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1.2. VIQ Import Notes
Choosing the best matching criteria
API_ID is the preferred matching criterion, since this is a unique identifier applied to every voter in the VLO database. However, if this field is not in your file, you'll need to select other matching criteria.
1) 'firstname','lastname', 'voter_id'. - the VOTER_ID field is a state-applied analogue of the API_ID field. If this is included, a high match rate can be obtained by using this field along with first and last name matching. However, don't expect a perfect match rate. API_ID is the only reliable way to achieve a 100% match.
2) 'firstname', 'lastname', 'address', 'city'. - If API_ID or VOTER_ID aren't available, then you'll need to search by contact information. Using ADDRESS and CITY fields alongside first and last name matching will let you search by registered home address.
3) 'firstname', 'lastname', 'maddress', 'mcity'. - If you have a large number of PO Boxes in your file you might want to match by MADDRESS and MCITY instead of ADDRESS and CITY. This will search by mailing address rather than home address.
Ensuring the quality of your data
We strongly recommend ensuring the quality of your file before importing. There are three general steps you can take that should improve your percentage of matches when you are importing a file that doesn't include API IDs.
1) Voters are listed in the VLO database by the name they used when registering to vote. Nicknames, middle names, and any other names besides their registered name won't match. Check whether you need to update the 'firstname' field to hold first names instead of nicknames (e.g., change 'Bill' to 'William' and 'Dave' to 'David'). Also, check for obvious spelling errors (e.g., 'Ramdolph' should be 'Randolph').
2) Include addresses where they are absent. If you can't get an address for that individual, remove them. For instance, there are twenty-six individuals named David Horn in NY. If you can't find an address for David Horn and attempt to match on name alone you are almost guaranteed to match to the wrong individual.
3) Remove any rows that don't contain data. For instance, remove any rows marked "Test".