# Profile variables codebook

> The 12 Verasight profile variables included in every self-serve dataset, with question wording, values, who is asked, and use in weighting.

Source: https://www.verasight.io/docs/data-and-methods/profile-variables

Every Verasight Self-Serve sample includes our 12 standard demographic profile variables, whatever the sample type or size. All Verasight panelists answer these questions when they join the panel, and we use the answers for sampling and screening. You do not need to ask these questions in your survey. You can add any other demographic questions you need.

Each variable is a column in [your data file](https://www.verasight.io/docs/data-and-methods/your-data-file#what-is-in-your-data-file). Self-Serve describes 11 demographic variables because partisanship counts as one item. In your data file, partisanship is two columns, `pid_base` and `pid_lean`, so the file has 12 profile variable columns.

## Download the codebook

Download the codebook as a comma-separated values file: [verasight-core-profile-variables-2026-11.csv](https://www.verasight.io/docs/files/verasight-core-profile-variables-2026-11.csv). The file has one row per variable, with the same fields as each variable entry.

## Variable index

| Variable | Label | Source | Used in weighting |
|---|---|---|---|
| [`gender`](https://www.verasight.io/docs/data-and-methods/profile-variables#gender) | Gender | Self-reported | No |
| [`age_group4`](https://www.verasight.io/docs/data-and-methods/profile-variables#age_group4) | Age | Self-reported | Yes (age) |
| [`education`](https://www.verasight.io/docs/data-and-methods/profile-variables#education) | Education | Self-reported | Yes (education) |
| [`hispanic`](https://www.verasight.io/docs/data-and-methods/profile-variables#hispanic) | Hispanic Ethnicity | Self-reported | Yes (race and ethnicity, with `race`) |
| [`race`](https://www.verasight.io/docs/data-and-methods/profile-variables#race) | Race | Self-reported | Yes (race and ethnicity, with `hispanic`) |
| [`income`](https://www.verasight.io/docs/data-and-methods/profile-variables#income) | Income | Self-reported | Yes (income) |
| [`pid_base`](https://www.verasight.io/docs/data-and-methods/profile-variables#pid_base) | Partisanship | Self-reported | Yes (party identification) |
| [`pid_lean`](https://www.verasight.io/docs/data-and-methods/profile-variables#pid_lean) | Party lean | Self-reported | No |
| [`vote_2024`](https://www.verasight.io/docs/data-and-methods/profile-variables#vote_2024) | 2024 Vote | Self-reported | Yes (2024 vote, with `votechoice_2024`) |
| [`votechoice_2024`](https://www.verasight.io/docs/data-and-methods/profile-variables#votechoice_2024) | 2024 Vote Choice | Self-reported | Yes (2024 vote, with `vote_2024`) |
| [`region`](https://www.verasight.io/docs/data-and-methods/profile-variables#region) | Census Region | Derived from ZIP code | Yes (Census region) |
| [`metro`](https://www.verasight.io/docs/data-and-methods/profile-variables#metro) | Metropolitan Status | Derived from ZIP code | Yes (metropolitan status) |

Used in weighting refers to [Nationally Representative weights](https://www.verasight.io/docs/data-and-methods/survey-weights#weighting-variables). Validated Voters weights use registered-voter benchmarks.

## Variable entries

Each value is numbered with its code in the .dta and .sav files. The .rds file stores the same values as factor levels in the same order. Universe is who the variable covers. Example values are synthetic.

A value can be blank, even where the universe is all respondents. Blanks are empty cells in the .csv file and missing values in the .rds, .dta and .sav files.

### gender

| Field | Value |
|---|---|
| Label | Gender |
| Question | Which best describes you? |
| Universe | All respondents |
| Source | Self-reported |
| Used in weighting | No |
| Example value | Female |

Values:

1. Male
2. Female
3. Other

### age_group4

| Field | Value |
|---|---|
| Label | Age |
| Question | Please enter your age in years: |
| Note | Delivered as four age groups. |
| Universe | All respondents |
| Source | Self-reported |
| Used in weighting | Yes (age) |
| Example value | 30-49 |

Values:

1. 18-29
2. 30-49
3. 50-64
4. 65+

### education

| Field | Value |
|---|---|
| Label | Education |
| Question | What is the highest level of school that you have completed? |
| Universe | All respondents |
| Source | Self-reported |
| Used in weighting | Yes (education) |
| Example value | 4-year or bachelor degree |

Values:

1. Some high school or less
2. High school graduate or GED
3. Some college, no degree
4. 2-year or associate degree
5. 4-year or bachelor degree
6. Post-graduate degree

### hispanic

| Field | Value |
|---|---|
| Label | Hispanic Ethnicity |
| Question | Are you of Hispanic or Latino origin background? |
| Universe | All respondents |
| Source | Self-reported |
| Used in weighting | Yes (race and ethnicity, with `race`) |
| Example value | No |

Values:

1. Yes
2. No

### race

| Field | Value |
|---|---|
| Label | Race |
| Question | Which of these best describes your race? |
| Universe | All respondents |
| Source | Self-reported |
| Used in weighting | Yes (race and ethnicity, with `hispanic`) |
| Example value | White |

Values:

1. Black or African-American
2. Pacific Islander
3. Asian or Asian-American
4. White
5. Native American or American Indian
6. Mixed race
7. Some other race

### income

| Field | Value |
|---|---|
| Label | Income |
| Question | Over the last 12 months, what was your total family income from all sources, before taxes? |
| Universe | All respondents |
| Source | Self-reported |
| Used in weighting | Yes (income) |
| Example value | $50,000 to under $75,000 |

Values:

1. Less than $15,000
2. $15,000 to under $50,000
3. $50,000 to under $75,000
4. $75,000 to under $100,000
5. $100,000 to under $150,000
6. $150,000 to under $200,000
7. More than $200,000

### pid_base

| Field | Value |
|---|---|
| Label | Partisanship |
| Question | Generally speaking, do you think of yourself as a(n): |
| Universe | All respondents |
| Source | Self-reported |
| Used in weighting | Yes (party identification) |
| Example value | Independent |

Values:

1. Democrat
2. Republican
3. Independent
4. Other or none

### pid_lean

| Field | Value |
|---|---|
| Label | Party lean |
| Question | If you had to pick, are your opinions closer to: |
| Universe | Not stated in the Core Profile Items doc. In a delivered file we checked, everyone who answered Independent or Other or none to pid_base had a value, as did a few partisans. |
| Source | Self-reported |
| Used in weighting | No |
| Example value | The Democratic Party |

Values:

1. The Democratic Party
2. The Republican Party
3. Neither

### vote_2024

| Field | Value |
|---|---|
| Label | 2024 Vote |
| Question | Many people were not able to vote in the 2024 election for president between Kamala Harris and Donald Trump. How about you? Were you able to vote, or for some reason were you unable to vote? |
| Note | The two candidate names appear in randomized order. |
| Universe | All respondents |
| Source | Self-reported |
| Used in weighting | Yes (2024 vote, with `votechoice_2024`) |
| Example value | I voted |

Values:

1. I voted
2. I was too young to vote
3. I was not registered to vote
4. I did not vote

### votechoice_2024

| Field | Value |
|---|---|
| Label | 2024 Vote Choice |
| Question | Who did you vote for in the 2024 presidential election? |
| Universe | Respondents who answered "I voted" to vote_2024 |
| Source | Self-reported |
| Used in weighting | Yes (2024 vote, with `vote_2024`) |
| Example value | Kamala Harris |

Values:

1. Kamala Harris
2. Donald Trump
3. Someone else

### region

| Field | Value |
|---|---|
| Label | Census Region |
| Measurement | Census region is determined based on a respondent's ZIP code. |
| Universe | All respondents |
| Source | Derived from ZIP code |
| Used in weighting | Yes (Census region) |
| Example value | Midwest |

Values:

1. Northeast
2. Midwest
3. South
4. West

### metro

| Field | Value |
|---|---|
| Label | Metropolitan Status |
| Measurement | Metropolitan status is determined based on a respondent's ZIP code. |
| Universe | All respondents |
| Source | Derived from ZIP code |
| Used in weighting | Yes (metropolitan status) |
| Example value | Metropolitan |

Values:

1. Non-metro
2. Metropolitan

## Variables to read together

Read these three pairs of variables together:

- **Race and ethnicity:** `race` has no Hispanic category. Hispanic or Latino origin is a separate question, `hispanic`.
- **Party:** `pid_base` records party identification. `pid_lean` records which party a respondent's opinions are closer to.
- **2024 vote:** `vote_2024` records whether a respondent voted in 2024. `votechoice_2024` is asked only of respondents who answered "I voted" to `vote_2024`.

## Geography

Your data file includes Census region and metropolitan status, as `region` and `metro`. Both are derived from each respondent's ZIP code. ZIP code, county, and state are not included in self-serve data.

If you need state or ZIP code and your institutional review board allows it, ask for it in your own survey. For a sample of a single state, see [custom research](https://www.verasight.io/docs/run-your-study/choose-a-sample-type#custom-research).