Data Quality Basics: Understanding Data Quality

In this topic, you will build the skills and techniques you need to understand what high-quality data is and how to apply this understanding when reviewing Part C and Part B 619 data.

Learning Objectives

1. Understand characteristics of high-quality data​, why high-quality data matters​, how to improve data quality​, and how to apply this understanding to Part C and Part B 619 data.
2. Understand what data quality means in the context of Part B 619 child outcomes, learn about specific tools to measure the quality of child outcomes data, and feel empowered to speak about data quality during discussions about child outcomes results.

Data Leadership Competencies addressed by this content

• FD-4. Is knowledgeable about the various aspects of data quality such as accuracy, completeness, timeliness, and the processes that are needed to ensure high-quality data statewide. 
• FD-7. Is knowledgeable of policies and procedures required for the governance and management of Part C/Part B 619 data, including those related to the quality and integrity of the data, and the security of and access to those data.
• IN-2. Demonstrates the ability to establish policies and procedures that address the quality and integrity of Part C/Part B 619 data

Resources

How Measuring Your Preschool Child Outcomes Data Quality Can Lead to Data Use for Better Results
NASDSE 2020 presentation on how data quality can impact results and the identification of root causes and contributing factors for improvement.

State-Level Data Quality Considerations Worksheet
This DaSy-developed worksheet is provided to facilitate conversations among team members about state data quality issues. Notes can be recorded in the column provided.

Local-Level Data Quality Considerations Worksheet
This DaSy-developed worksheet is provided to facilitate conversations among team members about local data quality issues. Notes can be recorded in the column provided.

IDEA Data Quality: Outlier Analyses Tools
These IDEA Data Center products are designed to be used by the state personnel responsible for the IDEA 618 and/or 616 data. They include a brief that introduces the principles of outlier analysis, a tutorial on completing an outlier analysis, and a tool state staff can use to conduct outlier analyses with their local data.

Pattern checking for data quality
This table describes strategies for using data analysis to improve the quality of state data by looking for patterns that indicate potential issues for further investigation.

Working Principles of High-Quality IDEA Data
High-quality data are timely, accurate, and complete. In addition, they are usable, accessible, and secure. Educators and representatives at the local, state, and federal levels use high-quality early intervention and special education data to inform their decisions as they work to meet the needs of children and students with disabilities and their families. Use this interactive PDF to learn more about the different components of high-quality data.

Strategies for Increasing Data Quality for Child Outcomes Measurement
This presentation will contain results from a group of six states across Part C and Part B 619 who recently completed 18 months of intensive TA to improve the quality of their child outcomes data. The results of this work are several new and innovative approaches to informing programs about data collection procedures, using data analysis to monitor for data quality, and working collaboratively with locals to review and use data. This presentation will also contain WA Part C work with local programs to support the powerful use of data.

Data Culture Toolkit: Assess and Improve Data Quality
Select from the tabs above for more information, resources, and tools to support state and local professionals as they strive to assess and address data quality.

Reflection / Practice Activities

You can’t effectively make decisions about program operations, accountability, or improvement without high-quality data. Poor data quality has a big impact in terms of accuracy, time, and effort. Data quality includes the dependability, validity, completeness, and timeliness of the data. The people, procedures, and data systems devoted to quality assurance at the municipal and state levels and throughout the data life cycle play an important role in supporting these characteristics of data quality.

Think about the data you interact with. How do you know if your data measures up? Often, it is necessary to not only review the quality of the data (is the data reliable, valid and complete?), but also determine if the data can be effectively used for making programmatic decisions.

Published April 2023.