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 Pub Number  Title  Date
REL 2017269 Comparing enrollment, characteristics, and academic outcomes of students in developmental courses and those in credit-bearing courses at Northern Marianas College
This study reports on the academic outcomes of full-time first time freshman seeking associate degrees who entered Northern Marianas College from fall semester 2008 through fall semester 2010. In English, 80.1 percent of these students enrolled in developmental courses; in math, 91 percent enrolled in developmental courses. To determine their academic outcomes, these students were tracked for eight semesters after their first year in college. The study found that students who initially enrolled in credit-bearing English or math classes had consistently more positive outcomes than students who initially enrolled in non-credit developmental English or math courses.
4/26/2017
REL 2017268 Using high school data to understand college readiness in the Northern Mariana Islands
This report examines the college readiness of public high school graduates in the Northern Mariana Islands as measured by whether the graduates were placed in developmental college courses or credit bearing college courses at Northern Marianas College. The study examined the high school records of recent graduates of the public school system in the Northern Mariana Islands who entered Northern Marianas College from fall semester 2010 through spring semester 2014. Demographic information was available about students' gender, ethnicity, primary language spoken at home, and economic need (based on whether or not students received Pell grants). The study found that 19.6 percent of students placed into credit-bearing English courses. Nearly 23 percent of female students, compared to about 16 percent of male students, placed into credit-bearing English courses. In math, 7.8 percent of students placed into credit-bearing courses. Students who did not receive Pell grants were more likely to place into credit-bearing math courses.
4/26/2017
REL 2017240 School discipline data indicators: A guide for districts and schools
Disproportionate rates of suspension for students of color are a local, state, and national concern. In particular, African American, Hispanic/Latino(a), and American Indian students experience suspensions more frequently than their White peers. Disciplinary actions that remove students from classroom instruction undermine their academic achievement and weaken their connection with school. This REL Northwest guide is designed to help educators use data to reduce disproportionate rates of suspension and expulsion based on race or ethnicity. It provides examples of selecting and analyzing data to determine whether racial disproportionality exists in a school or district's discipline practices. The guide also describes how to apply the Plan-Do-Study-Act continuous improvement cycle to inform intervention decisions and monitor progress toward desired outcomes.
4/14/2017
REL 2017263 Analyzing student-level disciplinary data: A guide for districts
The purpose of this report is to help guide districts in analyzing their own student-level disciplinary data to answer important questions about the use of disciplinary actions. This report, developed in collaboration with the Regional Educational Laboratory Northeast and Islands Urban School Improvement Alliance, provides information to district personnel about how to analyze their student-level data and answer questions about the use of disciplinary actions, such as whether these actions are disproportionately applied to some student subgroups, and whether there are differences in student academic outcomes across the types of disciplinary actions that students receive. This report identifies several considerations that should be accounted for prior to conducting any analysis of student-level disciplinary data. These include defining all data elements to be used in the analysis, establishing rules for transparency (including handling missing data), and defining the unit-of-analysis. The report also covers examples of descriptive analyses that can be conducted by districts to answer questions about their use of the disciplinary actions. SPSS syntax is provided to assist districts in conducting all of the analyses described in the report. The report will help guide districts to design and carry out their own analyses, or to engage in conversations with external researchers who are studying disciplinary data in their districts.
3/29/2017
REL 2017221 The "I" in QRIS Survey: Collecting data on quality improvement activities for early childhood education programs
Working closely with the Early Childhood Education Research Alliance and Iowa’s Quality Rating System Oversight Committee, Regional Educational Laboratory Midwest developed a new tool—the "I" in QRIS Survey—to help states collect data on the improvement activities and strategies used by early childhood education (ECE) providers participating in a Quality Rating and Improvement System (QRIS). As national attention increasingly has focused on the potential for high-quality early childhood education and care to reduce school-readiness gaps, states developed QRIS to document the quality of ECE programs, support systematic quality improvement efforts, and provide clear information to families about their child care choices. An essential element of a QRIS is the support offered to ECE providers to assist them in improving their quality. Although all the Midwestern states offer support to ECE providers to improve quality as part of their QRIS, states do not collect information systematically about how programs use these quality improvement resources. This survey measures program-level participation in workshops and trainings, coaching, mentoring, activities aimed at increasing the educational attainment of ECE staff, and financial incentive to encourage providers to improve quality. States can use this tool to document the current landscape of improvement activities, to identify gaps or strengths in quality improvement services offered across the state, and to identify promising improvement strategies. The survey is intended for use by state education agencies and researchers interested in the "I" in QRIS and can be adapted for their specific state context.
2/28/2017
REL 2017167 A comparison of two approaches to identifying beating-the-odds high schools in Puerto Rico
The Regional Educational Laboratory Northeast and Islands conducted this study using data on public high schools in Puerto Rico from national and territory databases to compare methods for identifying beating-the-odds schools. Schools were identified by two methods, a status method that ranked high-poverty schools based on their current observed performance and an exceeding-achievement-expectations method that ranked high-poverty schools based on the extent to which their actual performance exceeded (or fell short of) their expected performance. Graduation rates, reading proficiency rates, and mathematics proficiency rates were analyzed to identify schools for each method. The identified schools were then compared by method to determine agreement rates—that is, the amount of overlap in schools identified by each method. The report presents comparisons of the groups of schools—those identified by each method and all public high-poverty high schools in Puerto Rico—on descriptive information. Using the two methods—ranking by status and ranking by exceeding-achievement-expectations—two different lists of beating-the-odds schools were identified. The status method identified 17 schools, and the exceeding-achievement-expectations method identified 15 schools. Six schools were identified by both methods. The agreement rate between the two lists of beating-the-odds schools was 38 percent. The analyses suggest that using both methods to identify beating-the-odds schools is the best strategy because high schools identified by both methods demonstrate high levels of absolute performance and appear to be achieving higher levels of graduation rates and percent proficiency than might be expected given their demographics and prior performance.
12/6/2016
NFES 2017016 Forum Guide to Data Visualization: A Resource for Education Agencies
The purpose of this publication is to recommend data visualization practices that will help education agencies communicate data meaning in visual formats that are accessible, accurate, and actionable for a wide range of education stakeholders. Although this resource is designed for staff in education agencies, many of the visualization principles apply to other fields as well.
10/31/2016
REL 2016218 Self-study guide for implementing high school academic interventions
This Self-study Guide for Implementing High School Academic Interventions was developed to help district- and school-based practitioners plan and implement high school academic interventions. It is intended to promote reflection about current district and school strengths and challenges in planning for implementation of high school academic interventions, spark conversations among staff, and identify areas for improvement. The guide provides a template for data collection and guiding questions for discussion that may improve the implementation of high school academic interventions and decrease the number of students failing to graduate from high school on time.
8/23/2016
REL 2016164 Survey methods for educators: Analysis and reporting of survey data (part 3 of 3)
Educators at the state and local levels are increasingly using data to inform policy decisions. While student achievement data is often used to inform instructional or programmatic decisions, educators may also need additional sources of data, some of which may not be housed in their existing data systems. Creating and administering surveys is one way to collect such data. However, documentation available to educators about administering surveys may provide insufficient guidance about sampling or analysis approaches. Furthermore, some educators may not have training or experience in survey methods. In response to this need, REL Northeast & Islands created a series of three complementary guides that provide an overview of the survey research process designed for educators. The guides describe (1) survey development, (2) sampling respondents and survey administration, and (3) analysis and reporting of survey data.

Part three of this series, "Analysis and Reporting of Survey Data," outlines the following steps, drawn from the research literature:

1. Review the analysis plan
2. Prepare and check data files
3. Calculate response rates
4. Calculate summary statistics
5. Present the results in tables or figures
The guide provides detailed, real-world examples of how these steps have been used in a REL research alliance project. With this guide, educators will be able to analyze and report their own survey data.
8/2/2016
REL 2016160 Survey methods for educators: Selecting samples and administering surveys (part 2 of 3)
Educators at the state and local levels are increasingly using data to inform policy decisions. While student achievement data is often used to inform instructional or programmatic decisions, educators may also need additional sources of data, some of which may not be housed in their existing data systems. Creating and administering surveys is one way to collect such data. However, documentation available to educators about administering surveys may provide insufficient guidance about sampling or analysis approaches. Furthermore, some educators may not have training or experience in survey methods. In response to this need, REL Northeast & Islands created a series of three complementary guides that provide an overview of the survey research process designed for educators. The guides describe (1) survey development, (2) sampling respondents and survey administration, and (3) analysis and reporting of survey data.

Part two of this series, "Sampling Respondents and Survey Administration," outlines the following steps, drawn from the research literature:

1. Define the population
2. Specify the sampling procedure
3. Determine the sample size
4. Select the sample
5. Administer the survey
The guide provides detailed, real-world examples of how these steps have been used in a REL research alliance project. With this guide, educators will be able to develop their own sampling and survey administration plans.
8/2/2016
NCES 2016062 2012/14 Beginning Postsecondary Students Longitudinal Study (BPS:12/14) Data File Documentation
This publication describes the methodology used in the 2012/14 Beginning Postsecondary Students Longitudinal Study (BPS:12/14). BPS:12/14 is the first follow-up study of students who began postsecondary education in the 2011 – 12 academic year. These students were first interviewed as part of the 2011 – 12 National Postsecondary Student Aid Study (NPSAS:12). In particular, this report details the methodology and outcomes of the BPS:12/14 sample design, student interview design, student interview data collection processes, administrative records matching, data file processing, and weighting procedures.
5/31/2016
NCES 2015141 2008/12 Baccalaureate and Beyond Longitudinal Study (B&B:08/12): Data File Documentation
This publication describes the methods and procedures used in the 2008/12 Baccalaureate and Beyond Longitudinal Study (B&B:08/12). These graduates, who completed the requirements for a bachelor’s degree during the 2007–08 academic year, were first interviewed as part of the 2008 National Postsecondary Student Aid Study (NPSAS:08), and then followed up in 2009 as part of B&B:08/09. B&B:08/12 is the second follow-up interview of this cohort. This report details the methodology and outcomes of the B&B:08/12 student interview data collection and administrative records matching.
6/1/2015
REL 2015080 Instructional improvement cycle: A teacher's toolkit for collecting and analyzing data on instructional strategies
This toolkit, developed by REL Central in collaboration with York Public Schools in Nebraska, provides a process and tools to help teachers use data from their classroom assessments to evaluate promising practices. The toolkit provides teachers with guidance on how to deliberately apply and study one classroom strategy over the course of one unit and systematically document and compare results to consider the effects of a given instructional strategy on student learning. The process for testing the strategy uses a scientific approach by comparing the performance of students who receive the strategy to the performance of a similar group of students who do not receive the strategy. Teachers can use this information to reflect on their practice and consider adjustments to their instruction to increase student learning.
5/5/2015
REL 2015043 Practitioner Data Use in Schools: Workshop Toolkit
The Practitioner Data Use Workshop Toolkit is designed to help practitioners systematically and accurately use data to inform their teaching practice. The toolkit includes an agenda, slide deck, participant workbook, and facilitator’s guide and covers the following topics: developing data literacy, engaging in a cycle of inquiry, accessing and analyzing available data, identifying and creating student goals, and using data to make action plans about instructional decisions. The workshop was used with three REL-NEI research alliances: the Northeast Educator Effectiveness Research Alliance, the Urban School Improvement Alliance, and the Northeast Rural Districts Research Alliance, and can be customized for use in other contexts.
12/29/2014
REL 2015046 A Primer for Analyzing Nested Data: Multilevel Modeling in SPSS Using an Example from a REL Study
Analyzing data that possess some form of nesting is often challenging for applied researchers or district staff who are involved in or in charge of conducting data analyses. This report provides a description of the challenges for analyzing nested data and provides a primer of how multilevel regression modeling may be used to resolve these challenges. An illustration from the companion report, The correlates of academic performance for English language learner students in a New England district (REL 2014–020), is provided to show how multilevel modeling procedures are used and how the results are interpreted.
12/23/2014
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