EC Data University – Data Foundations Path

Introduction

Basic data skills, such as knowing what data are available, the purpose for collecting the data, how to collect the data, how the data will be used, and when to discard the data to make decisions for instruction and program improvement, are referring to the foundational concepts and skills, that once mastered, work with data will be easier and more meaningful!  

Once you have completed the four courses on this path you will be on your way to making data-informed decisions and informing instructional practices to improve student outcomes.

This path contains four courses:

  • Data Quality Basics: Understanding Data Quality
  • Data Quality Basics: Validity and Reliability
  • Common Part C and Part B 619 Data Elements
  • Qualitative and Quantitative Data

Each course has learning objectives, content, and practice/reflection activities. Scroll down to access the courses contained in this path.


Course #1 – Data Quality Basics: Understanding Data Quality

Course Learning Objectives

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

Data Leadership Competencies this course addresses

  • 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.

Course Content



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 cannot effectively make decisions about program operations, accountability, or improvement without high-quality data. Poor data quality actually has a big impact on the bottom line in terms of accuracy, time, and effort. Numerous qualities are included in data quality, including dependability, validity, completeness, and timeliness. The people, procedures, and data systems devoted to quality assurance at the municipal and state levels and throughout the data life cycle support and are necessary for these qualities.

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

The Data Culture Toolkit: Data Quality Considerations can help you think about and examine your data. Look at the 9 Data Quality Considerations and answer the questions for the data you work with

The Working Principals of High Quality Data is an interactive PDF to learn more about the different components of high-quality data. High-quality data are timely, accurate, and complete. 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.

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

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


Course #2 – Data Quality Basics: Validity and Reliability

Course Learning Objectives

  1. Examine the concepts of high-quality data, focusing on reliability and validity.
  2. Learn how the concepts of validity and reliability apply to Part C and Part B Section 619 data collection, analysis, and use.

Data Leadership Competencies this course addresses

  • 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.

Course Content



For reference: Validity of the Child Outcomes Summary (COS) Process Data: An Overview of Findings from the ENHANCE Project
This joint ECTA Center/DaSy Center webinar offered an overview of final results from the ENHANCE project. Four studies conducted in eight states provided information about the implementation and validity of COS information for accountability and program improvement. Results were drawn from a provider survey, coding of videos of the COS process, a study examining relationships between the COS and assessment tool scores, and examination of statewide and national population data. Participants learned about the findings and implications for states using the COS process.

Reflection / Practice Activities

The evidence of validity and reliability is a prerequisite to ensure quality data. Consider your data – can you tell that the data is valid / reliable? What evidence do you have? What else do you need?

Are the data collection procedures capable of producing valid and reliable data?

  • Identify the specific data elements
  • Define the data elements and communicate the agreed upon definitions
  • Communicate how each data element is related to the performance indicator. Is it directly or indirectly related?

Do you have the resources to do all the necessary steps to ensure the Data you need to collect will be valid and reliable?

  • Do you have written procedures for data collection? Training?
  • Are the data collection procedures capable of producing valid and reliable data?
  • Are the data collection procedures efficient?
  • What is/are the best source(s) of the data (e.g., families, program administrators)?
  • Are there existing reliable sources of data?
  • How will the information be collected, e.g., data system, checklist, self-rating scale, behavioral observation, interviews, etc.?

Have you identified ways to improve data quality?

  • Have you provided communication and training to ensure data quality?
  • Are procedures in place to check for the completeness, accuracy and timeliness of the data? Will this be done at the state or local level?
  • How can you use edit or consistency checks to improve data entry and data quality?
  • When low quality data are identified, how will you address it?

The Refining Your Evaluation Data Pathway – From Source to Use


Course #3 – Common Part C and Part B 619 Data Elements

Course Learning Objectives

  1. Learn about the Common Education Data Standards (CEDS).
  2. Learn how to utilize the CEDS ALIGN and CONNECT Tools to connect Part C and Part B 619 Data.
  3. Learn the benefits of developing data systems across Part C and Part B/619 programs with common data elements and definitions

Data Leadership Competencies this course addresses

  • FD-3. Is knowledgeable about relevant policies such as the Individuals with Disabilities Education Act (IDEA), Family Educational Rights and Privacy Act (FERPA), Health Insurance Portability and Accountability Act (HIPAA), and state regulations that address the privacy and security of Part C/Part B 619 data/records.
  • 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. 
  • IN-3.   Demonstrates the ability to establish policies and procedures that address the security of and access to Part C/ Part B 619 data.
  • IN-5.   Demonstrates the ability to delineate appropriate roles and responsibilities for decision-making authority, accountability, and management related to collection, analysis, use, and dissemination of Part C/ Part B 619 data.

Course Content



Common Data Elements and Definitions for Part C and 619 Programs
This presentation combines general CEDS information with opportunities for hands-on learning. It starts with an overview that will ensure that participants are knowledgeable in CEDS and how it can help early learning.

Part C IDEA Data Processes Toolkit
Using the Part C IDEA Data Processes Toolkit to document data processes for all 616 and 618 data collections will establish a well-managed process for data collection, validation, and submission. The toolkit contains an overview of the toolkit, Data Collection Protocols, SPP/APR Indicator Protocols, a State Landscape Protocol, a Local EI Program Determinations Protocol, a Data Collections Calendar, and additional resources that provide a structure for documenting data processes.

Part C Exiting Toolkit
Part C Exiting Toolkit allows users to access five different downloadable forms that will assist in the documentation of their Part C Exiting Process and provides checklists they can use to ensure high-quality data. The toolkit also contains the Part C Exiting Counts app. The app is a great tool for understanding the 10 federal Part C Exiting categories. The toolkit also contains links to documents that use Part C Exiting data and to other related resources.

Reflection / Practice Activities

Are you working in your state to develop a common data vocabulary?

Are you currently using CEDS?


Course #4 – Qualitative and Quantitative Data

Course Learning Objectives

  1. Understand the difference between the terms “quantitative” and “qualitative”
  2. Coming soon!

Data Leadership Competencies this course addresses

  • FD-5. Is knowledgeable about various methods for collecting data, including quantitative and qualitative methods, when to use various methods, and the advantages and limitations of such methods with different groups of respondents, especially those from historically underserved groups.

Course Content



Data Visualization Toolkit: Qualitative Data Design Principles
Qualitative methods allow for collecting and analyzing information that cannot be easily understood through numbers. Qualitative data can include open-ended survey responses, interview and focus group transcripts, observation notes, secondary data from organizations (e.g., reports, strategic plans, and policy documents), photographs, videos, and virtual communications such as emails and social media posts. Displaying qualitative data can help you explore, understand, and explain the information being presented.

Collecting, Analyzing, and Sharing Qualitative Data Encore Webinar
This webinar includes best practices in collecting, analyzing and sharing qualitative data with an example from Louisiana’s SSIP work. This webinar also shares resources to support qualitative methods and an activity for participants to practice analyzing qualitative data.

Strengthening SSIP Evaluations with Qualitative Methods
This report provides a broad overview of what qualitative research methods are, as well as examples and suggestions for collection high-quality qualitative data. Additionally, the report offers examples of two states using qualitative methods for their SSIP evaluation.

Reflection / Practice Activities

To be added once developed