education-data-context

Explain data provenance, missing value codes, and variable definitions for Urban Institute Education Data Portal datasets.

226|35|Updated Feb 7, 2026
One-click install
npx skills add https://github.com/DAAF-Contribution-Community/daaf --skill education-data-context
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: education-data-context
Source: https://github.com/DAAF-Contribution-Community/daaf/tree/main/.claude/skills/education-data-context
Command: npx skills add https://github.com/DAAF-Contribution-Community/daaf --skill education-data-context

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps data analysts understand the nuances, limitations, and proper interpretation of datasets from the Urban Institute Education Data Portal, preventing common analytical errors.

Core Features & Use Cases

  • Data Provenance: Provides context on data sources, collection methods, and potential biases.
  • Interpretation Guidance: Explains missing value codes, variable definitions, and year conventions.
  • Use Case: After downloading K-12 enrollment data (CCD), use this Skill to understand why certain grade values are negative and how to correctly calculate total enrollment, avoiding common pitfalls.

Quick Start

Use the education-data-context skill to understand the limitations of CCD data after pulling it.

Frequently Asked Questions about education-data-context

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I interpret missing values and negative numbers in Urban Institute education data?

Education data context explains missing value codes and variable definitions for Urban Institute datasets, where negative grade values often indicate suppressed data rather than actual counts, preventing common analytical errors.

What is the correct way to calculate total enrollment using CCD data?

Calculating total K-12 enrollment using CCD data requires applying proper data provenance context and year conventions to avoid common pitfalls like misinterpreting negative grade values or misaligning reporting periods.

Why do IPEDS and CRDC datasets have different year conventions?

IPEDS and CRDC datasets use different year conventions due to varying collection methods and data provenance, requiring specific interpretation guidance to align reporting periods correctly across multiple education data sources.

Can I use this education data context guidance for higher education datasets like IPEDS?

Yes, this education data context provides interpretation guidance and caveats for higher education data sources like IPEDS, addressing data limitations, variable definitions, and common analytical errors to ensure valid analysis.

What are the limitations of analyzing K-12 data from the Urban Institute Education Data Portal?

Limitations of analyzing K-12 data from the Urban Institute Education Data Portal include potential collection biases, specific year conventions, and unique missing value codes that require careful contextual interpretation to avoid analytical errors.