Data Quality
Data Quality is the collection of relevant, accurate data that can help program staff answer questions about recruitment, retention and the effectiveness of their programs and components.
Quick Review of the 2024 NRS Table Changes
This NRS Tips reviews changes to NRS tables resulting from renewal of the information collection in spring 2024 for reporting beginning in October 2024. It also explains how these changes expand reporting on measurable skills gains (MSGs) and what that means for data validation requirements.
Resources for Reporting Measurable Skill Gains (MSG) Types 3, 4, and 5
Adult education programs can now report additional types of MSG for workplace literacy and integrated education and training (IET) participants in NRS Tables 4 and 4c. The NRS tip sheet provides an overview of the MSG primary indicator of performance and reporting requirements, focusing on MSG types 3, 4 and 5. It includes a discussion of the validation and documentation required and examples of the types of MSG outcomes that can count.
NRS Tips: Increasing Posttesting to Improve Measurable Skill Gains
The primary indicators of performance for which adult education programs must collect data include measurable skill gains (MSG), or “a measure of a participant’s interim progress towards a credential or employment.” Although many factors affect an adult education program’s MSG outcome, a critical one is a program’s pretesting and posttesting rate. This NRSTips summarizes strategies that two states—Maine and Rhode Island—have used to successfully increase their posttesting rates.
NRS Tips: Collecting Data for Post-Exit Indicators in Practice
Under WIOA, adult education programs must collect data on program participants after program exit for each performance indicator. This NRSTips provides examples of successful strategies from one local program and one state for collecting required data on two performance indicators—credential attainment and employment.
Local Data Quality Checklist
States are required to submit an annual data quality checklist to the Office of Career, Technical and Adult Education. Similarly, some states may use a local data quality checklist that focuses on data collection and reporting activities, to help those programs remain informed about what is necessary to know and do to maintain data quality. This self-monitoring tool is available for local staff focused on data collection and reporting activities.
Demonstrating Success: A Technical Assistance Guide for Collecting Postexit Indicators
Performance on the postexit indicators demonstrates the success of programs in preparing participants for success in employment and postsecondary education. However, collecting the data for the indicators is new to adult education and poses many challenges to states. This guide serves as a technical assistance resource for state and local staff to understand the postexit indicators and suggests ways to improve the quality of these data. It explains the indicators and how each are collected and calculated and provides guidance to help states and programs overcome the many challenges they face in collecting these data. It also offers approaches to enhance the completeness and quality of data in each step of the data collection process.
Linking Data Quality with Action: Evaluating and Improving Local Program Performance
This guide offers new approaches and tools to identify and prevent data quality problems. It introduces a local data quality checklist, modeled after OCTAE’s state checklist, which states can use to understand and evaluate local data collection practices. This guide also brings together previously developed material on improving data quality into a single resource, a data quality toolkit that permits easy access to this content, to support ongoing state and local training around data quality practices.