Search Results: (1-15 of 36 records)
|NFES 2021110||Forum Guide to Metadata
The Forum Guide to Metadata presents and examines the ways in which metadata can be used by education agencies to improve data quality and promote a better understanding of education data. Supported by metadata-related case studies from state and local education agencies, the guide highlights the uses of metadata from a technical point of view, as well as the perspectives of data management, data reporting and use, and data privacy and security. The guide further discusses how to plan and successfully implement a metadata system in an education setting and provides examples of standard metadata items and definitions to assist agencies with standardization.
|NFES 2021094||Forum Guide to Staff Records
The Forum Guide to Staff Records was developed to help education agencies effectively collect and manage staff data; protect the privacy of staff data; and ensure that requests for data access and data releases are managed appropriately. The guide builds on information from the 2000 publication, Privacy Issues in Education Staff Records: Guidelines for Education Agencies and reflects how agencies have responded to changes in staff data over time. It includes a discussion of types of staff records, updated best practices for data collection and management, and case studies from state and local education agencies.
|NFES 2021078||Forum Guide to Virtual Education Data: A Resource for Education Agencies
The Forum Guide to Virtual Education Data: A Resource for Education Agencies is designed to assist agencies with collecting data in virtual education settings, incorporating the data into governance processes and policies, and using the data to improve virtual education offerings. This resource reflects lessons learned by the education data community during the coronavirus disease (COVID-19) pandemic and provides recommendations that will help agencies collect and use virtual education data.
|NFES 2021013|| Forum Guide to Strategies for Education Data Collection and Reporting (SEDCAR)
The Forum Guide to Strategies for Education Data Collection and Reporting (SEDCAR) was created to provide timely and useful best practices for education agencies that are interested in designing and implementing a strategy for data collection and reporting, focusing on these as key elements of the larger data process. It builds upon the Standards for Education Data Collection and Reporting (published by the Forum in 1991) and reflects the vast increase over the past three decades in the number of compulsory and/or continual data collections conducted by education agencies. This new resource is designed to be relevant to the state and local education agencies (SEAs and LEAs) of today, in which data are regularly collected for multiple purposes, and data collection and recording may be conducted by many different individuals within an agency.
|NFES 2020132||Forum Guide to Exit Codes
The Forum Guide to Exit Codes provides best practice information for tracking data about when students transferred, completed high school, dropped out, or otherwise exited an education agency. This resource defines exit codes and reviews their use in an education agency; provides an updated, voluntary, common taxonomy for exit codes; discusses best practices and methods for addressing specific challenges in exit codes data collection; features case studies that highlight different education agencies’ approaches to and experiences with exit coding.
|IES 2020001REV||Cost Analysis: A Starter Kit
This starter kit is designed for grant applicants who are new to cost analysis. The kit will help applicants an a cost analysis, setting the foundation for more complex economic analyses.
|NFES 2019160||Forum Guide to Personalized Learning Data
The Forum Guide to Personalized Learning Data is designed to assist education agencies as they consider whether and how to use personalized learning. It provides an overview of personalized learning and describes best practices used by education agencies to collect data for personalized learning; to use those data to meet goals; and to support relationships, resources, and systems needed for the effective use of data in personalized learning. Personalized learning is still a developing prospect in many locations. therefore, the concepts and examples provided are intended to help facilitate idea sharing and discussion.
|NFES 2019035||Forum Guide to Early Warning Systems
The Forum Guide to Early Warning Systems provides information and best practices to help education agencies plan, develop, implement, and use an early warning system in their agency to inform interventions that improve student outcomes. The document includes a review of early warning systems and their use in education agencies and explains the role of early warning indicators, quality data, and analytical models in early warning systems. It also describes how to adopt an effective system planning process and recommends best practices for early warning system development, implementation, and use. The document highlights seven case studies from state and local education agencies who have implemented, or are in the process of implementing, an early warning system.
|NFES 2018156||Forum Guide to Facility Information Management: A Resource for State and Local Education Agencies
The Forum Guide to Facility Information Management: A Resource for State and Local Education Agencies helps state and local education agencies plan, design, build, use, and improve their facility information systems. It includes a review of why school facilities data matter and recommends a five-step process that an education agency can undertake to develop a robust facility information system around goals, objectives, and indicators. The document also includes selected measures of school facilities quality and offers a logical approach to organizing facility and site data elements associated with facility identification, condition, design, utilization, management, and budget and finance.
|NCEE 20174027||Multi-armed RCTs: A design-based framework
Design-based methods have recently been developed as a way to analyze data for impact evaluations of interventions, programs, and policies. The estimators are derived using the building blocks of experimental designs with minimal assumptions, and have important advantages over traditional model-based impact methods. This report extends the design-based theory for the single treatment-control group design to designs with multiple research groups. It discusses how design-based estimators found in the literature need to be modified for multi-armed designs when comparing pairs of research groups to each other. It also discusses multiple comparison adjustments when conducting hypothesis tests across pairwise contrasts to identify the most effective interventions. Finally, it discusses the complex assumptions required to identify and estimate the complier average causal effect (CACE) parameter for multi-armed designs.
|NCEE 20174025||What is Design-Based Causal Inference for RCTs and Why Should I Use It?
Design-based methods have recently been developed as a way to analyze data from impact evaluations of interventions, programs, and policies. The approach uses the building blocks of experimental designs to develop impact estimators with minimal assumptions. The methods apply to randomized controlled trials and quasi-experimental designs with treatment and comparison groups. Although the fundamental concepts that underlie design-based methods are straightforward, the literature on these methods is technical, with detailed mathematical proofs required to formalize the theory. This brief aims to broaden knowledge of design-based methods by describing their key concepts and how they compare to traditional model-based methods, such as such as hierarchical linear modeling (HLM). Using simple mathematical notation, the brief is geared toward researchers with a good knowledge of evaluation designs and HLM.
|NFES 2016095||Forum Guide to Elementary/Secondary Virtual Education Data
Forum guide to elementary/secondary virtual education data was developed to assist education agencies as they: 1) consider the impact of virtual education on established data elements and methods of data collection, and 2) address the scope of changes, the rapid pace of new technology development, and the proliferation of resources in virtual education.
|NCEE 20154013||A Guide to Using State Longitudinal Data for Applied Research
State longitudinal data systems (SLDSs) promise a rich source of data for education research. SLDSs contain statewide student data that can be linked over time and to additional data sources for education management, reporting, improvement, and research, and ultimately for informing education policy and practice.
Authored by Karen Levesque, Robert Fitzgerald, and Joy Pfeiffer of RTI International, this guide is intended for researchers who are familiar with research methods but who are new to using SLDS data, are considering conducting SLDS research in a new state environment, or are expanding into new topic areas that can be explored using SLDS data. The guide also may be useful for state staff as background for interacting with researchers and may help state staff and researchers communicate across their two cultures. It highlights the opportunities and constraints that researchers may encounter in using state longitudinal data systems and offers approaches to addressing some common problems.
|NFES 2015158||Forum Guide to Alternative Measures of Socioeconomic Status in Education Data Systems
The Forum Guide to Alternative Measures of Socioeconomic Status in Education Data Systems provides “encyclopedia-type” entries for eight plausible alternative measures of socioeconomic status (SES) to help readers better understand the implications of collecting and interpreting a range of SES-related data in education agencies. Chapter 1 reviews recent changes in how SES data are collected in many education agencies and presents a call to action to the education community. Chapter 2 reviews practical steps an agency can take to adopt new measures. Chapter 3 describes each of the eight alternative measures, including potential benefits, challenges, and limitations of each option.
|NFES 2015157||Forum Guide to College and Career Ready Data
The Forum Guide to College and Career Ready Data examines how data are being used to support CCR initiatives. Chapter 1 presents an overview of college and career readiness. Chapter 2 focuses on five specific uses for data to support CCR programs: fostering individualized learning for students; supporting educators in addressing student-specific needs; guiding CCR programmatic decisions through the use of postsecondary feedback loops; measuring agency progress in meeting CCR accountability and continuous improvement goals; and maximizing career opportunities for all students. Each of the use cases includes policy and program questions to consider, a list of key data needs, useful analytics, suggested feedback to request from data users, and emerging needs related to the data use. Chapter 3 outlines a number of overarching issues for the use of CCR data, and Chapter 4 summarizes key points and emerging needs identified throughout the Guide.
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