US PREP Developmental Framework

Effective data use practice begins with and is rooted in two primary areas of focus that drive all subsequent actions: data quality and data governance. These actions support data use practices that have integrity and value. Cohering around these key components allows for data to be discussed and used in ways that meet multiple objects. For example, when data quality and governance exist, trust can be developed which supports buy-in. Additionally, these priorities can support equity goals by providing a method to explore multiple perspectives and experiences. 

Data quality also includes many aspects, but the two that are most critical are validity and reliability. Validity is the degree to which data represents what it is intended to represent, while reliability refers to how trustworthy the data is over time. Both are important aspects of data, yet have very different purposes. Programs want to ensure that they are measuring what they intend to measure. Far too often, data is used to measure quality, learning, and growth that lacks either these two aspects. Each question, definition, and metric should carefully align to the intended goal. Likewise, programs should strive to make sure that the methods used to generate those metrics can be consistently reported over time. Annually revising methodologies or definitions are important activities that can reduce the likelihood of long-term problems. Likewise, data collection procedures that have inherent flaws that don’t control for certain variables can lead a program to misdiagnose quality. That said, what is most important is that data is collected, analyzed, and used for decision making.

Data governance is the second aspect of data quality that, even if not named as governance, should be deliberately and intentionally addressed on an ongoing basis. Data governance is the formal oversight process related to defining data sources and the processes for collecting, analyzing, and using data. Data governance is most effective when it is a shared responsibility with various levels of educator preparation program, district, and campus-based stakeholders represented, each of which have applicable and relevant knowledge about the data, how it’s collected, and the decisions it can best inform. Data governance addresses both the fidelity and integrity of the data and data collection protocols. By carefully and collaboratively reviewing governance structures, findings will remain clean and accurate. This formal process will also support integrity by ensuring processes remain sound and benefit all stakeholders.

This data toolkit is intended to assist educator preparation programs with diagnosing the status of their data use practices as well as providing resources that programs may find valuable in the development of their own protocols. Within the toolkit, programs will find an explanation and resources aligned to what US PREP has developed as “the Data Cycle.” This Cycle includes the planning, collecting, moving, analyzing, visualizing,using, and then closing “the loop” on data to inform and promote action.

As a place to start, we suggest taking the data diagnostic. This diagnostic will measure your familiarity with this data cycle and provide you with a place to start in using this toolkit. From there, programs can read more about individual steps and further assess their own progress.