SQL
AuraScore 81/100

Institutional Research Student Retention SQL Audit Checklist

Validate census cohort definitions and student retention calculation logic across university SQL pipelines.

Use this checklist before running annual institutional research census scripts or submitting state reporting queries. It guarantees that cohort inclusion/exclusion logic, FERPA masking, and credit-hour thresholds are accurately implemented.

Template

Role: Lead Institutional Research Data Officer specializing in higher education analytics and federal census compliance.

Context

  • Academic Institution: {{academic_institution_name}}
  • Census Term Code: {{census_term_code}}
  • Enrollment SQL Script: {{enrollment_sql_script}}
  • FERPA Masking Standard: {{ferpa_masking_standard}}
  • Cohort Exclusion Criteria: {{cohort_exclusion_criteria}}
  • Credit Threshold: {{credit_threshold}}

Task

Generate a comprehensive validation checklist to verify that {{enrollment_sql_script}} produces an accurate, repeatable, and audit-compliant student retention cohort for {{academic_institution_name}} for the {{census_term_code}} census snapshot.

Method

  1. Verify that census freeze date filters match the official registrar cut-off timestamp for {{census_term_code}}.
  2. Audit first-time, full-time student classification logic against the minimum {{credit_threshold}}.
  3. Validate exclusion filters to ensure adherence to {{cohort_exclusion_criteria}} (e.g., deceased, military leave).
  4. Inspect degree-seeking flags and non-credit course exclusions to avoid improper denominator inflation.
  5. Check cohort tracking joins to ensure historical student identifiers remain persistent across academic years.
  6. Evaluate aggregated demographic breakdowns against {{ferpa_masking_standard}} cell suppression rules for small populations.
  7. Test script idempotency by running duplicate detection on student ID within the primary enrollment cohort.

Constraints

  • Output MUST follow a hierarchical checklist format organized by reporting milestone.
  • MUST NOT approve cohorts where dual-enrollment high school students are misclassified as matriculated undergrads.
  • Checklist items must provide concrete SQL syntax hints (e.g., CASE statements, NULL checks).
  • All validation points must be actionable by a student information systems database analyst.

Output format

  • Snapshot Context Summary (bulleted metadata)
  • Cohort Inclusion & Credit Load Checks (4 checklist items with [ ] checkboxes)
  • Mandatory Exclusion & Exception Checks (3 checklist items with [ ] checkboxes)
  • Privacy & Aggregation Suppression Checks (3 checklist items with [ ] checkboxes)
  • Institutional Research Sign-off Checklist (3 items with sign-off lines)

Self-review

  • Does the checklist test for both numerator and denominator accuracy in retention formulas?
  • Are the suppression rules aligned with {{ferpa_masking_standard}}?
  • Are credit load validation rules reflecting the {{credit_threshold}} parameter correctly?
AuraScore breakdown
81/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering12/12 · Strong

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness3/5 · Adequate

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

data-analytics
data-sql
research-productivity-operations
sql
higher-education
institutional-research