Overview
This guide provides onboarding information for new clients and details about what data will be requested throughout the school year.
New Client Onboarding
Below are detailed explanations of the data files ECRA Group will need to onboard your district. For any files containing student information, your district can securely send data to ECRA using the following guide:
How to Securely Transfer Data to ECRA.
Assessment Calendars
Include a list of all assessments administered in your district, specifying the grade levels and approximate testing windows.
Student Rosters
Submit both current and historical rosters. These should ideally be in your state’s standard raw data file format (e.g., the Illinois SIS file format found here).
Assessment Data
Provide raw historical assessment data from the past 4–5 years. These files should be exported directly from the test publisher’s platform whenever possible.
Tips for Efficient and Accurate Data Submission
Preferred Method: Direct Access to Publisher Portals
Granting ECRA access to your test publisher portals is the most efficient and preferred method for retrieving data. Step-by-step setup guides for common test publishers are available in the links on the left.
Alternative Method: Uploading Data Files to ECRISS
If you prefer to download raw data files and send them to ECRA, you can upload files to a secure data transfer folder on the ECRISS platform (see “How to safely and quickly transfer district data to ECRA for analysis” link on the left). When transferring or uploading data to the transfer folder, make sure file titles are clear and contain the correct term or year.
Use Raw, Unmodified Files
When sending data to ECRA, the raw data file that is exported directly from the publisher is always preferred. Instructions for standardized exports are provided for many testing publishers in the links on the left.
Maintain Consistent Student IDs
Consistency in student IDs across all rosters and assessment files is essential to ensure accurate matching and longitudinal tracking of student data.