DaSy Framework: Data Analysis and Use (DU) Subcomponent

The effective use of Part C and Part B 619 data is fundamental to the achievement of positive outcomes for children with disabilities and families. Achieving positive outcomes for all children with disabilities requires equitable access to IDEA services and the individualized and equitable provision of those services and supports. All high-quality state systems should be using data regularly to see if there are differences across subgroups which could be an indication that the system is not equitable and to identify the root causes for those differences and inform solutions. Part C and Part B 619 state staff need the knowledge and skills to formulate and answer critical questions about equitable access, services and supports, and positive outcomes for the overall population of children and families and for each of the various subgroups in the state.

The purpose of the Data Analysis and Use subcomponent is to assist state leaders in facilitating ongoing use of quality Part C and Part B 619 data for program accountability, program improvement, and program operations at state and local levels. Part C and Part B 619 state and local staff benefit from using data effectively, but they need knowledge and skills to be able to formulate critical questions about the outcomes of services provided. They also need to exhibit data leadership by setting expectations and supporting the conditions that will lead to effective data use at state and local levels. Effective data use requires ongoing planning, analysis, and dissemination of data products. In many cases, data become more valuable when they are linked with other data, creating additional information from which new data products can be developed (e.g., child outcome data with child service data). Data products are defined as all types of materials containing data, such as, for example, data tables, presentations, and reports.

Linking data with other data (e.g., child outcome data with child service data, Part C with 619 data) allows the state to answer critical questions that could not be answered by either data set alone. As data leaders, Part C and Part B 619 state staff need to understand the power and potential concerns associated with linked data and be able to actively participate in planning efforts that involve their linking program’s data with other data sets.

An assumption underlying the framework is that many different kinds of individuals, including those who have been historically underserved, should understand and use data. A data user is any person who accesses the data in any form, including raw data, data tables, data displays, reports, or any other data products. To be a skilled data user, individuals need professional development and access to technical expertise that supports their effective implementation. All data must be shared and used in compliance with data governance policies and with careful attention to the protection of personally identifiable information.

To achieve positive outcomes based on continuous improvement of programs and systems, the state needs to ensure availability of quality data; analyze, prepare, and disseminate a variety of data products; and provide leadership to build the capacity of state and local staff and stakeholders for effective data use.

This subcomponent consists of three sections. The first section, Data Availability, addresses activities that ensure that users of the data have the quality data they need when they need it. Next, the Data Analysis section addresses activities involving planning and conducting data analyses that meet the needs of the data users. The third section addresses Data Leadership and Data Use, the activities that support creating and maintaining the conditions for a culture of data use at state and local levels.

Section 1: Data Availability

Quality Indicator DU1

Part C/619 state staff implement the processes required to ensure quality data are available for analyses.

about DU1 Elements of Quality

DU1 – Elements of Quality
DU1a	All data sources are identified and documented.
DU1b	Clear, timely, and necessary guidance is provided for all data collections.
DU1c	State and local staff are trained on data collection and submission processes.
DU1d	Approved processes are in place to meet data requests of potential users (e.g., agency staff, researchers, legislators).
DU1e	A schedule or timeline is developed for accessing and preparing data for all required data analysis activities.
DU1f	Processes required to ensure quality data are reviewed and revised as needed.

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Section 2: Data Analysis

Quality Indicator DU2

Part C/619 state staff plan and prepare for data analyses.

about DU2 Elements of Quality

DU2 – Elements of Quality
DU2a	The purposes for the analyses are identified, including the critical questions to be addressed.
DU2b	Plans for data analysis routinely include critical questions to intentionally examine equitable access, services and supports, and outcomes.
DU2c	The type and format of data products that will be used to disseminate results of the analyses are identified.
DU2d	Data are reviewed and their completeness, accuracy, and timeliness are verified.
DU2e	Data analysis methods that are appropriate for the purpose and use of the data are identified.
DU2f	Data analysis plans are reviewed by individuals with relevant technical and programmatic expertise.

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Quality Indicator DU3

Part C/619 state staff conduct data analyses that meet the needs of the state agency and other users.

about DU3 Elements of Quality

DU3 – Elements of Quality
DU3a	Analyses are conducted consistent with the attributes of the data (e.g., data quality, significance levels, sample size), intended purposes, and the planned data products.
DU3b	The strengths and possible limitations of the analyses are identified.
DU3c	Results of the analyses are reviewed by individuals with relevant technical and programmatic expertise including the potential risks of misinterpretation.
DU3d	Data products are developed that meet the needs of intended users, incorporating where appropriate: dashboards that display multiple data views; comparative analyses (e.g., subgroups, trends) and examination of root causes; best practices for identifying, displaying, and labeling data by subgroups by disability characteristics, race, ethnicity, gender, immigration status, home language, geography, socioeconomic status; features to provide users with the ability to interact with the data (e.g., filtering, sorting); and effective data visualizations (e.g., clear and understandable); use of appropriate racial and cultural icons).
DU3e	Disclosure avoidance techniques are used to ensure that personally identifiable information (PII) is protected in accordance with federal and state requirements in all data products.
DU3f	Documentation is developed to support future replication of the analyses conducted (e.g., data elements, tools and methods used, strengths/limitations of data analysis and results, data products developed) where applicable.

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Section 3: Data Leadership and Data Use

Quality Indicator DU4

State Part C/619 coordinators function as data leaders to create and maintain the conditions for a culture of data use at the state level.

about DU4 Elements of Quality

DU4 – Elements of Quality
DU4a	Staff have the knowledge and skills necessary to use data to inform decision-making, including using data to examine equitable access, services and supports, and outcomes.
DU4b	A commitment to using data for decision-making exists throughout the agency.
DU4c	Data are high quality and comprehensive.
DU4d	Data products (both routine and ad hoc) are available and timely.
DU4e	Data are routinely made available across administrative units for collaborative use (e.g., monitoring, fiscal, contracts, programs).
DU4f	The strengths and limitations of the analyses and the potential risks of misinterpretation are shared with users of the data products (e.g., data quality, significance levels, small sample size, comparative analyses such as by race, ethnicity, disability characteristics).
DU4g	Staff participate in efforts to share IDEA data with and access other early childhood data across programs, agencies, or initiatives (e.g., Medicaid, ECIDS, Child Welfare, EHDI, SLDS, Education).

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Quality Indicator DU5

Part C/619 state staff lead an ongoing data-informed decision-making process (i.e., review of data analyses, interpret results, and make decisions informed by the data).

about DU5 Elements of Quality

DU5 – Elements of Quality
DU5a	Effective and routine processes for data-informed decision-making have been adopted (e.g., Plan Do Study Act, Change Management).
DU5b	Processes for data-informed decision-making are implemented consistently by individuals and teams.
DU5c	Stakeholder groups that represent the full range of diversity in the state, especially those who have been historically underserved, participate in data-informed decision-making processes as appropriate to the topic.
DU5d	Data from cross-program and cross-agency partnerships are used for program improvement.
DU5e	Processes for data-informed decision-making are reviewed and revised as needed.
DU5f	Data-informed decision-making processes routinely and intentionally use data to examine equitable access, experiences, and outcomes.

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Quality Indicator DU6

Part C/619 state staff support local programs or districts in building a culture of data use.

about DU6 Elements of Quality

DU6 – Elements of Quality
DU6a	Professional development opportunities are available to build data skills of local programs or district administrators, staff, and stakeholders.
DU6b	Supports are provided to ensure data needed by local program or districts to inform decisions are high quality and comprehensive.
DU6c	Data products and displays for local programs and districts are available and timely.
DU6d	Supports are provided to local programs or districts to engage stakeholders in the ongoing use of data.
DU6e.	Supports are provided to create and sustain local use of data for decision-making by individuals and teams.
DU6f	Supports are provided to local programs or districts to implement data-informed decision-making processes that routinely and intentionally use data to examine equitable access, services and supports, and outcomes.
DU6g	Supports are reviewed and revised as needed based on local program or district feedback.

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Resources Related to Data Analysis and Use

Critical Questions About Early Intervention and Early Childhood Special Education
What questions should data users, such as program directors, advocates, and policymakers, be asking?
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Data Visualization Toolkit
This DaSy toolkit provides guidance for effectively creating and presenting data visuals.
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Planning, Conducting, and Documenting Data Analysis for Program Improvement
This 2015 document was developed to help technical assistance (TA) providers and state staff define and limit the scope of data analysis for program improvement ...
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Data Meeting Toolkit
This toolkit is a suite of tools that groups can use to guide conversation around data and support databased decisionmaking. The toolkit provides resources to support ...
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Data Culture Toolkit
The DaSy toolkit is a resource containing information, guidance, and templates to help Part C and Part B 619 program staff build effective data teams ...
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Early Childhood Data Use Assessment Tool
This resource is designed to identify and improve data use skills among early childhood education (ECE) program staff so that they can use data to ...
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Answering Key Questions with an Early Childhood Data System
This 2013 issue brief describes a 7-step process to design and use essential questions in an early childhood data system. Suggested stakeholders, their roles, and ...
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Data use for continuous quality improvement: What the Head Start field can learn from other disciplines, a literature review and conceptual framework
This report includes a comprehensive literature review and conceptual framework produced as part of the Head Start Leadership, Excellence, and Data Systems project. The review ...
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