Broken Social Contract
Study 1: Analysis Report
Background
This is a preregistered nationally representative study. See
preregistration.
This is joint work with
Prof. Sandra Matz,
led by me as the Principal Investigator.
Description
In this project, we examine the role of the psychological experience of a broken social contract in people’s trust in institutions and anti-establishment sentiment. To measure this, we identify gaps between what people believe they are promised by the state on paper (i.e., the constitution) and what they are being provided by the government in practice. Controlling for a wide range of potential confounds, we then examine the explanatory role this gap plays in various socio-political behavioral and attitudinal outcomes; broadly defined as political discontent.
Hypotheses
- The psychological experience of a broken social contract is
negatively associated with the likelihood of voting in the 2024
Presidential Election.
- The psychological experience of a broken social contract is
positively associated with support for radical change.
- The psychological experience of a broken social contract is
positively associated with anti-establishment sentiment.
- The psychological experience of a broken social contract is
negatively associated with trust in democratic governmental institutions
(the executive, legislative, and judicial branches of government).
- The psychological experience of a broken social contract is negatively associated with trust in mainstream non-political institutions (media, education, police, military, finance, medicine).
Study design
Two major parts:
1. Broken Social Contract: Participants listed what
they believe are the top five guiding values for the US on paper and
what they believe are the top five guiding values for the US in
practice. For each of these perspectives, they assigned weights to each
value based on its perceived importance to the US on paper and in
practice, respectively.
2. Attitudes and individual differences: Participants
completed measures of anti-establishment sentiment, trust in
institutions, support for radical change, voting intentions, Social
Dominance Orientation (SDO), big five personality traits (i.e.,
openness, conscientiousness, extroversion, agreeableness, and
neuroticism), political ideology and identification, and demographic
information.
Computational analytic strategy
We retrieved embeddings for each of the open-response guiding values
inputted by participants, five for the US on paper and five for the US
in practice. The embeddings were retrieved from OpenAI’s API, using the
text-embedding-3-large
model. This gave us a 3,072-dimensional vector representing the phrase’s
meaning in a large multi-dimensional semantic space.
Using participant-assigned weights, we then created weighted means for
each perspective (US on paper and US in practice). This resulted in two
3,072-dimensional vectors, representing America’s promise and America’s
policies in practice, as perceived by a single participant.
To compare the two vectors, we calculated their cosine similarity. So,
higher scores mean that America’s policies resemble its promise, whereas
lower scores mean that its policies do NOT resemble its promise.
Finally, to arrive at a broken social contract score, we
reverse-score the cosine similarity by subtracting it from 1.
Analysis plan
The following linear models will be conducted for each of these
outcome variables: (1) Likelihood to vote in the 2024 Presidential
election; (2) support for radical change; (3) anti-establishment
sentiment; (4) trust in democratic institutions; (5) trust in mainstream
societal institutions.
1. Linear Model 1: Broken contract score as predictor; conservatism as
control.
2. Linear Model 2: Broken contract score as predictor; conservatism,
SDO, and agreeableness as controls.
3. Linear Model 3: Broken contract score as predictor; conservatism,
SDO, agreeableness, gender, race, ethnicity, income, education, and age
as controls.
4. Linear Model 4: Broken contract score as predictor; conservatism,
SDO, agreeableness, gender, race, ethnicity, income, education, age,
county mediation income, county GINI coefficient, and county density as
controls.
Data collection
We collected data on 3/27/2024-3/28/2024 through Connect by
CloudResearch; an online participant recruitment platform. The sample
was intended to be representative of the U.S. population in March 2024
along the lines of gender (~50.4% women ~49.6% men), race (~58.9%
non-Hispanic White, ~18.2% Hispanic or Latino, ~13.6% Black, ~6.3%
Asian, and ~3% more than one race or other), age (~20.1% 18-29 years,
~25.9% 30-44 years, ~23.3% 45-59 years, and ~30.7% 60-99 years), and
political party affiliation (41% Independents, 30% Republicans, and 28%
Democrats). The target sample was set at 1,200 participants.
We excluded participants who failed attention checks: one simple
attention check embedded in the anti-establishment scale and another
with incoherent open-text. Let’s see how many eligible participants we
end up with:
n_eligible <- df_bsc %>%
group_by(is_elg) %>%
summarise(N = n()) %>%
ungroup() %>%
filter(is_elg == 1) %>%
select(N) %>%
unlist() %>%
unname()
df_bsc %>%
group_by(is_elg) %>%
summarise(N = n()) %>%
ungroup() %>%
mutate(Perc = round(100*(N/sum(N)),2)) %>%
ungroup() %>%
kbl() %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| is_elg | N | Perc |
|---|---|---|
| 0 | 17 | 1.41 |
| 1 | 1188 | 98.59 |
Great. That leaves us with 1188 eligible participants.
Demographics
Race and ethnicity
df_bsc_elg %>%
mutate(race = ifelse(is.na(race),"Other (please specify)",race)) %>%
mutate(ethnicity = ifelse(is.na(hispanic),"Non-Hisapnic",
ifelse(hispanic == 1,"Hispanic","Non-Hispanic"))) %>%
group_by(race,ethnicity) %>%
summarise(N = n()) %>%
ungroup() %>%
mutate(Perc = round(100*(N/sum(N)),2)) %>%
ungroup() %>%
arrange(desc(Perc)) %>%
kbl() %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| race | ethnicity | N | Perc |
|---|---|---|---|
| White | Non-Hispanic | 760 | 63.97 |
| Black or African American | Non-Hispanic | 159 | 13.38 |
| Other (please specify) | Hispanic | 92 | 7.74 |
| White | Hispanic | 73 | 6.14 |
| Asian | Non-Hispanic | 53 | 4.46 |
| multiracial | Non-Hispanic | 32 | 2.69 |
| Other (please specify) | Non-Hispanic | 8 | 0.67 |
| multiracial | Hispanic | 4 | 0.34 |
| Black or African American | Hispanic | 2 | 0.17 |
| American Indian or Alaska Native | Non-Hispanic | 1 | 0.08 |
| Asian | Hispanic | 1 | 0.08 |
| Middle Eastern or North African | Hispanic | 1 | 0.08 |
| Middle Eastern or North African | Non-Hispanic | 1 | 0.08 |
| Native Hawaiian or Other Pacific Islander | Non-Hispanic | 1 | 0.08 |
Gender
df_bsc_elg %>%
mutate(gender = ifelse(is.na(gender) | gender == "","other",gender)) %>%
group_by(gender) %>%
summarise(N = n()) %>%
ungroup() %>%
mutate(Perc = round(100*(N/sum(N)),2)) %>%
ungroup() %>%
arrange(desc(Perc)) %>%
kbl() %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| gender | N | Perc |
|---|---|---|
| man | 595 | 50.08 |
| woman | 592 | 49.83 |
| other | 1 | 0.08 |
Age
df_bsc_elg %>%
summarise(age_mean = round(mean(age,na.rm = T),2),
age_sd = round(sd(age,na.rm = T),2)) %>%
kbl() %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| age_mean | age_sd |
|---|---|
| 45.35 | 15.85 |
Education
df_bsc_elg %>%
group_by(edu) %>%
summarise(N = n()) %>%
ungroup() %>%
mutate(Perc = round(100*(N/sum(N)),2)) %>%
ungroup() %>%
kbl() %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| edu | N | Perc |
|---|---|---|
| noHS | 4 | 0.34 |
| GED | 310 | 26.09 |
| 2yearColl | 151 | 12.71 |
| 4yearColl | 517 | 43.52 |
| MA | 146 | 12.29 |
| PHD | 57 | 4.80 |
| NA | 3 | 0.25 |
Income
df_bsc_elg %>%
ggplot(aes(x = income)) +
geom_bar() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_blank(),
axis.title.y = element_blank()) +
coord_flip()
County inequallity
County GINI coefficient was taken from the US Census Website.
df_bsc_elg %>%
filter(!is.na(county_gini)) %>%
ggplot(aes(x = county_gini)) +
geom_histogram(fill = "lightblue",
binwidth = 0.005) +
#scale_x_continuous(breaks = seq(0,1,0.1)) +
ylab("count") +
geom_vline(xintercept = mean(df_bsc_elg$county_gini,na.rm = T),
color = "black",
linetype = "dashed",
size = 1.1) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_text(color = "black",
face = "bold"))
County median income
County median income was taken from the US Census Website.
df_bsc_elg %>%
filter(!is.na(county_medianincome)) %>%
ggplot(aes(x = county_medianincome)) +
geom_histogram(fill = "lightblue",
binwidth = 5000) +
#scale_x_continuous(breaks = seq(0,1,0.1)) +
ylab("count") +
geom_vline(xintercept = mean(df_bsc_elg$county_medianincome,na.rm = T),
color = "black",
linetype = "dashed",
size = 1.1) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_text(color = "black",
face = "bold"))
County density
County density was taken from the US Census Website.
df_bsc_elg %>%
filter(!is.na(county_density)) %>%
ggplot(aes(x = county_density)) +
geom_histogram(fill = "lightblue",
binwidth = 500) +
#scale_x_continuous(breaks = seq(0,1,0.1)) +
ylab("count") +
geom_vline(xintercept = mean(df_bsc_elg$county_density,na.rm = T),
color = "black",
linetype = "dashed",
size = 1.1) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_text(color = "black",
face = "bold"))
Politics
Ideology
Participants were asked about the extent to which they subscribe to the following ideologies on a scale of 1-7 (select NA if unfamiliar): Conservatism, Liberalism, Democratic Socialism, Libertarianism, Progressivism.
means <- df_bsc_elg %>%
dplyr::select(PID,ideo_con:ideo_prog) %>%
pivot_longer(-PID,
names_to = "ideo",
values_to = "score") %>%
filter(!is.na(score)) %>%
group_by(ideo) %>%
summarise(score = mean(score)) %>%
ungroup()
df_bsc_elg %>%
dplyr::select(PID,ideo_con:ideo_prog) %>%
pivot_longer(-PID,
names_to = "ideo",
values_to = "score") %>%
filter(!is.na(score)) %>%
ggplot() +
geom_density(aes(x = score), fill = "lightblue") +
scale_x_continuous(limits = c(1,7),
breaks = seq(1,7,1)) +
geom_vline(data = means,mapping = aes(xintercept = score),
color = "black",
linetype = "dashed",
size = 1.1) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold")) +
facet_wrap(~ideo,nrow = 2)
Party ID
df_bsc_elg %>%
group_by(party_id) %>%
summarise(N = n()) %>%
ungroup() %>%
mutate(Perc = round(100*(N/sum(N)),2)) %>%
ungroup() %>%
kbl() %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| party_id | N | Perc |
|---|---|---|
| Democrat | 401 | 33.75 |
| Independent | 401 | 33.75 |
| Republican | 386 | 32.49 |
Vote in 2020
df_bsc_elg %>%
group_by(vote_2020) %>%
summarise(N = n()) %>%
ungroup() %>%
mutate(Perc = round(100*(N/sum(N)),2)) %>%
ungroup() %>%
arrange(desc(N)) %>%
kbl() %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| vote_2020 | N | Perc |
|---|---|---|
| Joe Biden | 551 | 46.38 |
| Donald Trump | 411 | 34.60 |
| I did not vote | 176 | 14.81 |
| Third-party candidate | 50 | 4.21 |
Vote in 2024
df_bsc_elg %>%
group_by(vote_2024) %>%
summarise(N = n()) %>%
ungroup() %>%
mutate(Perc = round(100*(N/sum(N)),2)) %>%
ungroup() %>%
arrange(desc(N)) %>%
kbl() %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| vote_2024 | N | Perc |
|---|---|---|
| Joe Biden | 507 | 42.68 |
| Donald Trump | 452 | 38.05 |
| Robert F. Kennedy Jr. | 102 | 8.59 |
| Other | 83 | 6.99 |
| Cornel West | 23 | 1.94 |
| Jill Stein | 20 | 1.68 |
| NA | 1 | 0.08 |
Political Discontent Measures
Anti-establishment sentiment
- The US’s economy is rigged to advantage the rich and powerful
- Traditional politicians and parties don’t care about people like
me
- Experts in this country don’t understand the lives of people like
me
- Most of the time we can trust people in the government to do what is
right [R]
Cronbach’s alpha = 0.75
df_bsc_elg %>%
ggplot(aes(x = antiest)) +
geom_density(fill = "lightblue",
color = NA) +
scale_x_continuous(breaks = seq(1,7,1),
limits = c(1,7)) +
ylab("density") +
geom_vline(xintercept = mean(df_bsc_elg$antiest,na.rm = T),
color = "black",
linetype = "dashed",
size = 1.1) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_text(color = "black",
face = "bold"))
Support for radical change
To what extent do you agree with the following statement?
The way this country works needs to be radically changed
df_bsc_elg %>%
ggplot(aes(x = change)) +
geom_histogram(fill = "lightblue",
binwidth = 1) +
scale_x_continuous(breaks = seq(1,7,1),
limits = c(0,8)) +
ylab("count") +
geom_vline(xintercept = mean(df_bsc_elg$change,na.rm = T),
color = "black",
linetype = "dashed",
size = 1.1) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_text(color = "black",
face = "bold"))
Trust in democratic political instituions
Please indicate how much you trust or distrust the following
institutions (1 = Strongly Distrust to 7 = Strongly
Trust)
1. The US Congress / Legislative Branch
2. The US Government / Executive Branch
3. The US Courts / Judicial Branch
Cronbach’s alpha = 0.84
df_bsc_elg %>%
ggplot(aes(x = trust_deminst)) +
geom_density(fill = "lightblue",
color = NA) +
scale_x_continuous(breaks = seq(1,7,1),
limits = c(1,7)) +
ylab("density") +
geom_vline(xintercept = mean(df_bsc_elg$trust_deminst,na.rm = T),
color = "black",
linetype = "dashed",
size = 1.1) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_text(color = "black",
face = "bold"))
Trust in non-political mainstream institutions
Please indicate how much you trust or distrust the following
institutions (1 = Strongly Distrust to 7 = Strongly
Trust)
1. Mainstream media in the US (e.g., CNN, FOX News, MSNBC, New York
Times, Wall-Street Journal, USA Today)
2. The education system in the US
3. Law enforcement / police in the US
4. The US Military
5. Financial institutions in the US (e.g., Wall Street, The Fed, The Big
Banks)
6. The medical system in the US
Cronbach’s alpha = 0.81
df_bsc_elg %>%
ggplot(aes(x = trust_natinst)) +
geom_density(fill = "lightblue",
color = NA) +
scale_x_continuous(breaks = seq(1,7,1),
limits = c(1,7)) +
ylab("density") +
geom_vline(xintercept = mean(df_bsc_elg$trust_natinst,na.rm = T),
color = "black",
linetype = "dashed",
size = 1.1) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_text(color = "black",
face = "bold"))
Trust in science
Please indicate how much you agree or disagree with the following
statements (1 = Strongly Disagree to 7 = Strongly
Agree)
1. I generally trust the recommendations of scientists
2. Scientific institutions generate objective knowledge
3. I look to the social sciences for answers to social problems
Cronbach’s alpha = 0.86
df_bsc_elg %>%
ggplot(aes(x = trust_science)) +
geom_density(fill = "lightblue",
color = NA) +
scale_x_continuous(breaks = seq(1,7,1),
limits = c(1,7)) +
ylab("density") +
geom_vline(xintercept = mean(df_bsc_elg$trust_science,na.rm = T),
color = "black",
linetype = "dashed",
size = 1.1) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_text(color = "black",
face = "bold"))
Turnout for Presidential Election
What is the likelihood that you will vote in the 2024 Presidential Elections? 1 = Not at All Likely to 5 = Extremely Likely
df_bsc_elg %>%
ggplot(aes(x = vote_likely)) +
geom_histogram(fill = "lightblue",
binwidth = 1) +
scale_x_continuous(breaks = seq(1,5,1),
limits = c(0,6)) +
ylab("count") +
geom_vline(xintercept = mean(df_bsc_elg$vote_likely,na.rm = T),
color = "black",
linetype = "dashed",
size = 1.1) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_text(color = "black",
face = "bold"))
Control variables
Social Dominance Orientation
- An ideal society requires some groups to be on top and others to be
on the bottom
- Some groups of people are simply inferior to other groups
- No one group should dominate in society [R]
- Groups at the bottom are just as deserving as groups at the top
[R]
- Group equality should not be our primary goal
- It is unjust to try to make groups equal
- We should do what we can to equalize conditions for different groups
[R]
- We should work to give all groups an equal chance to succeed
[R]
Cronbach’s alpha = 0.9
df_bsc_elg %>%
ggplot(aes(x = SDO)) +
geom_density(fill = "lightblue",
color = NA) +
scale_x_continuous(breaks = seq(1,7,1),
limits = c(1,7)) +
ylab("density") +
geom_vline(xintercept = mean(df_bsc_elg$SDO,na.rm = T),
color = "grey15",
size = 1,
linetype = "dashed") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_text(color = "black",
face = "bold"))
Ten-Iten Personality Inventory
I see myself as… (1 = Strongly Disagree to 7 = Strongly Agree)
- Extraverted, enthusiastic
- Critical, quarrelsome [R]
- Dependable, self-disciplined
- Anxious, easily upset
- Open to new experiences, complex
- Reserved, quiet [R]
- Sympathetic, warm
- Disorganized, careless [R]
- Calm, emotionally stable [R]
- Conventional, uncreative [R]
Extraversion: Mean score of items 1 and 6
Agreeableness: Mean score of items 2 and 7
Conscientiousness: Mean score of items 3 and 8
Neuroticism: Mean score of items 4 and 9
Openness: Mean score of items 5 and 10
means <- df_bsc_elg %>%
dplyr::select(PID,TIPI_extra:TIPI_open) %>%
pivot_longer(-PID,
names_to = "trait",
values_to = "score") %>%
filter(!is.na(score)) %>%
group_by(trait) %>%
summarise(score = mean(score)) %>%
ungroup()
df_bsc_elg %>%
dplyr::select(PID,TIPI_extra:TIPI_open) %>%
pivot_longer(-PID,
names_to = "trait",
values_to = "score") %>%
filter(!is.na(score)) %>%
ggplot() +
geom_density(aes(x = score), fill = "lightblue") +
scale_x_continuous(limits = c(1,7),
breaks = seq(1,7,1)) +
geom_vline(data = means,mapping = aes(xintercept = score),
color = "black",
linetype = "dashed",
size = 1.1) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold")) +
facet_wrap(~trait,nrow = 2)
Analysis
Correlations
df_bsc_elg %>%
rename(brokencontract = brokencontract_openai) %>%
dplyr::select(brokencontract,antiest,change,trust_deminst,trust_natinst,trust_science,vote_likely,
SDO,ideo_con:ideo_prog,TIPI_extra:TIPI_open) %>%
corPlot(upper = TRUE,stars = TRUE,xsrt = 270)
Linear models
Here, I report the preregistered analyses (all variables are
z-scored). Feel free to explore different linear models, with original
scales, here:
EXPLORE MODELS ↗
DV: Antiestablishment Sentiment
Model 1
Outcome: Antiestablishment sentiment
Predictor: Broken social contract
Controls: conservatism
m1 <- lm(antiest_z ~ brokencontract_openai_z + ideo_con_z,data = df_bsc_elg)
apa_lm <- apa_print(m1)
kbl(apa_lm$table) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value |
|---|---|---|---|---|---|
| Intercept | 0.00 | [-0.05, 0.06] | 0.09 | 1120 | .932 |
| Brokencontract openai z | 0.22 | [0.16, 0.28] | 7.66 | 1120 | < .001 |
| Ideo con z | -0.11 | [-0.16, -0.05] | -3.71 | 1120 | < .001 |
Model 2
Outcome: Antiestablishment sentiment
Predictor: Broken social contract
Controls: conservatism, social dominance orientation,
agreeableness
m1 <- lm(antiest_z ~ brokencontract_openai_z + ideo_con_z + SDO_z + TIPI_agree_z,data = df_bsc_elg)
apa_lm <- apa_print(m1)
kbl(apa_lm$table) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value |
|---|---|---|---|---|---|
| Intercept | 0.00 | [-0.05, 0.06] | 0.09 | 1118 | .926 |
| Brokencontract openai z | 0.21 | [0.16, 0.27] | 7.36 | 1118 | < .001 |
| Ideo con z | -0.13 | [-0.20, -0.06] | -3.82 | 1118 | < .001 |
| SDO z | 0.04 | [-0.03, 0.11] | 1.19 | 1118 | .235 |
| TIPI agree z | -0.08 | [-0.14, -0.02] | -2.75 | 1118 | .006 |
Model 3
Outcome: Antiestablishment sentiment
Predictor: Broken social contract
Controls: conservatism, social dominance orientation,
agreeableness, gender, race, ethnicity, income, education, age
m1 <- lm(antiest_z ~ brokencontract_openai_z + ideo_con_z + SDO_z + TIPI_agree_z + man + white + hispanic + income_num_z + edu_num_z + age_z,data = df_bsc_elg)
apa_lm <- apa_print(m1)
kbl(apa_lm$table) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value |
|---|---|---|---|---|---|
| Intercept | -0.10 | [-0.25, 0.04] | -1.38 | 1010 | .168 |
| Brokencontract openai z | 0.21 | [0.15, 0.27] | 6.92 | 1010 | < .001 |
| Ideo con z | -0.12 | [-0.19, -0.05] | -3.48 | 1010 | < .001 |
| SDO z | 0.06 | [-0.01, 0.13] | 1.76 | 1010 | .078 |
| TIPI agree z | -0.07 | [-0.13, -0.01] | -2.18 | 1010 | .029 |
| Man | 0.01 | [-0.11, 0.13] | 0.19 | 1010 | .847 |
| White | 0.10 | [-0.05, 0.26] | 1.28 | 1010 | .202 |
| Hispanic | 0.01 | [-0.22, 0.25] | 0.11 | 1010 | .913 |
| Income num z | -0.13 | [-0.19, -0.07] | -4.40 | 1010 | < .001 |
| Edu num z | -0.11 | [-0.17, -0.05] | -3.43 | 1010 | < .001 |
| Age z | -0.12 | [-0.19, -0.05] | -3.47 | 1010 | < .001 |
Model 4
Outcome: Antiestablishment sentiment
Predictor: Broken social contract
Controls: conservatism, social dominance orientation,
agreeableness, gender, race, ethnicity, income, education, age, county
median income, county GINI coefficient (i.e., county inequality), and
county density
m1 <- lm(antiest_z ~ brokencontract_openai_z + ideo_con_z + SDO_z + TIPI_agree_z + man + white + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)
apa_lm <- apa_print(m1)
kbl(apa_lm$table) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value |
|---|---|---|---|---|---|
| Intercept | -0.09 | [-0.23, 0.06] | -1.15 | 993 | .248 |
| Brokencontract openai z | 0.21 | [0.15, 0.26] | 6.82 | 993 | < .001 |
| Ideo con z | -0.13 | [-0.20, -0.06] | -3.57 | 993 | < .001 |
| SDO z | 0.07 | [0.00, 0.14] | 2.03 | 993 | .042 |
| TIPI agree z | -0.07 | [-0.13, 0.00] | -2.10 | 993 | .036 |
| Man | 0.01 | [-0.11, 0.13] | 0.12 | 993 | .905 |
| White | 0.08 | [-0.08, 0.24] | 1.03 | 993 | .303 |
| Hispanic | 0.03 | [-0.21, 0.26] | 0.24 | 993 | .812 |
| Income num z | -0.13 | [-0.19, -0.07] | -4.26 | 993 | < .001 |
| Edu num z | -0.10 | [-0.16, -0.04] | -3.30 | 993 | .001 |
| Age z | -0.12 | [-0.19, -0.06] | -3.55 | 993 | < .001 |
| County medianincome z | -0.01 | [-0.07, 0.06] | -0.18 | 993 | .854 |
| County gini z | -0.05 | [-0.13, 0.02] | -1.50 | 993 | .135 |
| County density z | 0.00 | [-0.07, 0.07] | -0.08 | 993 | .938 |
DV: Support for radical change
Model 1
Outcome: Support for radical change
Predictor: Broken social contract
Controls: conservatism
m1 <- lm(change_z ~ brokencontract_openai_z + ideo_con_z,data = df_bsc_elg)
apa_lm <- apa_print(m1)
kbl(apa_lm$table) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value |
|---|---|---|---|---|---|
| Intercept | -0.02 | [-0.08, 0.03] | -0.77 | 1120 | .439 |
| Brokencontract openai z | 0.16 | [0.10, 0.21] | 5.41 | 1120 | < .001 |
| Ideo con z | -0.19 | [-0.25, -0.13] | -6.58 | 1120 | < .001 |
Model 2
Outcome: Support for radical change
Predictor: Broken social contract
Controls: conservatism, social dominance orientation,
agreeableness
m1 <- lm(change_z ~ brokencontract_openai_z + ideo_con_z + SDO_z + TIPI_agree_z,data = df_bsc_elg)
apa_lm <- apa_print(m1)
kbl(apa_lm$table) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value |
|---|---|---|---|---|---|
| Intercept | -0.02 | [-0.08, 0.04] | -0.74 | 1118 | .460 |
| Brokencontract openai z | 0.16 | [0.10, 0.22] | 5.50 | 1118 | < .001 |
| Ideo con z | -0.15 | [-0.22, -0.08] | -4.41 | 1118 | < .001 |
| SDO z | -0.08 | [-0.15, -0.01] | -2.37 | 1118 | .018 |
| TIPI agree z | -0.02 | [-0.08, 0.04] | -0.69 | 1118 | .492 |
Model 3
Outcome: Support for radical change
Predictor: Broken social contract
Controls: conservatism, social dominance orientation,
agreeableness, gender, race, ethnicity, income, education, age
m1 <- lm(change_z ~ brokencontract_openai_z + ideo_con_z + SDO_z + TIPI_agree_z + man + white + hispanic + income_num_z + edu_num_z + age_z,data = df_bsc_elg)
apa_lm <- apa_print(m1)
kbl(apa_lm$table) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value |
|---|---|---|---|---|---|
| Intercept | 0.25 | [0.11, 0.39] | 3.47 | 1010 | < .001 |
| Brokencontract openai z | 0.15 | [0.10, 0.21] | 5.33 | 1010 | < .001 |
| Ideo con z | -0.12 | [-0.19, -0.06] | -3.62 | 1010 | < .001 |
| SDO z | -0.05 | [-0.12, 0.02] | -1.50 | 1010 | .134 |
| TIPI agree z | -0.01 | [-0.07, 0.05] | -0.34 | 1010 | .734 |
| Man | -0.30 | [-0.41, -0.18] | -4.98 | 1010 | < .001 |
| White | -0.16 | [-0.32, -0.01] | -2.14 | 1010 | .033 |
| Hispanic | 0.10 | [-0.13, 0.32] | 0.85 | 1010 | .394 |
| Income num z | -0.11 | [-0.16, -0.05] | -3.54 | 1010 | < .001 |
| Edu num z | -0.08 | [-0.14, -0.03] | -2.82 | 1010 | .005 |
| Age z | -0.25 | [-0.31, -0.18] | -7.38 | 1010 | < .001 |
Model 4
Outcome: Support for radical change
Predictor: Broken social contract
Controls: conservatism, social dominance orientation,
agreeableness, gender, race, ethnicity, income, education, age, county
median income, county GINI coefficient (i.e., county inequality), and
county density
m1 <- lm(change_z ~ brokencontract_openai_z + ideo_con_z + SDO_z + TIPI_agree_z + man + white + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)
apa_lm <- apa_print(m1)
kbl(apa_lm$table) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value |
|---|---|---|---|---|---|
| Intercept | 0.24 | [0.10, 0.38] | 3.27 | 993 | .001 |
| Brokencontract openai z | 0.15 | [0.10, 0.21] | 5.24 | 993 | < .001 |
| Ideo con z | -0.13 | [-0.20, -0.06] | -3.69 | 993 | < .001 |
| SDO z | -0.05 | [-0.12, 0.01] | -1.53 | 993 | .127 |
| TIPI agree z | -0.01 | [-0.07, 0.05] | -0.36 | 993 | .722 |
| Man | -0.29 | [-0.41, -0.17] | -4.84 | 993 | < .001 |
| White | -0.16 | [-0.32, -0.01] | -2.09 | 993 | .037 |
| Hispanic | 0.12 | [-0.11, 0.34] | 1.00 | 993 | .318 |
| Income num z | -0.11 | [-0.17, -0.05] | -3.49 | 993 | < .001 |
| Edu num z | -0.08 | [-0.14, -0.02] | -2.66 | 993 | .008 |
| Age z | -0.24 | [-0.31, -0.18] | -7.11 | 993 | < .001 |
| County medianincome z | 0.01 | [-0.06, 0.07] | 0.20 | 993 | .843 |
| County gini z | -0.04 | [-0.11, 0.03] | -1.25 | 993 | .211 |
| County density z | 0.04 | [-0.03, 0.11] | 1.20 | 993 | .229 |
DV: Trust in democratic political institutions
Model 1
Outcome: Trust in democratic political
institutions
Predictor: Broken social contract
Controls: conservatism
m1 <- lm(trust_deminst_z ~ brokencontract_openai_z + ideo_con_z,data = df_bsc_elg)
apa_lm <- apa_print(m1)
kbl(apa_lm$table) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value |
|---|---|---|---|---|---|
| Intercept | 0.00 | [-0.06, 0.05] | -0.05 | 1120 | .957 |
| Brokencontract openai z | -0.26 | [-0.32, -0.20] | -9.04 | 1120 | < .001 |
| Ideo con z | 0.06 | [0.00, 0.12] | 2.09 | 1120 | .036 |
Model 2
Outcome: Trust in democratic political
institutions
Predictor: Broken social contract
Controls: conservatism, social dominance orientation,
agreeableness
m1 <- lm(trust_deminst_z ~ brokencontract_openai_z + ideo_con_z + SDO_z + TIPI_agree_z,data = df_bsc_elg)
apa_lm <- apa_print(m1)
kbl(apa_lm$table) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value |
|---|---|---|---|---|---|
| Intercept | 0.00 | [-0.06, 0.05] | -0.05 | 1118 | .957 |
| Brokencontract openai z | -0.25 | [-0.30, -0.19] | -8.65 | 1118 | < .001 |
| Ideo con z | 0.10 | [0.03, 0.17] | 3.01 | 1118 | .003 |
| SDO z | -0.08 | [-0.14, -0.01] | -2.27 | 1118 | .024 |
| TIPI agree z | 0.10 | [0.05, 0.16] | 3.55 | 1118 | < .001 |
Model 3
Outcome: Trust in democratic political
institutions
Predictor: Broken social contract
Controls: conservatism, social dominance orientation,
agreeableness, gender, race, ethnicity, income, education, age
m1 <- lm(trust_deminst_z ~ brokencontract_openai_z + ideo_con_z + SDO_z + TIPI_agree_z + man + white + hispanic + income_num_z + edu_num_z + age_z,data = df_bsc_elg)
apa_lm <- apa_print(m1)
kbl(apa_lm$table) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value |
|---|---|---|---|---|---|
| Intercept | 0.13 | [-0.01, 0.28] | 1.77 | 1010 | .078 |
| Brokencontract openai z | -0.23 | [-0.29, -0.17] | -7.78 | 1010 | < .001 |
| Ideo con z | 0.10 | [0.03, 0.17] | 2.80 | 1010 | .005 |
| SDO z | -0.09 | [-0.16, -0.02] | -2.58 | 1010 | .010 |
| TIPI agree z | 0.11 | [0.04, 0.17] | 3.38 | 1010 | < .001 |
| Man | -0.02 | [-0.14, 0.10] | -0.32 | 1010 | .751 |
| White | -0.12 | [-0.27, 0.04] | -1.45 | 1010 | .148 |
| Hispanic | -0.09 | [-0.32, 0.14] | -0.77 | 1010 | .444 |
| Income num z | 0.05 | [-0.01, 0.11] | 1.66 | 1010 | .097 |
| Edu num z | 0.11 | [0.05, 0.17] | 3.64 | 1010 | < .001 |
| Age z | 0.01 | [-0.06, 0.08] | 0.28 | 1010 | .776 |
Model 4
Outcome: Trust in democratic political
institutions
Predictor: Broken social contract
Controls: conservatism, social dominance orientation,
agreeableness, gender, race, ethnicity, income, education, age, county
median income, county GINI coefficient (i.e., county inequality), and
county density
m1 <- lm(trust_deminst_z ~ brokencontract_openai_z + ideo_con_z + SDO_z + TIPI_agree_z + man + white + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)
apa_lm <- apa_print(m1)
kbl(apa_lm$table) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value |
|---|---|---|---|---|---|
| Intercept | 0.11 | [-0.04, 0.25] | 1.40 | 993 | .162 |
| Brokencontract openai z | -0.23 | [-0.29, -0.17] | -7.58 | 993 | < .001 |
| Ideo con z | 0.11 | [0.04, 0.18] | 3.01 | 993 | .003 |
| SDO z | -0.10 | [-0.17, -0.03] | -2.86 | 993 | .004 |
| TIPI agree z | 0.11 | [0.04, 0.17] | 3.39 | 993 | < .001 |
| Man | -0.02 | [-0.14, 0.10] | -0.35 | 993 | .727 |
| White | -0.09 | [-0.25, 0.07] | -1.07 | 993 | .285 |
| Hispanic | -0.09 | [-0.33, 0.14] | -0.78 | 993 | .435 |
| Income num z | 0.05 | [-0.01, 0.11] | 1.52 | 993 | .129 |
| Edu num z | 0.10 | [0.04, 0.17] | 3.35 | 993 | < .001 |
| Age z | 0.02 | [-0.05, 0.09] | 0.49 | 993 | .624 |
| County medianincome z | 0.02 | [-0.04, 0.09] | 0.72 | 993 | .470 |
| County gini z | 0.04 | [-0.03, 0.11] | 1.04 | 993 | .297 |
| County density z | 0.04 | [-0.03, 0.11] | 1.09 | 993 | .277 |
DV: Trust in non-political mainstream institutions
Model 1
Outcome: Trust in non-political mainstream
institutions
Predictor: Broken social contract
Controls: conservatism
m1 <- lm(trust_natinst_z ~ brokencontract_openai_z + ideo_con_z,data = df_bsc_elg)
apa_lm <- apa_print(m1)
kbl(apa_lm$table) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value |
|---|---|---|---|---|---|
| Intercept | 0.01 | [-0.05, 0.07] | 0.28 | 1120 | .779 |
| Brokencontract openai z | -0.22 | [-0.28, -0.17] | -7.70 | 1120 | < .001 |
| Ideo con z | 0.08 | [0.03, 0.14] | 2.92 | 1120 | .004 |
Model 2
Outcome: Trust in non-political mainstream
institutions
Predictor: Broken social contract
Controls: conservatism, social dominance orientation,
agreeableness
m1 <- lm(trust_natinst_z ~ brokencontract_openai_z + ideo_con_z + SDO_z + TIPI_agree_z,data = df_bsc_elg)
apa_lm <- apa_print(m1)
kbl(apa_lm$table) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value |
|---|---|---|---|---|---|
| Intercept | 0.01 | [-0.05, 0.06] | 0.27 | 1118 | .791 |
| Brokencontract openai z | -0.20 | [-0.26, -0.15] | -7.19 | 1118 | < .001 |
| Ideo con z | 0.12 | [0.06, 0.19] | 3.75 | 1118 | < .001 |
| SDO z | -0.08 | [-0.14, -0.01] | -2.25 | 1118 | .025 |
| TIPI agree z | 0.18 | [0.12, 0.24] | 6.13 | 1118 | < .001 |
Model 3
Outcome: Trust in non-political mainstream
institutions
Predictor: Broken social contract
Controls: conservatism, social dominance orientation,
agreeableness, gender, race, ethnicity, income, education, age
m1 <- lm(trust_natinst_z ~ brokencontract_openai_z + ideo_con_z + SDO_z + TIPI_agree_z + man + white + hispanic + income_num_z + edu_num_z + age_z,data = df_bsc_elg)
apa_lm <- apa_print(m1)
kbl(apa_lm$table) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value |
|---|---|---|---|---|---|
| Intercept | 0.10 | [-0.04, 0.24] | 1.36 | 1010 | .174 |
| Brokencontract openai z | -0.20 | [-0.26, -0.14] | -6.77 | 1010 | < .001 |
| Ideo con z | 0.11 | [0.04, 0.18] | 3.18 | 1010 | .002 |
| SDO z | -0.09 | [-0.15, -0.02] | -2.51 | 1010 | .012 |
| TIPI agree z | 0.16 | [0.09, 0.22] | 5.03 | 1010 | < .001 |
| Man | 0.02 | [-0.10, 0.14] | 0.34 | 1010 | .732 |
| White | -0.10 | [-0.26, 0.05] | -1.29 | 1010 | .198 |
| Hispanic | -0.07 | [-0.30, 0.16] | -0.62 | 1010 | .536 |
| Income num z | 0.10 | [0.04, 0.16] | 3.24 | 1010 | .001 |
| Edu num z | 0.09 | [0.04, 0.15] | 3.12 | 1010 | .002 |
| Age z | 0.16 | [0.10, 0.23] | 4.85 | 1010 | < .001 |
Model 4
Outcome: Trust in non-political mainstream
institutions
Predictor: Broken social contract
Controls: conservatism, social dominance orientation,
agreeableness, gender, race, ethnicity, income, education, age, county
median income, county GINI coefficient (i.e., county inequality), and
county density
m1 <- lm(trust_natinst_z ~ brokencontract_openai_z + ideo_con_z + SDO_z + TIPI_agree_z + man + white + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)
apa_lm <- apa_print(m1)
kbl(apa_lm$table) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value |
|---|---|---|---|---|---|
| Intercept | 0.08 | [-0.06, 0.23] | 1.10 | 993 | .273 |
| Brokencontract openai z | -0.20 | [-0.25, -0.14] | -6.63 | 993 | < .001 |
| Ideo con z | 0.12 | [0.05, 0.19] | 3.47 | 993 | < .001 |
| SDO z | -0.10 | [-0.17, -0.03] | -2.84 | 993 | .005 |
| TIPI agree z | 0.16 | [0.10, 0.22] | 5.07 | 993 | < .001 |
| Man | 0.02 | [-0.10, 0.14] | 0.28 | 993 | .777 |
| White | -0.08 | [-0.24, 0.07] | -1.02 | 993 | .307 |
| Hispanic | -0.07 | [-0.30, 0.16] | -0.61 | 993 | .543 |
| Income num z | 0.09 | [0.03, 0.15] | 2.98 | 993 | .003 |
| Edu num z | 0.09 | [0.03, 0.15] | 2.82 | 993 | .005 |
| Age z | 0.18 | [0.11, 0.25] | 5.17 | 993 | < .001 |
| County medianincome z | 0.03 | [-0.03, 0.10] | 1.09 | 993 | .276 |
| County gini z | -0.02 | [-0.09, 0.05] | -0.46 | 993 | .643 |
| County density z | 0.07 | [0.00, 0.14] | 2.05 | 993 | .041 |
DV: Turnout for Presidential Election
Model 1
Outcome: Turnout for Presidential Election
Predictor: Broken social contract
Controls: conservatism
m1 <- lm(vote_likely_z ~ brokencontract_openai_z + ideo_con_z,data = df_bsc_elg)
apa_lm <- apa_print(m1)
kbl(apa_lm$table) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value |
|---|---|---|---|---|---|
| Intercept | 0.02 | [-0.04, 0.08] | 0.76 | 1120 | .447 |
| Brokencontract openai z | -0.03 | [-0.09, 0.02] | -1.16 | 1120 | .248 |
| Ideo con z | 0.12 | [0.06, 0.18] | 4.05 | 1120 | < .001 |
Model 2
Outcome: Turnout for Presidential Election
Predictor: Broken social contract
Controls: conservatism, social dominance orientation,
agreeableness
m1 <- lm(vote_likely_z ~ brokencontract_openai_z + ideo_con_z + SDO_z + TIPI_agree_z,data = df_bsc_elg)
apa_lm <- apa_print(m1)
kbl(apa_lm$table) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value |
|---|---|---|---|---|---|
| Intercept | 0.02 | [-0.03, 0.08] | 0.78 | 1118 | .435 |
| Brokencontract openai z | -0.02 | [-0.07, 0.04] | -0.63 | 1118 | .529 |
| Ideo con z | 0.18 | [0.11, 0.24] | 5.23 | 1118 | < .001 |
| SDO z | -0.11 | [-0.18, -0.04] | -3.26 | 1118 | .001 |
| TIPI agree z | 0.12 | [0.06, 0.18] | 3.96 | 1118 | < .001 |
Model 3
Outcome: Turnout for Presidential Election
Predictor: Broken social contract
Controls: conservatism, social dominance orientation,
agreeableness, gender, race, ethnicity, income, education, age
m1 <- lm(vote_likely_z ~ brokencontract_openai_z + ideo_con_z + SDO_z + TIPI_agree_z + man + white + hispanic + income_num_z + edu_num_z + age_z,data = df_bsc_elg)
apa_lm <- apa_print(m1)
kbl(apa_lm$table) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value |
|---|---|---|---|---|---|
| Intercept | -0.04 | [-0.18, 0.10] | -0.58 | 1010 | .565 |
| Brokencontract openai z | -0.04 | [-0.09, 0.02] | -1.25 | 1010 | .211 |
| Ideo con z | 0.19 | [0.12, 0.25] | 5.53 | 1010 | < .001 |
| SDO z | -0.12 | [-0.19, -0.06] | -3.63 | 1010 | < .001 |
| TIPI agree z | 0.05 | [-0.01, 0.11] | 1.65 | 1010 | .099 |
| Man | -0.03 | [-0.14, 0.09] | -0.45 | 1010 | .654 |
| White | 0.04 | [-0.11, 0.19] | 0.49 | 1010 | .626 |
| Hispanic | 0.19 | [-0.03, 0.41] | 1.66 | 1010 | .097 |
| Income num z | 0.06 | [0.00, 0.12] | 2.03 | 1010 | .043 |
| Edu num z | 0.08 | [0.02, 0.14] | 2.63 | 1010 | .009 |
| Age z | 0.33 | [0.26, 0.39] | 9.90 | 1010 | < .001 |
Model 4
Outcome: Turnout for Presidential Election
Predictor: Broken social contract
Controls: conservatism, social dominance orientation,
agreeableness, gender, race, ethnicity, income, education, age, county
median income, county GINI coefficient (i.e., county inequality), and
county density
m1 <- lm(vote_likely_z ~ brokencontract_openai_z + ideo_con_z + SDO_z + TIPI_agree_z + man + white + hispanic + income_num_z + edu_num_z + age_z + county_medianincome_z + county_gini_z + county_density_z,data = df_bsc_elg)
apa_lm <- apa_print(m1)
kbl(apa_lm$table) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value |
|---|---|---|---|---|---|
| Intercept | -0.02 | [-0.16, 0.12] | -0.25 | 993 | .803 |
| Brokencontract openai z | -0.04 | [-0.09, 0.02] | -1.25 | 993 | .213 |
| Ideo con z | 0.19 | [0.12, 0.26] | 5.59 | 993 | < .001 |
| SDO z | -0.12 | [-0.19, -0.05] | -3.56 | 993 | < .001 |
| TIPI agree z | 0.05 | [-0.01, 0.11] | 1.69 | 993 | .091 |
| Man | -0.03 | [-0.15, 0.09] | -0.54 | 993 | .590 |
| White | 0.01 | [-0.15, 0.16] | 0.09 | 993 | .925 |
| Hispanic | 0.20 | [-0.03, 0.42] | 1.73 | 993 | .083 |
| Income num z | 0.05 | [-0.01, 0.11] | 1.73 | 993 | .083 |
| Edu num z | 0.08 | [0.02, 0.14] | 2.61 | 993 | .009 |
| Age z | 0.33 | [0.27, 0.40] | 9.83 | 993 | < .001 |
| County medianincome z | 0.04 | [-0.02, 0.11] | 1.43 | 993 | .153 |
| County gini z | -0.03 | [-0.10, 0.04] | -0.82 | 993 | .412 |
| County density z | -0.02 | [-0.08, 0.05] | -0.53 | 993 | .597 |
Social Contract Measure
Values: US on paper
Since its independence and onwards, the formation of the United States as a sovereign country was based on a number of values, all of which were inscribed in the constitution. This document, importantly, has evolved since it was first written.
ON PAPER, what do you think are the values that the U.S. stands for? Please list FIVE values.
Each value should consist of 1-3 words. Try to be as clear and succinct as possible.
paste disabled
below are values mentioned, ordered by the number of mentions*
Weights: US on paper
participants were shown the values that they inputted in the previous question and were asked to assign them a forced sum of 100 points, based on its perceived importance to the US on paper.
The values you listed are shown below.
Now, we ask you to indicate how important you think each value is to the U.S. on paper.
To that end, you have a sum of 100 points. Please allocate those points to the values you listed based on how important you think they are to the U.S. ON PAPER.
For example, if you think all five values are equally important, each of them would get 20 points. If you think value X is more important than value Y, then value X would get more points than value Y.
Values: US in practice
We want you to think of the values that the United States stands for in practice.
Regardless of what is written in the constitution and across party lines, the U.S. administrations stand for certain values and do not stand for others.
IN PRACTICE, what do you believe are the values that the U.S. stands for? Please list FIVE values.
Each value should consist of 1-3 words. Try to be as clear and succinct as possible.
paste disabled
below are values mentioned, ordered by the number of mentions*
Weights: US in practice
participants were shown the values that they inputted in the previous question and were asked to assign them a forced sum of 100 points, based on its perceived importance to the US in practice.
The values you listed are shown below.
Now, we ask you to indicate how important you think each value is to the U.S. in practice.
To that end, you have a sum of 100 points. Please allocate those points to the values you listed based on how important you think they are to the U.S. IN PRACTICE.
For example, if you think all five values are equally important, each of them would get 20 points. If you think value X is more important than value Y, then value X would get more points than value Y.
Computed Broken Social Contract Variable
See Computational analytical strategy above. This is the distribution: