SayPro Data Collection Template: Graduation Rates, Test Scores, and Retention

Data Category	Description	Data Points to Collect	Disaggregation Criteria
Institution Name	Name of the educational institution	Name of the school, college, or university	N/A
Academic Year	Year of data collection	Academic year (e.g., 2023-2024, 2024-2025)	N/A
Educational Level	Type of educational institution or grade level	Primary, Secondary, Post-Secondary (College/University), Vocational/Technical Training	N/A
Graduation Rate	Percentage of students who successfully graduated	Number of students who graduated / Total number of students enrolled × 100	By Year, By Demographic Group, By Educational Level
Test Scores	Scores from standardized tests (e.g., SAT, ACT, state assessments)	Average test score per student, percentage of students meeting proficiency benchmarks	By Year, By Demographic Group, By Educational Level
Retention Rate	Percentage of students retained from one year to the next	Number of students retained in the subsequent year / Total number of students at the start of the year × 100	By Year, By Demographic Group, By Educational Level
Demographic Group	Groupings based on key demographic indicators	Gender, Race/Ethnicity, Socioeconomic Status (Free/Reduced Lunch, Income Level), Special Education Status	By Year, By Educational Level

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Disaggregation Criteria:

1. By Year: The data should be collected separately for each academic year (e.g., 2023-2024, 2024-2025) to track changes and trends over time.
2. By Demographic Group:
   • Gender: Male, Female, Non-Binary, etc.
   • Race/Ethnicity: White, Black/African American, Hispanic/Latino, Asian, Native American, Pacific Islander, Other.
   • Socioeconomic Status: Low-Income, Middle-Income, High-Income, Free/Reduced Lunch Eligibility.
   • Special Education: Students with disabilities, English Language Learners (ELL), Gifted/Talented.
3. By Educational Level: Separate data for primary, secondary, and post-secondary education, with appropriate categorizations for each level of instruction.

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Example of Data Collection Template:

Institution Name	Academic Year	Educational Level	Graduation Rate	Test Scores (Avg.)	Retention Rate	Demographic Group
Springdale High School	2023-2024	Secondary	85%	1200 (SAT)	90%	Female, White
Springdale High School	2023-2024	Secondary	80%	1180 (SAT)	88%	Male, Black
Brookstone University	2023-2024	Post-Secondary	75%	1500 (SAT)	92%	Hispanic, Low-Income
Brookstone University	2023-2024	Post-Secondary	78%	1450 (SAT)	89%	Asian, High-Income

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Instructions for Data Collection:

1. Collecting Raw Data: We source data from institutional reports, government educational databases, and standardized testing organizations. If certain data points are not publicly available, SayPro will engage with the institutions directly to request the necessary information.
2. Data Entry: For each institution, we enter the collected data under the appropriate columns, ensuring all relevant metrics—graduation rates, test scores, and retention rates—are captured.
3. Data Accuracy: Each data entry is carefully verified and cross-checked to ensure completeness and accuracy. We ensure that the data reflects the most recent and reliable information available.
4. Updating: The data template is updated annually to track trends and to provide an evolving picture of institutional performance. This allows for actionable insights that inform program adjustments or improvements.
5. Disaggregation: All data is broken down according to the demographic groups listed, providing a granular view of performance across different student populations.

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Data Review and Usage:

• The SayPro team uses this template to analyze patterns in graduation rates, test scores, and retention over time. This data helps identify achievement gaps and areas where support may be needed.
• By disaggregating data by demographic groups, we ensure that any disparities—whether by race, socioeconomic status, or other factors—are visible and can be addressed proactively.
• The insights derived from this analysis are shared with stakeholders (e.g., educational institutions, policy makers, community leaders) to drive data-informed decision-making that aims to improve educational outcomes at all levels.