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National Forum on Education Statistics, 2023
The Forum Guide to Data Quality provides best practices from districts and states for maintaining quality data as these agencies regularly review and revise their methods for working with data, as well as their expectations for all staff who are responsible for education data and their quality. This resource is intended to help districts and…
Descriptors: Data Collection, Quality Control, Best Practices, Educational Indicators
US Department of Education, 2006
Spurred by the "No Child Left Behind Act" of 2001, virtually every educational reform program now includes an accountability component that requires sound data collection and reporting. Improving data quality has thus emerged as a high priority for educators and policymakers across the country. The list of programs for which data quality…
Descriptors: Accountability, Data Collection, Federal Legislation, Improvement
Robertson, Robert E. – US Government Accountability Office, 2005
GAO prepared this report under the Comptroller General's authority as part of an effort to assist policy makers in determining how federal disability programs could more effectively meet the needs of individuals with disabilities and addressed it to each committee of jurisdiction. In this report, GAO assesses: (1) the extent to which state…
Descriptors: Federal Programs, Disabilities, Vocational Rehabilitation, Program Effectiveness
Advanced Technology, Inc., Reston, VA. – 1984
Edits that can be performed by the processor who applies for federal campus-based student aid programs are discussed, along with a longer-term approach to assessing the efficacy of recommended edit checks. Attention is focused on the accuracy of data submitted by institutions on the application portion of the Fiscal Operations Report and…
Descriptors: Computation, Data Collection, Error Patterns, Evaluation Methods
Advanced Technology, Inc., Reston, VA. – 1985
Efforts of the Department of Education (ED) to simplify the Pell Grant formula by reducing the number of data elements used to calculate awards (i.e., data element reduction) are evaluated. A framework is developed to assess the critical characteristics of individual data elements, to eliminate elements from the formula, and to develop proposals…
Descriptors: Computation, Data Collection, Efficiency, Eligibility
Jerald, Craig – Center for Comprehensive School Reform and Improvement, 2005
This is the last in a series of four policy briefs to be published by The Center for Comprehensive School Reform and Improvement in 2005. The briefs are intended to provide fresh insights and useful advice to policymakers and school assistance providers. The first part of this policy brief breaks down the process of sustaining improvement and…
Descriptors: School Restructuring, Educational Change, Elementary Secondary Education, Academic Achievement
Advanced Technology, Inc., Reston, VA. – 1984
Errors made in the allocation of federal funds to institutions for the three campus-based aid programs specified in Title IV of the Higher Education Act are discussed. Funds are allocated directly to participating colleges, which in turn award the money to students, for the following programs: Supplemental Education Opportunity Grants, College…
Descriptors: Accountability, Computation, Data Collection, Enrollment Rate
Advanced Technology, Inc., Reston, VA. – 1984
Error analysis for the institutional process of applying for federal aid is considered as part of the Quality Control Study of the U.S. Office of Student Financial Aid. Attention is focused on the results of the data collection activities that occurred using the Department of Education's records for the Fiscal-Operations Report and Application to…
Descriptors: Accountability, Computation, Data Collection, Enrollment Rate
Advanced Technology, Inc., Reston, VA. – 1984
The impact of data discrepancies made by colleges on the Fiscal Operations Report and Application to Participate (FISAP) is addressed, with attention to both impact on the entire aid program and resource allocation to institutions. Brief descriptions are provided of the allocation formulas for the three campus-based aid programs (Supplemental…
Descriptors: Computation, Data Collection, Error Patterns, Evaluation Criteria
Bosker, Roel; And Others – 1996
This article explores the following questions: first, "Do colleges of higher vocational education vary in the mean success of their graduates on the labor market?" and, second, "Are the differences between these colleges stable over time?". The analysis drew upon school effectiveness, quality assurance and labor market…
Descriptors: Accountability, College Outcomes Assessment, College Students, Data Collection
Advanced Technology, Inc., Reston, VA. – 1983
The extent to which colleges are complying with the 1982-1983 Pell Grant validation requirements was assessed. Fall 1982 financial aid data were drawn from a representative sample of 3,490 Pell Grant recipients at 317 colleges that are part of the Regular Disbursement System. Key findings show: (1) the vast majority of institutions collect the…
Descriptors: Accountability, Compliance (Legal), Data Collection, Delivery Systems
Advanced Technology, Inc., Reston, VA. – 1983
The issues, options, and procedures for annually measuring overall payment error in the Pell Grant program are specified in detail. Guidelines for establishing a definition of Pell Grant payment error are provided, and the design issues related to error measurement are examined. A comparison is made of options for selecting a study sample and for…
Descriptors: Accountability, Compliance (Legal), Costs, Data Collection
Condelli, Larry; Castillo, Laura; Seburn, Mary; Deveaux, Jon – 2002
This guide for improving the quality of National Reporting System for Adult Education (NRS) data through improved data collection and training is intended for local providers and state administrators. Chapter 1 explains the guide's purpose, contents, and use and defines the following components of data quality: objectivity; integrity;…
Descriptors: Accountability, Adult Education, Audits (Verification), Compliance (Legal)