The first weekly wave of the Democracy Fund + UCLA Nationscape survey, fielded July 18–24, 2019. Approximately 6,250 completed online interviews drawn from the Lucid respondent exchange platform using a non-probability quota design, with raking weights calibrated to ACS demographic targets and 2016 presidential vote choice.
Format
A data frame with approximately 6,250 rows and 171 variables
(170 survey variables plus wave_id added by the prepare script).
- response_id
Unique respondent ID (integer).
- start_date
Interview date (character,
"YYYY-MM-DD"format).- wave_id
Wave identifier:
"ns20190718"for all rows in this dataset.- weight
Raking weight calibrated to ACS demographic targets and 2016 presidential vote choice. Use for all population-level estimates.
- right_track
Country direction:
1= Right direction,2= Wrong track,3= Not sure.- economy_better
Economy outlook:
1= Better,2= Worse,3= Same,4= Not sure.- interest
Political interest (4-pt):
1= Very interested,4= Not at all interested.- registration
Voter registration:
1= Registered,2= Not registered,3= Not eligible.- pres_approval
Trump presidential approval:
1= Strongly approve,2= Somewhat approve,3= Somewhat disapprove,4= Strongly disapprove.- vote_intention
2020 vote intention:
1= Trump,2= Democratic candidate,3= Other,4= Don't plan to vote,5= Not sure.- vote_2016
2016 presidential vote. See labels.
- vote_2016_other_text
Write-in for
vote_2016"other" choice.- consider_trump
Would consider voting for Trump:
1= Yes,2= No.- not_trump
Reason for not considering Trump (open text).
- primary_party
Primary vote party:
1= Democratic,2= Republican,3= Other.- dem_vote_intent
Democratic primary vote intention. See labels.
- dem_vote_intent_TEXT
Write-in for
dem_vote_intent"other".- rank_dems_1
Top-ranked Democratic presidential candidate. See labels.
- rank_dems_2
Second-ranked Democratic candidate. See labels.
- rank_dems_3
Third-ranked Democratic candidate. See labels.
- replace_trump
Wants non-Trump Republican nominee:
1= Yes,2= No,3= Not sure.- house_intent
U.S. House vote intention:
1= Democrat,2= Republican,3= Other,4= Won't vote,5= Not sure.- senate_intent
U.S. Senate vote intention. Same codes as
house_intent.- governor_intent
Governor vote intention. Same codes as
house_intent.- news_sources_facebook
Used social media for political news in past week:
1= Selected,2= Not selected. See"question_preface"attribute for shared question stem. Same coding for allnews_sources_*variables.- news_sources_cnn
Used CNN for political news.
- news_sources_msnbc
Used MSNBC for political news.
- news_sources_fox
Used Fox News for political news.
- news_sources_network
Used network news (ABC/CBS/NBC/PBS).
- news_sources_localtv
Used local TV news.
- news_sources_telemundo
Used Telemundo or Univision.
- news_sources_npr
Used NPR.
- news_sources_amtalk
Used AM talk radio.
- news_sources_new_york_times
Used a national newspaper.
- news_sources_local_newspaper
Used a local newspaper.
- news_sources_other
Used another news source:
1= Selected,2= Not selected.- news_sources_other_TEXT
Write-in for
news_sources_other.- group_favorability_whites
Favorability toward Whites:
1= Very favorable,2= Somewhat favorable,3= Somewhat unfavorable,4= Very unfavorable,5= Not sure. Same coding for allgroup_favorability_*variables.- group_favorability_blacks
Favorability toward Blacks.
- group_favorability_latinos
Favorability toward Latinos.
- group_favorability_asians
Favorability toward Asians.
- group_favorability_christians
Favorability toward Christians.
- group_favorability_socialists
Favorability toward Socialists.
- group_favorability_muslims
Favorability toward Muslims.
- group_favorability_labor_unions
Favorability toward labor unions.
- group_favorability_the_police
Favorability toward the police.
- group_favorability_undocumented
Favorability toward undocumented immigrants.
- group_favorability_lgbt
Favorability toward gays and lesbians.
- group_favorability_republicans
Favorability toward Republicans.
- group_favorability_democrats
Favorability toward Democrats.
- cand_favorability_trump
Favorability toward Donald Trump. Same 5-point scale as
group_favorability_*variables.- cand_favorability_obama
Favorability toward Barack Obama.
- cand_favorability_cortez
Favorability toward Alexandria Ocasio-Cortez.
- cand_favorability_biden
Favorability toward Joe Biden.
- cand_favorability_harris
Favorability toward Kamala Harris.
- cand_favorability_buttigieg
Favorability toward Pete Buttigieg.
- cand_favorability_warren
Favorability toward Elizabeth Warren.
- cand_favorability_sanders
Favorability toward Bernie Sanders.
- cand_favorability_pence
Favorability toward Mike Pence.
- trump_biden
Trump vs. Biden head-to-head:
1= Trump,2= Biden,3= Not sure. Same coding for alltrump_*matchup variables.- trump_sanders
Trump vs. Sanders.
- trump_harris
Trump vs. Harris.
- trump_warren
Trump vs. Warren.
- trump_buttigieg
Trump vs. Buttigieg.
- trump_booker
Trump vs. Cory Booker.
- trump_castro
Trump vs. Julian Castro.
- trump_gabbard
Trump vs. Tulsi Gabbard.
- trump_gillibrand
Trump vs. Kirsten Gillibrand.
- trump_orourke
Trump vs. Beto O'Rourke.
- pence_biden
Pence vs. Biden head-to-head:
1= Pence,2= Biden,3= Not sure. Same coding for allpence_*matchup variables.- pence_buttigieg
Pence vs. Buttigieg.
- pence_harris
Pence vs. Harris.
- pence_sanders
Pence vs. Sanders.
- pence_warren
Pence vs. Warren.
- cand_truth_donald_trump
Whether Donald Trump cares about telling the truth:
1= Yes,2= No,3= Not sure. Same coding for allcand_truth_*variables.- cand_truth_elizabeth_warren
Whether Elizabeth Warren cares about the truth.
- cand_truth_joe_biden
Whether Joe Biden cares about the truth.
- cand_truth_bernie_sanders
Whether Bernie Sanders cares about the truth.
- cand_truth_pete_buttigieg
Whether Pete Buttigieg cares about the truth.
- cand_truth_kamala_harris
Whether Kamala Harris cares about the truth.
- cand_facts_donald_trump
Whether Donald Trump relies on facts vs. hunches:
1= Facts and evidence,2= Hunches,3= Not sure. Same coding for allcand_facts_*variables.- cand_facts_elizabeth_warren
Whether Elizabeth Warren relies on facts.
- cand_facts_joe_biden
Whether Joe Biden relies on facts.
- cand_facts_bernie_sanders
Whether Bernie Sanders relies on facts.
- cand_facts_pete_buttigieg
Whether Pete Buttigieg relies on facts.
- cand_facts_kamala_harris
Whether Kamala Harris relies on facts.
- racial_attitudes_tryhard
Agree/disagree: minorities should work their way up without special favors.
1= Strongly agree,2= Agree,3= Neither,4= Disagree,5= Strongly disagree. Same scale for allracial_attitudes_*andgender_attitudes_*variables.- racial_attitudes_generations
Agree/disagree: generations of slavery make it difficult for Blacks to work out of the lower class.
- racial_attitudes_marry
Agree/disagree: I prefer close relatives marry someone from the same race.
- racial_attitudes_date
Agree/disagree: it's alright for Blacks and Whites to date.
- gender_attitudes_maleboss
Agree/disagree: more comfortable with a male boss than female boss.
- gender_attitudes_logical
Agree/disagree: women are just as capable of thinking logically as men.
- gender_attitudes_opportunity
Agree/disagree: increased opportunities for women have improved quality of life.
- gender_attitudes_complain
Agree/disagree: women who complain about harassment cause more problems than they solve.
- discrimination_blacks
Perceived discrimination against Blacks:
1= A great deal,2= A lot,3= A little,4= None at all,5= Not sure. Same scale for alldiscrimination_*variables.- discrimination_whites
Perceived discrimination against Whites.
- discrimination_muslims
Perceived discrimination against Muslims.
- discrimination_christians
Perceived discrimination against Christians.
- discrimination_women
Perceived discrimination against Women.
- discrimination_men
Perceived discrimination against Men.
- sen_knowledge
U.S. Senate knowledge question. See labels.
- sc_knowledge
U.S. Supreme Court knowledge question. See labels.
- pid3
3-category party ID:
1= Democrat,2= Republican,3= Independent,4= Something else.- pid7_legacy
7-point party ID (legacy coding). See labels.
- strength_democrat
Strength of Democratic ID (conditional on
pid3 == 1). See labels.- strength_republican
Strength of Republican ID (conditional on
pid3 == 2). See labels.- lean_independent
Partisan lean of Independents (conditional on
pid3 == 3). See labels.- ideo5
5-point ideological self-placement:
1= Very liberal,5= Very conservative.- employment
Employment status (selected choice). See labels.
- employment_other_text
Write-in for
employment"other".- foreign_born
Born outside the U.S.:
1= Yes,2= No.- language
Primary language at home. See labels.
- religion
Religious affiliation (selected choice). See labels.
- religion_other_text
Write-in for
religion"other".- is_evangelical
Born-again or evangelical Christian:
1= Yes,2= No.- orientation_group
Sexual orientation. See labels.
- in_union
Labor union membership:
1= Yes,2= No,3= Non-union household,4= Not sure.- household_gun_owner
Household gun ownership:
1= Yes,2= No,3= Not sure.- wall
Support building a wall on the southern U.S. border:
1= Strongly support,2= Somewhat support,3= Somewhat oppose,4= Strongly oppose,5= Not sure. Same scale for all policy items throughlimit_magazines. See"question_preface"attribute on each variable for the exact shared question stem.- cap_carbon
Support capping carbon emissions.
- environment
Support large-scale government investment in environmental technology.
- guns_bg
Support requiring background checks for all gun purchases.
- mctaxes
Support cutting taxes for families making < $100K/year.
- estate_tax
Support eliminating the estate tax.
- raise_upper_tax
Support raising taxes on families making > $600K.
- college
Support ensuring all students can graduate from state colleges debt-free.
- abortion_waiting
Support requiring a waiting period and ultrasound before an abortion.
- abortion_never
Support never permitting abortion.
- abortion_conditions
Support permitting abortion in cases other than rape/incest/life at risk.
- late_term_abortion
Support permitting late-term abortion.
- abortion_insurance
Support allowing employers to decline abortion coverage.
- guaranteed_jobs
Support guaranteeing jobs for all Americans.
- green_new_deal
Support enacting a Green New Deal.
- gun_registry
Support creating a public registry of gun ownership.
- immigration_separation
Support separating children from parents prosecuted for illegal border crossing.
- immigration_system
Support shifting to a merit-based immigration system.
- immigration_wire
Support requiring proof of citizenship to wire money internationally.
- impeach_trump
Support impeaching President Trump.
- israel
Support withdrawing military support for Israel.
- marijuana
Support legalizing marijuana.
- maternityleave
Support requiring 12 weeks of paid maternity leave.
- medicare_for_all
Support Medicare-for-All.
- military_size
Support reducing the size of the U.S. military.
- minwage
Support raising the minimum wage to $15/hour.
- muslimban
Support banning people from predominantly Muslim countries.
- oil_and_gas
Support removing barriers to domestic oil and gas drilling.
- reparations
Support granting reparations to descendants of slaves.
- right_to_work
Support allowing people to work in unionized workplaces without paying union dues.
- ten_commandments
Support displaying the Ten Commandments in public schools and courthouses.
- trade
Support limiting trade with other countries.
- trans_military
Support allowing transgender people to serve in the military.
- uctaxes2
Support raising taxes on families making > $250K.
- vouchers
Support providing tax-funded vouchers for private or religious schools.
- gov_insurance
Support providing government-run health insurance to all Americans.
- public_option
Support providing the option to purchase government-run insurance.
- health_subsidies
Support subsidizing health insurance for lower income people not on Medicaid.
- path_to_citizenship
Support creating a path to citizenship for all undocumented immigrants.
- dreamers
Support a path to citizenship for DREAMers.
- deportation
Support deporting all undocumented immigrants.
- ban_guns
Support banning all guns.
- ban_assault_rifles
Support banning assault rifles.
- limit_magazines
Support limiting gun magazines to 10 bullets.
- age
Respondent age in years.
- gender
Gender:
1= Male,2= Female,3= Other.- census_region
Census region:
1= Northeast,2= Midwest,3= South,4= West.- hispanic
Hispanic or Latino origin:
1= Yes,2= No.- race_ethnicity
Race/ethnicity (6 categories). See labels.
- household_income
Household income (7 brackets). See labels.
- education
Educational attainment (6 categories). See labels.
- state
U.S. state of residence (2-letter abbreviation).
- congress_district
Congressional district.
Source
Democracy Fund Voter Study Group / UCLA. Nationscape Data Set, version
December 2021. https://www.voterstudygroup.org/data/nationscape
(free download; academic research use). Prepared by
data-raw/prepare-nationscape-phase1.R.
For full methodology, see the Nationscape User Guide and the
Representative Assessment report in
data-raw/nationscape/Nationscape-User-Guide-2021Dec.pdf.
Details
This dataset is the first of 77 weekly waves collected from July 2019 through January 2021. The full survey ran in three phases:
| Phase | Weeks | Dates | Approx. N |
| Phase 1 | 1–24 | Jul 18, 2019 – Dec 26, 2019 | 150,000 |
| Phase 2 | 25–50 | Jan 2, 2020 – Jun 25, 2020 | 162,500 |
| Phase 3 | 51–77 | Jul 2, 2020 – Jan 12, 2021 | 168,750 |
Only Wave 1 is bundled in the package because 77 waves × ~6,250 rows
would be prohibitively large. To obtain the full dataset by phase, use the
prepare scripts in data-raw/ (see the Source section).
Survey design:
The Nationscape is a calibrated non-probability sample (quota design with
raking weights). Use as_survey_calibrated() — it is designed specifically
for this use case and will gain bootstrap re-calibration variance in Phase
2.5:
svy <- as_survey_calibrated(ns_wave1, weights = weight)Metadata:
All substantive columns carry variable labels ("label" attribute) set
during data preparation. Battery items additionally carry a
"question_preface" attribute with the shared question stem. Value
labels ("labels" attribute) are present for all coded response items.
Battery structure:
Most multi-item question groups follow a {battery}_{item} naming
convention. All items within a battery share an identical
"question_preface" attribute:
| Battery prefix | Preface summary | N items |
news_sources_* | News sources used in past week | 13 |
group_favorability_* | Favorability toward named groups | 13 |
cand_favorability_* | Favorability toward named candidates | 9 |
trump_* | Trump head-to-head matchups | 10 |
pence_* | Pence head-to-head matchups | 5 |
cand_truth_* | Whether each candidate tells the truth | 6 |
cand_facts_* | Whether each candidate relies on facts | 6 |
racial_attitudes_* | Agree/disagree racial attitude items | 4 |
gender_attitudes_* | Agree/disagree gender attitude items | 4 |
discrimination_* | Perceived discrimination by group | 6 |
Three policy batteries share the same Agree/Disagree/Neither scale:
wall, cap_carbon, environment, guns_bg, mctaxes, estate_tax,
raise_upper_tax, college, abortion_waiting, abortion_never,
abortion_conditions, late_term_abortion, abortion_insurance,
guaranteed_jobs, green_new_deal, gun_registry,
immigration_separation, immigration_system, immigration_wire,
impeach_trump, israel, marijuana, maternityleave,
medicare_for_all, military_size, minwage, muslimban,
oil_and_gas, reparations, right_to_work, ten_commandments,
trade, trans_military, uctaxes2, vouchers, gov_insurance,
public_option, health_subsidies, path_to_citizenship, dreamers,
deportation, ban_guns, ban_assault_rifles, limit_magazines.
References
Tausanovitch, Chris and Lynn Vavreck. 2021. Democracy Fund + UCLA Nationscape, October 10–17, 2019 (version 20210301). Retrieved from voterstudygroup.org/data/nationscape.
Rivers, Douglas and Delia Bailey. 2009. "Inference from matched samples in the 2008 U.S. national elections." Proceedings of the Joint Statistical Meetings, Social Statistics Section.
Examples
# Design variables
head(ns_wave1[, c("response_id", "weight", "age", "gender")])
#> # A tibble: 6 × 4
#> response_id weight age gender
#> <chr> <dbl> <dbl> <dbl>
#> 1 00100002 1.75 37 1
#> 2 00100003 0.144 45 2
#> 3 00100004 0.213 24 1
#> 4 00100005 0.0506 26 1
#> 5 00100007 0.137 60 1
#> 6 00100008 0.0653 55 2
# Inspect a battery item's metadata
attr(ns_wave1$group_favorability_blacks, "label")
#> [1] "Blacks"
attr(ns_wave1$group_favorability_blacks, "question_preface")
#> [1] "Here are the names of some groups that are in the news from time to time. How favorable is your impression of each?"
attr(ns_wave1$news_sources_cnn, "labels")
#> Yes No
#> 1 2
# Create a calibrated survey design (correct approach for raked non-prob samples)
svy <- as_survey_calibrated(ns_wave1, weights = weight)
get_freqs(svy, pres_approval)
#> # A tibble: 5 × 3
#> pres_approval pct n
#> <fct> <dbl> <int>
#> 1 Strongly approve 0.184 1222
#> 2 Somewhat approve 0.206 1295
#> 3 Somewhat disapprove 0.152 871
#> 4 Strongly disapprove 0.415 2799
#> 5 Not sure 0.0445 230
# Party identification distribution
table(ns_wave1$pid3)
#>
#> 1 2 3 4
#> 2291 1819 1868 437