the City of Philadelphia's Office for Health and Opportunity partnered with the Penn Child Research Center at the University of Pennsylvania's Graduate School of Education to create a data model that tracks evidence-based early risk experiences for children 0 to 5 years old. One table and three figures are appended.
College Enrollment Patterns for Rural Indiana High School Graduates
Author: Burke, Mathew R.
Date: 2016-00-00
Type: Reports - Research
Subjects: High School Graduates,College Bound Students,Enrollment Trends,Rural Population,Public Colleges,Enrollment Rate,Predictive Validity,Predictor Variables,Eligibility,Rural Urban Differences,Full Time Students,Lunch Programs,College Preparation,School Location,Proximity,Student Characteristics,Institutional Characteristics,Longitudinal Studies,School Statistics,Data Analysis

Postsecondary education is a fundamental tool for achieving upward mobility and economic growth. Students with an associate's or bachelor's degree earn substantially more in a lifetime and experience better working conditions and job benefits than students with only a high school diploma. This study examines differences in public college enrollment rates as well as the usefulness of previously identified early college success predictors in predicting presumptive college eligibility for 2010 graduates of Indiana public rural and nonrural high schools. The report also presents methodologies that could be useful for examining rural-nonrural college enrollment patterns outside Indiana. The study explored five research questions on 2010 graduates of Indiana public rural and nonrural high schools who enrolled in Indiana public colleges: (1) What proportion of graduates of rural and nonrural high schools enrolled in college, enrolled in different types of colleges (two- or four-year colleges of varying selectivity), and enrolled full-time?; (2) Did graduates of rural and nonrural high schools differ in their academic preparation or eligibility for the school lunch program (a proxy for low-income status)?; (3) Where are two- and four-year colleges located, and how does distance from high schools to colleges vary for graduates of rural and nonrural high schools who enrolled in Indiana public colleges?; (4) What proportion of rural and nonrural high school graduates who enrolled in college had academic characteristics that made them &quot;presumptively eligible&quot; (see box 1) for two- or four-year public colleges of varying selectivity? What proportion who enrolled in a college undermatched with their level of presumptive eligibility?; and (5) After student- and school-level characteristics were controlled among high school graduates who enrolled in a public college, did any rural-nonrural differences remain with respect to enrolling in a two-year program? Study used administrative data from the Indiana state longitudinal data system and the Indian Commission for Higher Education. A similar proportion of graduates of rural and nonrural Indiana public high schools enrolled in college. However, rural graduates were more likely than nonrural graduates to enroll in a two-year college and less likely to enroll in a very selective four-year college.
Summer Bridge Programs. What Works Clearinghouse Intervention Report
Author: What Works Clearinghouse (ED)
Date: 2016-07-00
Type: Reports - Evaluative
Subjects: Program Evaluation,Summer Programs,Transitional Programs,High School Graduates,College Bound Students,School Orientation,Academic Advising,Skill Development,Time Management,Study Skills,Acceleration (Education),Undergraduate Students,Outcomes of Education,Postsecondary Education,Educational Attainment,Program Effectiveness

&quot;Summer bridge programs&quot; are designed to ease the transition to college and support postsecondary success by providing students with the academic skills and social resources needed to succeed in a college environment. These programs occur in the summer &quot;bridge&quot; period between high school and college. Although the content of summer bridge programs can vary across institutions and by the population served, they typically last 2-4 weeks and involve (a) an in-depth orientation to college life and resources, (b) academic advising, (c) training in skills necessary for college success (e.g., time management and study skills), and/or (d) accelerated academic coursework. The What Works Clearinghouse (WWC) identified one study of &quot;summer bridge programs&quot; that both falls within the scope of the Supporting Postsecondary Success topic area and meets WWC group design standards. This study meets WWC group design standards with reservations. The study included 2,222 undergraduate students enrolled at Georgia Tech. Based on the study, &quot;summer bridge programs&quot; were found to have potentially positive effects on postsecondary attainment for postsecondary students. The following are appended: (1) Research details for Murphy et al. (2010); (2) Outcome measure for the degree attainment (college) domain; and (3) Findings included in the rating for the degree attainment (college) domain. [Study that meets WWC group design standards with reservations was: Murphy, T. E., Gaughan, M., Hume, R., &amp; Moore, S. G. Jr. (2010). &quot;College graduation rates for minority students in a selective technical university: Will participation in a summer bridge program contribute to success?&quot; &quot;Educational Evaluation and Policy Analysis,&quot; 32(1), 70-83. doi: 10.3102/0162373709360064. See EJ880616 to view the study.]
Engaging Parents in Parent Engagement Programs
Author: Levitt, Steven
Date: 2016-00-00
Type: Reports - Research
Subjects: Foreign Countries,Parent Participation,Parent Education,Program Effectiveness,Elementary School Students,Parent Student Relationship,Incentives,Attendance,Randomized Controlled Trials,Parent Background

Researchers, policymakers, and educators have long recognized the role of parents in shaping student achievement. A large body of observational studies documents the strong relationship between family background and educational outcomes, but to date there have been very few experimental studies in this area. In this study, the authors offer a parent academy targeted to parents of elementary school children in the UK. In addition to investigating the impact of the program, their research question focuses on the effect of offering an incentive for attendance on parent engagement in the program. The Parent Academy (PA) included one 2-hour session every two weeks that parents attended at their child's school with 13 sessions over the course of the school year. The sessions focused on developing parent skills to support learning out of school. Each session focused on a particular content area, with English topics in the first half of the year and Math topics in the second half of the year. The study used a randomized controlled trial design with three groups: Control, Parent academy without incentives, or Parent Academy with incentives. The main finding is that the attendance incentive has a large, significant, and sustained impact on attendance at the parent academy. More work is needed to understand which parents are most sensitive to incentives and the effect on student performance of increased parental participation in parent education programs. Figures are appended.
Customized Nudging to Improve FAFSA Completion and Income Verification
Author: Page, Lindsay
Date: 2016-00-00
Type: Reports - Research
Subjects: Federal Aid,Student Financial Aid,Access to Education,Barriers,Low Income Groups,Intervention,Access to Information,Communication Strategies,School Districts,High School Seniors,College Applicants,Telecommunications,Handheld Devices,Technology Uses in Education,Information Dissemination,Student Records,Content Analysis,Regression (Statistics)

For most students from low- or moderate-income families, successfully completing the Free Application for Federal Student Aid (FAFSA) is a crucial gateway on the path to college access. However, FAFSA filing and income verification tasks pose substantial barriers to college access for low-income students. In this paper, the authors report on a pair of interventions that utilize automated, text-based outreach to: (1) provide students and families with customized information about the importance of and their status on completing the FAFSA; (2) simplify information for students and families about how to complete the FAFSA; and (3) connect students and families to personalized counseling assistance to complete the FAFSA as well as the subsequent verification process, if selected. Data from the study points to the benefit of text-based outreach as a low-cost and readily scalable strategy for improving student completion of important college-going milestones, such as timely FAFSA filing. Tables and figures are appended.
Continuous Improvement in Action: Educators' Evidence Use for School Improvement
Author: Cannata, Marisa
Date: 2016-00-00
Type: Reports - Research
Subjects: Educational Improvement,Data Interpretation,Evidence Based Practice,Input Output Analysis,Program Implementation,Educational Change,Evidence,Barriers,High Schools,School Effectiveness,Educational Practices,Low Income Students,Minority Group Students,Mixed Methods Research,Information Utilization,Educational Innovation,Accountability

The focus of the article is the process educators use to interpret data to turn it into usable knowledge (Honig &amp; Coburn, 2008) while engaging in a continuous improvement process. The authors examine the types of evidence educators draw upon, its perceived relevance, and the social context in which the evidence is examined. Evidence includes data related to student or school outputs (e.g., passing rates), inputs (e.g., student demographic information), school processes (e.g., data on program implementation) and perceptions (e.g., surveys) (Marsh, 2012). Previous research also indicates that educators draw on evidence substitutes--anecdotal information, experience, intuition, ideological preference, and customary practice (Bryk &amp; Gomez, 2008; Dorman &amp; Binnewies, 2015; Ingram, Louis, &amp; Schroeder, 2004). Considering this diversity of evidence enabled the authors to examine how teacher-led implementation teams come to shared conclusions about what they are learning about their improvement efforts. The following research questions are asked: (1) How are educators learning about and using data for the improvement of a school-wide reform; (2) What types of evidence are they citing to make claims about what they are learning; and (3) What are the barriers educators face as they learn how to apply data to make claims about what they are learning? Two tables are appended.
General Framework for Effect Sizes in Cluster Randomized Experiments
Author: VanHoudnos, Nathan
Date: 2016-00-00
Type: Reports - Research
Subjects: Effect Size,Randomized Controlled Trials,Educational Experiments,Educational Research,Models,Hierarchical Linear Modeling,Hypothesis Testing,Statistical Analysis,Equations (Mathematics),Least Squares Statistics,Definitions,Sampling

Cluster randomized experiments are ubiquitous in modern education research. Although a variety of modeling approaches are used to analyze these data, perhaps the most common methodology is a normal mixed effects model where some effects, such as the treatment effect, are regarded as fixed, and others, such as the effect of group random assignment or the neighborhoods where the students live, are regarded as random. For these models, the standard reference used by education researchers is Raudenbush and Bryk (2002). Although mixed effects models enjoy wide use in estimating parameters from and testing hypotheses about these experiments, the development of standardized mean difference effect size indices for them is relatively recent. This paper unifies the currently published effect sizes in a general mixed effects modeling framework. Researchers then apply this framework to suggest an effect size for a model that currently lacks a published effect size, a random slope model with heterogeneous treatment effects. The general framework proposed by researchers generates interpretable effect sizes for a wide class of mixed effects. Further, they illustrate the value of this framework by deriving a new effect size for a cluster randomized controlled trial with heterogeneous treatment effects. One figure is appended.
Developing a Theory of Treatment Effect Heterogeneity through Better Design: Where Do Behavioral Science Interventions Work Best?
Author: Tipton, Elizabeth
Date: 2016-00-00
Type: Reports - Research
Subjects: Behavioral Sciences,Behavioral Science Research,Intervention,Educational Experiments,Generalization,Evaluation Methods,Randomized Controlled Trials,Probability,Sampling,Public Schools,High School Students,Grade 9,Research Design,Hierarchical Linear Modeling,Generalizability Theory,Surveys

Questions regarding the generalizability of results from educational experiments have been at the forefront of methods development over the past five years. This work has focused on methods for estimating the effect of an intervention in a well-defined inference population (e.g., Tipton, 2013; O'Muircheartaigh and Hedges, 2014); methods for assessing similarity between the students and schools in a study to those in an inference population (e.g., Stuart, Cole, Bradshaw, and Leaf, 2011; Tipton, 2014a); and methods for improved site selection with generalization in mind (e.g., Tipton et al, 2014; Tipton, 2014b). To date, this work has been developed in the context of the large-scale education experiments typically funded by IES, wherein schools are typically randomly assigned to receive a yearlong curricular intervention. Given this context, this work on generalization has focused largely on estimation of the average &quot;treatment effect&quot; in a population, particularly under the assumption that random selection of schools into the study is infeasible (see Olsen, Orr, Bell, and Stuart, 2013). This paper argues that behavioral science interventions are different in two important ways from these standard large-scale education experiments, and that these differences provide an opportunity to develop new methods for generalization. First, behavioral science interventions are more often randomly assigned to students, not to intact schools; this results in the more powerful multi-site (i.e., random-block) design that, in addition to an average treatment impact, also allows for estimation of the distribution of treatment impacts across schools. Second, and perhaps of even greater importance, the fact that these behavioral science interventions do not focus on curricular changes and do not require large and lasting changes to school routines means that it is easier to recruit schools and students into the studies. The paper argues further that these differences allow researchers to shift from answering only questions about the average effect, to questions regarding variability in treatment impacts as well. Behavioral science interventions are becoming more common in education and policy more generally. With this comes the opportunity to develop generalizable theories of treatment effect heterogeneity--something that is difficult to do with whole-school reform. By doing so, researchers will be better situated to help policy makers and school officials understand where these brief interventions hold most promise, and where further research is needed. Tables and figures are appended.
Empirically Driven Variable Selection for the Estimation of Causal Effects with Observational Data
Author: Keller, Bryan
Date: 2016-00-00
Type: Reports - Research
Subjects: Computation,Influences,Observation,Data,Selection,Simulation,Methods,Identification,Statistical Bias,Regression (Statistics)

Observational studies are common in educational research, where subjects self-select or are otherwise non-randomly assigned to different interventions (e.g., educational programs, grade retention, special education). Unbiased estimation of a causal effect with observational data depends crucially on the assumption of ignorability, which specifies that potential outcomes under different treatment conditions are independent of treatment assignment, given the observed covariates. The primary goals in this paper are to: (1) propose and evaluate an empirically driven method for the identification and removal of potential instrumental and non-informative pretreatment variables based on lack of association with the outcome (though bias will exist in the presence of unmeasured confounders); and (2) to investigate, through simulation studies, the efficacy of three variable selection methods as measured by their success in identifying Instrumental variables (IVs) and Non-informative variables (NVs) and by improvement in bias and mean squared error relative to no variable selection at all. Two simulation studies were conducted. The conclusions of the simulations are twofold. First, the authors show that for the two data-generation processes used herein, preprocessing data to detect and remove potential instrumental and non-informative variables based on their relationships with the outcome improved the mean squared error of treatment effect estimation. Of course, this has to do with the bias/variance trade off: more sensitive variable selection methods do a better job detecting and removing noninformative variables (which decreases variance) while simultaneously dropping more weak confounders (which increases bias). Second, they find that recursive feature elimination with random forests is a promising method for predictor selection, as evidenced by strong performance in its naïve implementation across both simulation studies. Finally, we note that the ability of the methods to single out NVs and IVs depends in part upon the magnitudes and directions of correlations between unmeasured confounders and other predictors. Tables and figures are appended.