Data Analysis for PolicyMaking in Georgia
Module 2 · Reading Voting & Registration Data › 2.5 Module 2 checkpoint
Subsection 2.5

Module 2 checkpoint

~3 min

Module 2 Checkpoint

You have completed Module 2: Reading Voting & Registration Data. These four questions cover the voter file schema, turnout calculation, and data quality.

Module 2 Recap — Voter File Key Concepts Reading the Voter File • VRN is the primary key — always use for joins • Party = primary participation history, not affiliation • Voting history flags: binary (voted / did not vote) • Race field: ~15–20% "Unknown" — not random • Filter to Status = Active before counting Turnout & Quality • Turnout = ballots cast ÷ registered voters × 100 • Low turnout = signal, not explanation • GA county code ≠ FIPS code — use a crosswalk • Missing reg. dates → exclude from recency analysis • Post-redistricting: check for precinct assignment lag
Diagram 2.5 · Module 2 recap. Key voter file concepts and data quality rules.

What is the primary key used to uniquely identify a voter in Georgia's voter file?

Correct! The Voter Registration Number (VRN) is the unique identifier. Name and address both change and have quality issues — always join on VRN when possible. Not quite. The VRN is the unique key. Names change, addresses change — always join on VRN.

If a Georgia precinct has 4,000 registered voters and 2,200 ballots were cast, what is the turnout rate?

Correct! Turnout rate = ballots cast ÷ registered voters × 100. 2,200 ÷ 4,000 = 0.55 = 55.0%. Not quite. Turnout rate = 2,200 ÷ 4,000 = 55.0%.

What does it mean when approximately 15–20% of Georgia voter records have race/ethnicity coded as 'Unknown'?

Correct! Unknown race coding is not random — it correlates with registration method and county. Any racial analysis must account for how Unknown records are distributed before drawing conclusions. Not quite. Unknown is not random — it correlates with county and registration method. You cannot ignore these rows without first checking whether they cluster in ways that affect your analysis.

After a redistricting cycle, you notice that many voters in your dataset still show precinct assignments that match the old district map. What is the most likely cause?

Correct! Precinct assignment lag is common in the months after redistricting. The voter file database takes time to catch up to official boundary changes — you should cross-check against official shapefiles. Not quite. Precinct assignment lag is the most likely cause. Cross-check against official boundary shapefiles.

Action: Complete all four questions, then slide to finish Module 2.