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Views on identity labels and language

Source reportMethodology

Overview

About 52% of adults selected neither Latinx nor country of origin when asked which label they prefer. Roughly 39% selected country of origin, and 9% selected Latinx.

Awareness of Latine was limited. About 68% said they had not heard of the term, while 32% said they had.

Topline

52% selected neither Latinx nor country of origin.

Do you prefer to be labeled as Latinx or by your country of origin?

  • Neither 52.4%
  • Country of origin 38.9%
  • Latinx 8.7%

2022 · base n 1,163 · +/- 3.0%

Verasight National AAPOR Omnibus Survey

View source data

Neither label was the most selected answer

About 52% selected neither Latinx nor country of origin as their preferred label. Country of origin was selected by 39%.

Latinx was the least selected of the three offered answers. Roughly 9% of adults selected it.

Headline

68% said they had not heard of Latine.

Have you heard of the term Latine?

68.1% no

2022 · base n 1,163 · +/- 3.0%

Verasight National AAPOR Omnibus Survey

View source data

Most adults had not heard of Latine

About 68% of adults said they had not heard of the term Latine. Roughly 32% said they had heard of it.

That result keeps the label-preference finding in context: familiarity with the related term was limited.

Topline

25% selected completely feminine, and 25% selected completely masculine.

Would you consider yourself to be...

  • Completely Feminine 25.4%
  • Completely Masculine 24.8%
  • Mostly Masculine 18.6%
  • Mostly Feminine 16.8%
  • Slightly Masculine 8.1%
  • Slightly Feminine 6.2%

2022 · base n 1,163 · +/- 3.0%

Verasight National AAPOR Omnibus Survey

View source data

Gender self-description was split across endpoints

The largest gender self-description responses were close. About 25% selected completely feminine, and 25% selected completely masculine.

Middle categories were smaller: 19% selected mostly masculine, 17% selected mostly feminine, 8% selected slightly masculine, and 6% selected slightly feminine.

Methodology

Full methodology
Mode
Verasight panel recruited via random address-based sampling, random person-to-person text messaging, and dynamic online targeting
Population
US adults age 18+
Field dates
2022-05-28 → 2022-06-01
Base (unweighted)
1,163
Margin of error
+/- 3.0%
Module
2022 Verasight National AAPOR Omnibus Survey
Sponsor
Verasight
Weight variable
weight
Weighting targets
age, race/ethnicity, sex, income, education, region, metropolitan status

Sources

[3]
  • 01
    Do you prefer to be labeled as Latinx or by your country of origin?Anchors the topic in label preference.reports.verasight.io/reports/2022-verasight-national-aapor-omnibus-survey
  • 02
    Have you heard of the term Latine?Adds awareness of another identity term.reports.verasight.io/reports/2022-verasight-national-aapor-omnibus-survey
  • 03
    Would you consider yourself to be...Adds a separate gender self-description measure.reports.verasight.io/reports/2022-verasight-national-aapor-omnibus-survey

Citation

2022 Verasight National AAPOR Omnibus Survey, fielded May 28-June 1, 2022, N=1,163 US adults age 18+, +/- 3.0%.

https://reports.verasight.io/reports/2022-verasight-national-aapor-omnibus-survey#do-you-prefer-to-be-labeled-as-latinx-or-by-your-country-of-origin

Verasight survey methodology

How Verasight conducts surveys.

This page describes the Verasight general survey contract, separate from how the Data Library packages it. Each wave's specific field dates, sample sizes, and module breakdown are listed in that wave's report.

Mode
Verasight panel recruited via random address-based sampling, random person-to-person text messaging, and dynamic online targeting.
Population
US adults age 18+.
Sample design
Surveys are run as omnibus or single-topic waves. Omnibus waves are split into modules with their own respondent set, typically around one thousand respondents per module.
Field window
Each wave specifies its own field dates. Most omnibus waves field across roughly two weeks.
Weighting
Per-module weighting to CPS targets including age, race and ethnicity, sex, income, education, region, and metropolitan status.
Partisanship benchmark
Pew Research Center's NPORS benchmarking surveys, three-year running average.
Vote benchmark
2024 presidential vote population benchmarks.
Margin of error
Typically about plus or minus 3.4 to 3.6 percent per module at standard module sizes. Question-level MoE is recomputed when a base shrinks materially below the module baseline.
Reporting
Every wave is published as a standalone report at verasight.io/reports with full instrument and methodology.
Transparency
AAPOR transparency standards.

Wave-specific methodology, full weighting variable lists, and verbatim instrument text live in each report at verasight.io/reports.