Background

This is a preregistered nationally representative study.

Description

In this project, we examine the role of a broken social contract in people’s trust in institutions and anti-establishment sentiment. Specifically, we identify, through various methods, gaps between what people believe they are promised by the government on paper and what they are being provided by the government in practice. We then examine the explanatory role this gap plays in various socio-political behavioral and attitudinal outcomes.

Hypotheses

  1. The perceived extent to which the promise of the US on paper is broken is positively associated with support for radical change.
  2. The perceived extent to which the promise of the US on paper is broken is positively associated with anti-establishment sentiment.
  3. The perceived extent to which the promise of the US on paper is broken is negatively associated with trust in democratic institutions (executive, legislative, and judicial branch).
  4. The perceived extent to which the promise of the US on paper is broken is negatively associated with trust in mainstream societal institutions (media, education, police, military, finance, medicine).

Study design

The study will consist of two major parts:

1. Social contract: Participants will see eight overarching values that guide the US on paper, derived from a previous study. They will be asked to indicate their perception of priorities of the US on paper as they relate to these values. Then, they will indicate the extent to which they believe the US lives up to each of these values. Then, they will give their own preferred prioritization of these values, if they were to design a brand new country and be randomly born into it.
2. Attitudes and individual differences: Participants will complete measures of anti-establishment sentiment, trust in institutions, SDO, TIPI, support for radical change, and political identification/behavior.

Analysis plan

The following linear models will be conducted for each of these outcome variables: (1) support for radical change; (2) anti-establishment sentiment; (3) trust in democratic institutions; (4) trust in mainstream societal institutions.

1. Correlation matrix: Broken promise score, support for radical change, anti-establishment sentiment, trust in democratic institutions, trust in mainstream societal institutions, conservatism, SDO, TIPI extraversion, TIPI agreeableness, TIPI Conscientiousness, TIPI neuroticism, TIPI openness.
2. Linear Model 1: Broken promise score as predictor; conservatism as control.
3. Linear Model 2: Broken promise score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, age, county mediation income, county GINI coefficient, and county density as controls.

Attention Check

But first, let’s exclude participants who failed attention checks: one simple attention check embedded in the anti-establishment scale.

eligible_N <- df_bsc %>% 
  group_by(check_1) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  filter(check_1 == 0) %>% 
  select(N) %>% 
  unname() %>% 
  unlist()

df_bsc %>% 
  group_by(check_1) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")
check_1 N Perc
0 994 98.51
1 15 1.49

Great. That leaves us with 994 eligible participants.

Demographics

Race and ethnicity

df_bsc_elg %>% 
  group_by(race,hispanic) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")
## `summarise()` has grouped output by 'race'. You can override using the
## `.groups` argument.
race hispanic N Perc
American Indian or Alaska Native 0 1 0.10
Asian 0 49 4.93
Black or African American 0 152 15.29
Black or African American 1 6 0.60
Middle Eastern or North African 0 1 0.10
Native Hawaiian or Other Pacific Islander 0 1 0.10
Other (please specify) 0 4 0.40
White 0 588 59.15
White 1 54 5.43
multiracial 0 43 4.33
multiracial 1 4 0.40
NA 1 91 9.15

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() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")
gender N Perc
man 457 45.98
other 7 0.70
woman 530 53.32

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
40.47 14.31

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 3 0.30
GED 343 34.51
2yearColl 145 14.59
4yearColl 354 35.61
MA 107 10.76
PHD 34 3.42
NA 8 0.80

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()

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 355 35.71
Independent 333 33.50
Republican 306 30.78

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 438 44.06
Donald Trump 322 32.39
I did not vote 187 18.81
Third-party candidate 47 4.73

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 425 42.76
Donald Trump 369 37.12
Robert F. Kennedy Jr.  88 8.85
Other 74 7.44
Jill Stein 23 2.31
Cornel West 13 1.31
2 0.20

Measures

US priorities 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 (in the constitution), what are the values that the U.S. stands for? We want you to indicate the United State’s priorities on paper.

To that end, you have a sum of 100 points. Please allocate those points to the following values based on how important you think they are to the U.S. on paper.

The values, presented in alphabetical order, are: Democracy, Equality, Freedom, Individualism, Justice, Pursuit of Happiness, Right to Bear Arms, Tolerance.

So, If you think a certain value is more important to the U.S. on paper than another value, then the first value would get more points than the second. If you think a certain value is not important at all to the U.S. on paper, it would get zero points. The total must add up to 100 points.

df_bsc_long_elg %>% 
  filter(type == "paper") %>% 
  group_by(value) %>% 
  summarise(priority_us = round(mean(weight),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")
value priority_us
arms 10.37
democracy 19.40
equality 13.36
freedom 20.94
happiness 10.01
individualism 8.20
justice 11.88
tolerance 5.85

US delivering on its promise

Now, please indicate the extent to which the U.S. government, regardless of party, is living up to each of these values, in practice.

In the scale below, 0 means that the U.S. is not living up to what this value stands for at all (it cannot get any worse) and 100 means that the U.S. is living up to what this value stands for to a great extent (it cannot get any better).

df_bsc_long_elg %>% 
  filter(type == "paper") %>% 
  group_by(value) %>% 
  summarise(score = round(mean(score,na.rm = T),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")
value score
arms 57.88
democracy 47.83
equality 41.32
freedom 50.59
happiness 41.78
individualism 49.74
justice 39.11
tolerance 39.32

Participants’ ideal

And now, we want you to imagine your ideal country.

Importantly, imagine this ideal country as if you are randomly born into its population. You can end up in any level of its citizenry. So, if you could design a country completely from scratch and write its constitution, what would be its guiding values?

Again, you have a sum of 100 points. Please allocate those points to the following values based on how important they are to you.

The values, presented in alphabetical order, are: Democracy, Equality, Freedom, Individualism, Justice, Pursuit of Happiness, Right to Bear Arms, Tolerance.

So, If a certain value is more important to you than another value, then the first value would get more points than the second. If a certain value is not important at all to you, it would get zero points. Must total 100 points.

df_bsc_long_elg %>% 
  filter(type == "ideal") %>% 
  group_by(value) %>% 
  summarise(priority_ideal = round(mean(weight),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")
value priority_ideal
arms 6.14
democracy 17.34
equality 15.93
freedom 19.56
happiness 11.22
individualism 8.03
justice 14.13
tolerance 7.64

Broken promise

The score was computed from two questions: US priorities on paper and US delivering on its promise.

For every value, we multiplied the weight (priorities of the US on paper) by the score (US delivering on its promise) to created weighted scores - one per value. Then, we summed all the weighted mean scores. Then, we substracted that sum score from 100 to create the broken social contract score.

df_bsc_elg %>%
  ggplot(aes(x = brokenpromise)) +
  geom_histogram(fill = "lightblue",
                 binwidth = 1) +
  geom_vline(xintercept = mean(df_bsc_elg$brokenpromise,na.rm = T),
             color = "black",
             linetype = "dashed",
             size = 1.1) +
  scale_x_continuous(limits = c(-1,101),
                     breaks = seq(0,100,20)) +
  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"))

Personal disappointment

The score was computed from two questions: personal priorities and US delivering on its promise.

Same way as the broken promise score.

df_bsc_elg %>%
  ggplot(aes(x = personaldisappoint)) +
  geom_histogram(fill = "lightblue",
                 binwidth = 1) +
  geom_vline(xintercept = mean(df_bsc_elg$personaldisappoint,na.rm = T),
             color = "black",
             linetype = "dashed",
             size = 1.1) +
  scale_x_continuous(limits = c(-1,101),
                     breaks = seq(0,100,20)) +
  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"))

Anti-Establishment

  1. The US’s economy is rigged to advantage the rich and powerful
  2. Traditional politicians and parties don’t care about people like me
  3. Experts in this country don’t understand the lives of people like me
  4. Most of the time we can trust people in the government to do what is right [R]
    alpha = 0.77
df_bsc_elg %>% 
  ggplot(aes(x = antiest)) +
  geom_density(fill = "lightblue",
                 color = "black") +
  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"))

Trust in democratic institutions

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

alpha = 0.86

df_bsc_elg %>% 
  ggplot(aes(x = trust_deminst)) +
  geom_density(fill = "lightblue",
                 color = "black") +
  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 national 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

alpha = 0.83

df_bsc_elg %>% 
  ggplot(aes(x = trust_natinst)) +
  geom_density(fill = "lightblue",
                 color = "black") +
  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"))

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_density(fill = "lightblue",
                 color = "black") +
  scale_x_continuous(breaks = seq(1,7,1),
                     limits = c(1,7)) +
  ylab("density") +
  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"))

SDO

  1. An ideal society requires some groups to be on top and others to be on the bottom
  2. Some groups of people are simply inferior to other groups
  3. No one group should dominate in society [R]
  4. Groups at the bottom are just as deserving as groups at the top [R]
  5. Group equality should not be our primary goal
  6. It is unjust to try to make groups equal
  7. We should do what we can to equalize conditions for different groups [R]
  8. We should work to give all groups an equal chance to succeed [R]

    alpha = 0.9
df_bsc_elg %>% 
  ggplot(aes(x = SDO)) +
  geom_density(fill = "lightblue",
                 color = "black") +
  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"))

TIPI

I see myself as… (1 = Strongly Disagree to 7 = Strongly Agree)

  1. Extraverted, enthusiastic
  2. Critical, quarrelsome [R]
  3. Dependable, self-disciplined
  4. Anxious, easily upset
  5. Open to new experiences, complex
  6. Reserved, quiet [R]
  7. Sympathetic, warm
  8. Disorganized, careless [R]
  9. Calm, emotionally stable [R]
  10. 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)

Voting intentions

What is the likelihood that you will vote in the 2024 Presidential Elections?

df_bsc_elg %>% 
  ggplot(aes(x = vote_likely)) +
  geom_histogram(fill = "lightblue",
                 color = "black",
                 binwidth = 1) +
  scale_x_continuous(breaks = seq(1,5,1),
                     limits = c(0,6)) +
  ylab("density") +
  geom_vline(xintercept = mean(df_bsc_elg$vote_likely,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"))

Analysis

Correlation matrix

Broken promise score, personal disappointment score, support for radical change, anti-establishment sentiment, trust in democratic institutions, trust in mainstream societal institutions, conservatism, SDO, TIPI extraversion, TIPI agreeableness, TIPI Conscientiousness, TIPI neuroticism, TIPI openness.

df_bsc_elg %>% 
  dplyr::select(brokenpromise:personaldisappoint,change,antiest:trust_natinst,ideo_con,SDO,TIPI_extra:TIPI_open) %>%
  corPlot(upper = TRUE,stars = TRUE,xsrt = 270)

Outcome Variable: Support for radical change

Model 1

Broken promise score as predictor; conservatism as control.

m1 <- lm(change_z ~ brokenpromise_z + ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-31)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.01 [-0.07, 0.05] -0.31 930 .757
Brokenpromise z 0.28 [0.21, 0.34] 8.76 930 < .001
Ideo con z -0.16 [-0.22, -0.10] -5.05 930 < .001

Model 2

Broken promise score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, age, county mediation income, county GINI coefficient, and county density as controls.

m1 <- lm(change_z ~ brokenpromise_z + ideo_con_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-32)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.94 [-2.80, 0.91] -1.00 780 .318
Brokenpromise z 0.31 [0.24, 0.38] 8.78 780 < .001
Ideo con z -0.03 [-0.11, 0.05] -0.80 780 .425
SDO z -0.16 [-0.24, -0.08] -3.86 780 < .001
TIPI agree z -0.03 [-0.10, 0.04] -0.84 780 .403
Genderwoman 0.19 [0.05, 0.32] 2.71 780 .007
RaceAsian 0.86 [-1.02, 2.74] 0.90 780 .369
RaceBlack or African American 0.99 [-0.87, 2.85] 1.04 780 .297
RaceMiddle Eastern or North African 0.42 [-2.18, 3.03] 0.32 780 .749
Racemultiracial 1.05 [-0.82, 2.92] 1.10 780 .271
RaceOther please specify 1.93 [-0.33, 4.20] 1.68 780 .094
RaceWhite 0.74 [-1.11, 2.59] 0.79 780 .433
Hispanic 0.07 [-0.18, 0.32] 0.53 780 .593
Income num z -0.03 [-0.10, 0.05] -0.71 780 .476
Edu num z -0.04 [-0.12, 0.03] -1.17 780 .242
Age z -0.17 [-0.24, -0.10] -4.74 780 < .001
County medianincome z 0.01 [-0.06, 0.08] 0.27 780 .785
County gini z -0.03 [-0.12, 0.05] -0.75 780 .456
County density z 0.01 [-0.07, 0.09] 0.30 780 .766

Outcome Variable: Anti-establishment sentiment

Model 1

Broken promise score as predictor; conservatism as control.

m1 <- lm(antiest_z ~ brokenpromise_z + ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-33)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.01 [-0.07, 0.05] -0.35 930 .727
Brokenpromise z 0.33 [0.27, 0.39] 10.65 930 < .001
Ideo con z -0.10 [-0.16, -0.03] -3.07 930 .002

Model 2

Broken promise score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, age, county mediation income, county GINI coefficient, and county density as controls.

m1 <- lm(antiest_z ~ brokenpromise_z + ideo_con_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-34)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.08 [-1.70, 1.86] 0.09 780 .928
Brokenpromise z 0.34 [0.27, 0.41] 10.15 780 < .001
Ideo con z -0.05 [-0.12, 0.03] -1.23 780 .220
SDO z -0.08 [-0.16, 0.00] -2.06 780 .040
TIPI agree z -0.07 [-0.14, 0.00] -2.08 780 .038
Genderwoman 0.08 [-0.05, 0.21] 1.20 780 .231
RaceAsian 0.08 [-1.72, 1.88] 0.09 780 .931
RaceBlack or African American -0.30 [-2.08, 1.49] -0.33 780 .745
RaceMiddle Eastern or North African -1.98 [-4.48, 0.52] -1.55 780 .121
Racemultiracial -0.13 [-1.93, 1.66] -0.14 780 .886
RaceOther please specify 0.10 [-2.07, 2.27] 0.09 780 .931
RaceWhite -0.15 [-1.92, 1.63] -0.16 780 .872
Hispanic 0.17 [-0.07, 0.41] 1.36 780 .175
Income num z -0.06 [-0.13, 0.00] -1.84 780 .066
Edu num z -0.15 [-0.22, -0.08] -4.23 780 < .001
Age z -0.02 [-0.09, 0.05] -0.55 780 .580
County medianincome z -0.04 [-0.10, 0.03] -1.06 780 .292
County gini z -0.13 [-0.21, -0.05] -3.12 780 .002
County density z 0.04 [-0.04, 0.12] 1.03 780 .302

Outcome Variable: Trust in democratic institutions

Model 1

Broken promise score as predictor; conservatism as control.

m1 <- lm(trust_deminst_z ~ brokenpromise_z + ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-35)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.01 [-0.06, 0.05] -0.18 930 .859
Brokenpromise z -0.41 [-0.47, -0.35] -13.54 930 < .001
Ideo con z 0.05 [0.00, 0.11] 1.83 930 .067

Model 2

Broken promise score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, age, county mediation income, county GINI coefficient, and county density as controls.

m1 <- lm(trust_deminst_z ~ brokenpromise_z + ideo_con_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-36)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.97 [-0.76, 2.70] 1.10 780 .271
Brokenpromise z -0.38 [-0.44, -0.31] -11.52 780 < .001
Ideo con z 0.07 [-0.01, 0.14] 1.77 780 .077
SDO z -0.01 [-0.09, 0.06] -0.32 780 .745
TIPI agree z 0.13 [0.06, 0.19] 3.78 780 < .001
Genderwoman -0.08 [-0.21, 0.04] -1.28 780 .202
RaceAsian -0.93 [-2.68, 0.82] -1.05 780 .296
RaceBlack or African American -0.72 [-2.45, 1.01] -0.81 780 .416
RaceMiddle Eastern or North African 0.19 [-2.24, 2.63] 0.15 780 .877
Racemultiracial -1.16 [-2.91, 0.58] -1.31 780 .192
RaceOther please specify -1.05 [-3.16, 1.07] -0.97 780 .331
RaceWhite -0.94 [-2.67, 0.79] -1.07 780 .285
Hispanic -0.10 [-0.33, 0.14] -0.83 780 .407
Income num z 0.01 [-0.05, 0.08] 0.37 780 .713
Edu num z 0.12 [0.06, 0.19] 3.59 780 < .001
Age z 0.00 [-0.07, 0.06] -0.14 780 .890
County medianincome z 0.03 [-0.04, 0.09] 0.87 780 .387
County gini z 0.07 [-0.01, 0.15] 1.67 780 .096
County density z 0.03 [-0.05, 0.10] 0.74 780 .457

Outcome Variable: Trust in national institutions

Model 1

Broken promise score as predictor; conservatism as control.

m1 <- lm(trust_natinst_z ~ brokenpromise_z + ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-37)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.01 [-0.07, 0.05] -0.38 930 .704
Brokenpromise z -0.38 [-0.44, -0.32] -12.50 930 < .001
Ideo con z 0.12 [0.06, 0.18] 3.94 930 < .001

Model 2

Broken promise score as predictor; conservatism, SDO, TIPI agreeableness, gender, race, ethnicity, income, education, age, county mediation income, county GINI coefficient, and county density as controls.

m1 <- lm(trust_natinst_z ~ brokenpromise_z + ideo_con_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-38)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.44 [-2.13, 1.26] -0.51 780 .612
Brokenpromise z -0.36 [-0.42, -0.30] -11.31 780 < .001
Ideo con z 0.10 [0.03, 0.18] 2.86 780 .004
SDO z 0.00 [-0.07, 0.07] -0.02 780 .988
TIPI agree z 0.17 [0.11, 0.23] 5.15 780 < .001
Genderwoman 0.03 [-0.10, 0.15] 0.43 780 .667
RaceAsian 0.31 [-1.41, 2.02] 0.35 780 .726
RaceBlack or African American 0.65 [-1.05, 2.34] 0.75 780 .453
RaceMiddle Eastern or North African 1.55 [-0.83, 3.93] 1.28 780 .202
Racemultiracial 0.12 [-1.59, 1.82] 0.13 780 .895
RaceOther please specify 0.06 [-2.01, 2.13] 0.06 780 .954
RaceWhite 0.45 [-1.24, 2.14] 0.52 780 .604
Hispanic -0.24 [-0.47, -0.01] -2.04 780 .042
Income num z 0.08 [0.02, 0.15] 2.47 780 .014
Edu num z 0.12 [0.06, 0.19] 3.59 780 < .001
Age z 0.10 [0.03, 0.16] 3.02 780 .003
County medianincome z 0.03 [-0.03, 0.10] 1.02 780 .307
County gini z 0.06 [-0.02, 0.14] 1.49 780 .136
County density z 0.07 [0.00, 0.14] 1.85 780 .064

Exploratory analysis

Each of the values delivered scores, independently, will be inserted into a model as a predictor. This will be done for each of the four primary outcome variables separately.

For each one of these exploratory models, we will include political ideology as a moderator.

The personal disappointment score will be added to all models from the analysis plan as a control variable.

Controlling for personal disapointment

Support for radical change

m1 <- lm(change_z ~ brokenpromise_z + personaldisappoint_z + ideo_con_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-39)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.97 [-2.79, 0.85] -1.04 779 .297
Brokenpromise z -0.09 [-0.25, 0.07] -1.13 779 .260
Personaldisappoint z 0.45 [0.29, 0.61] 5.53 779 < .001
Ideo con z 0.00 [-0.08, 0.08] -0.02 779 .988
SDO z -0.15 [-0.23, -0.07] -3.66 779 < .001
TIPI agree z -0.02 [-0.09, 0.05] -0.49 779 .627
Genderwoman 0.17 [0.03, 0.30] 2.47 779 .014
RaceAsian 0.90 [-0.94, 2.74] 0.96 779 .337
RaceBlack or African American 1.00 [-0.82, 2.82] 1.08 779 .282
RaceMiddle Eastern or North African 0.51 [-2.05, 3.07] 0.39 779 .695
Racemultiracial 1.03 [-0.81, 2.87] 1.10 779 .272
RaceOther please specify 2.36 [0.13, 4.59] 2.08 779 .038
RaceWhite 0.79 [-1.02, 2.61] 0.86 779 .391
Hispanic -0.05 [-0.30, 0.20] -0.36 779 .718
Income num z -0.01 [-0.08, 0.06] -0.37 779 .712
Edu num z -0.03 [-0.10, 0.04] -0.78 779 .436
Age z -0.17 [-0.23, -0.10] -4.75 779 < .001
County medianincome z 0.00 [-0.07, 0.07] -0.04 779 .966
County gini z -0.04 [-0.12, 0.04] -0.92 779 .360
County density z 0.03 [-0.05, 0.11] 0.65 779 .513

Anti-establishment sentiment

m1 <- lm(antiest_z ~ brokenpromise_z + personaldisappoint_z + ideo_con_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-40)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.05 [-1.67, 1.77] 0.06 779 .952
Brokenpromise z -0.16 [-0.31, -0.02] -2.16 779 .031
Personaldisappoint z 0.57 [0.42, 0.73] 7.42 779 < .001
Ideo con z -0.01 [-0.08, 0.07] -0.20 779 .843
SDO z -0.07 [-0.14, 0.01] -1.77 779 .077
TIPI agree z -0.06 [-0.12, 0.01] -1.66 779 .098
Genderwoman 0.06 [-0.07, 0.18] 0.85 779 .394
RaceAsian 0.13 [-1.61, 1.87] 0.15 779 .881
RaceBlack or African American -0.28 [-2.00, 1.44] -0.32 779 .750
RaceMiddle Eastern or North African -1.87 [-4.29, 0.55] -1.52 779 .130
Racemultiracial -0.16 [-1.89, 1.58] -0.18 779 .860
RaceOther please specify 0.63 [-1.47, 2.74] 0.59 779 .555
RaceWhite -0.08 [-1.79, 1.64] -0.09 779 .929
Hispanic 0.02 [-0.21, 0.26] 0.18 779 .857
Income num z -0.05 [-0.12, 0.02] -1.42 779 .156
Edu num z -0.13 [-0.20, -0.06] -3.81 779 < .001
Age z -0.02 [-0.08, 0.05] -0.46 779 .646
County medianincome z -0.05 [-0.12, 0.01] -1.52 779 .129
County gini z -0.14 [-0.22, -0.06] -3.44 779 < .001
County density z 0.06 [-0.02, 0.13] 1.54 779 .124

Trust in democratic institutions

m1 <- lm(trust_deminst_z ~ brokenpromise_z + personaldisappoint_z + ideo_con_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-41)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 1.01 [-0.65, 2.66] 1.19 779 .233
Brokenpromise z 0.19 [0.05, 0.33] 2.63 779 .009
Personaldisappoint z -0.65 [-0.79, -0.50] -8.69 779 < .001
Ideo con z 0.02 [-0.05, 0.09] 0.59 779 .553
SDO z -0.03 [-0.10, 0.04] -0.75 779 .452
TIPI agree z 0.11 [0.05, 0.17] 3.37 779 < .001
Genderwoman -0.05 [-0.18, 0.07] -0.89 779 .376
RaceAsian -0.99 [-2.67, 0.68] -1.17 779 .244
RaceBlack or African American -0.74 [-2.39, 0.92] -0.87 779 .382
RaceMiddle Eastern or North African 0.07 [-2.26, 2.39] 0.06 779 .955
Racemultiracial -1.13 [-2.80, 0.53] -1.34 779 .182
RaceOther please specify -1.65 [-3.68, 0.37] -1.60 779 .109
RaceWhite -1.02 [-2.67, 0.63] -1.21 779 .227
Hispanic 0.06 [-0.16, 0.29] 0.55 779 .580
Income num z -0.01 [-0.07, 0.06] -0.18 779 .860
Edu num z 0.10 [0.04, 0.17] 3.11 779 .002
Age z -0.01 [-0.07, 0.05] -0.28 779 .781
County medianincome z 0.05 [-0.02, 0.11] 1.41 779 .159
County gini z 0.08 [0.00, 0.15] 1.99 779 .047
County density z 0.01 [-0.06, 0.08] 0.22 779 .823

Trust in national institutions

m1 <- lm(trust_natinst_z ~ brokenpromise_z + personaldisappoint_z + ideo_con_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-42)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.41 [-2.04, 1.22] -0.49 779 .624
Brokenpromise z 0.14 [0.00, 0.28] 2.00 779 .045
Personaldisappoint z -0.58 [-0.72, -0.43] -7.84 779 < .001
Ideo con z 0.06 [-0.01, 0.13] 1.82 779 .069
SDO z -0.01 [-0.09, 0.06] -0.39 779 .698
TIPI agree z 0.15 [0.09, 0.22] 4.81 779 < .001
Genderwoman 0.05 [-0.07, 0.17] 0.85 779 .394
RaceAsian 0.25 [-1.40, 1.90] 0.30 779 .764
RaceBlack or African American 0.63 [-1.00, 2.27] 0.76 779 .448
RaceMiddle Eastern or North African 1.44 [-0.85, 3.73] 1.23 779 .218
Racemultiracial 0.14 [-1.51, 1.79] 0.17 779 .867
RaceOther please specify -0.48 [-2.47, 1.52] -0.47 779 .639
RaceWhite 0.38 [-1.25, 2.01] 0.46 779 .648
Hispanic -0.09 [-0.32, 0.13] -0.81 779 .417
Income num z 0.07 [0.00, 0.13] 2.05 779 .041
Edu num z 0.10 [0.04, 0.17] 3.14 779 .002
Age z 0.09 [0.03, 0.16] 3.01 779 .003
County medianincome z 0.05 [-0.01, 0.11] 1.51 779 .130
County gini z 0.07 [-0.01, 0.14] 1.77 779 .078
County density z 0.05 [-0.02, 0.12] 1.42 779 .156

Breaking it by specific value

df_bsc_elg %>% 
  dplyr::select(antiest,change,trust_deminst,trust_natinst,democracy:tolerance) %>%
  corPlot(upper = TRUE,stars = TRUE,xsrt = 270)

Support for radical change

Democracy: No controls

m1 <- lm(change_z ~ democracy_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-45)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.06] 0.00 992 > .999
Democracy z -0.18 [-0.24, -0.12] -5.83 992 < .001

Democracy: With controls

m1 <- lm(change_z ~ democracy_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-46)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.81 [-2.72, 1.10] -0.83 829 .406
Democracy z -0.18 [-0.25, -0.11] -5.27 829 < .001
SDO z -0.20 [-0.27, -0.13] -5.75 829 < .001
TIPI agree z -0.06 [-0.13, 0.01] -1.75 829 .081
Genderwoman 0.24 [0.10, 0.37] 3.41 829 < .001
RaceAsian 0.77 [-1.16, 2.70] 0.78 829 .434
RaceBlack or African American 0.84 [-1.07, 2.75] 0.86 829 .389
RaceMiddle Eastern or North African 0.51 [-2.17, 3.19] 0.37 829 .710
Racemultiracial 0.92 [-1.01, 2.85] 0.94 829 .349
RaceOther please specify 1.72 [-0.61, 4.05] 1.45 829 .149
RaceWhite 0.59 [-1.32, 2.49] 0.60 829 .546
Hispanic 0.07 [-0.18, 0.32] 0.54 829 .592
Income num z -0.03 [-0.10, 0.04] -0.79 829 .429
Edu num z -0.04 [-0.11, 0.03] -1.10 829 .273
Age z -0.15 [-0.22, -0.08] -4.31 829 < .001
County medianincome z 0.00 [-0.07, 0.07] 0.08 829 .940
County gini z -0.03 [-0.12, 0.05] -0.77 829 .441
County density z 0.00 [-0.08, 0.08] 0.01 829 .996

Democracy: Interaction with ideology

m1 <- lm(change_z ~ democracy_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-47)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.01 [-0.07, 0.05] -0.28 932 .777
Democracy z -0.22 [-0.29, -0.16] -6.77 932 < .001
Ideo con z -0.21 [-0.27, -0.14] -6.46 932 < .001
Democracy z \(\times\) Ideo con z 0.02 [-0.04, 0.09] 0.67 932 .502

Equality: No controls

m1 <- lm(change_z ~ equality_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-48)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.06] 0.00 992 > .999
Equality z -0.11 [-0.17, -0.05] -3.52 992 < .001

Equality: With controls

m1 <- lm(change_z ~ equality_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-49)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -1.09 [-3.02, 0.84] -1.11 829 .267
Equality z -0.10 [-0.17, -0.03] -2.87 829 .004
SDO z -0.18 [-0.25, -0.11] -5.21 829 < .001
TIPI agree z -0.07 [-0.14, 0.00] -2.01 829 .044
Genderwoman 0.24 [0.10, 0.38] 3.43 829 < .001
RaceAsian 1.02 [-0.93, 2.97] 1.03 829 .305
RaceBlack or African American 1.11 [-0.82, 3.04] 1.13 829 .258
RaceMiddle Eastern or North African 0.77 [-1.94, 3.48] 0.56 829 .575
Racemultiracial 1.23 [-0.72, 3.17] 1.24 829 .216
RaceOther please specify 2.14 [-0.21, 4.50] 1.79 829 .074
RaceWhite 0.87 [-1.06, 2.79] 0.88 829 .378
Hispanic 0.07 [-0.19, 0.32] 0.50 829 .617
Income num z -0.03 [-0.10, 0.04] -0.78 829 .436
Edu num z -0.06 [-0.14, 0.01] -1.67 829 .096
Age z -0.16 [-0.23, -0.09] -4.50 829 < .001
County medianincome z 0.01 [-0.07, 0.08] 0.17 829 .865
County gini z -0.03 [-0.12, 0.05] -0.80 829 .421
County density z -0.01 [-0.09, 0.08] -0.13 829 .897

Equality: Interaction with ideology

m1 <- lm(change_z ~ equality_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-50)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.02 [-0.09, 0.04] -0.72 932 .472
Equality z -0.10 [-0.16, -0.03] -2.97 932 .003
Ideo con z -0.17 [-0.24, -0.11] -5.28 932 < .001
Equality z \(\times\) Ideo con z 0.10 [0.03, 0.16] 2.96 932 .003 
interact_plot(m1,
              pred = "equality_z",
              modx = "ideo_con_z")

Freedom: No controls

m1 <- lm(change_z ~ freedom_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-52)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.06] -0.04 991 .967
Freedom z -0.24 [-0.30, -0.18] -7.91 991 < .001

Freedom: With controls

m1 <- lm(change_z ~ freedom_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-53)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.76 [-2.64, 1.12] -0.79 828 .428
Freedom z -0.24 [-0.31, -0.18] -7.30 828 < .001
SDO z -0.17 [-0.24, -0.10] -4.85 828 < .001
TIPI agree z -0.05 [-0.12, 0.02] -1.52 828 .130
Genderwoman 0.24 [0.10, 0.37] 3.50 828 < .001
RaceAsian 0.70 [-1.20, 2.60] 0.72 828 .469
RaceBlack or African American 0.75 [-1.13, 2.63] 0.78 828 .434
RaceMiddle Eastern or North African 0.42 [-2.22, 3.06] 0.31 828 .755
Racemultiracial 0.89 [-1.01, 2.78] 0.92 828 .358
RaceOther please specify 2.13 [-0.16, 4.42] 1.82 828 .068
RaceWhite 0.54 [-1.34, 2.42] 0.57 828 .572
Hispanic 0.06 [-0.19, 0.31] 0.50 828 .618
Income num z -0.02 [-0.09, 0.06] -0.46 828 .642
Edu num z -0.08 [-0.15, -0.01] -2.30 828 .022
Age z -0.16 [-0.23, -0.10] -4.72 828 < .001
County medianincome z 0.01 [-0.06, 0.08] 0.16 828 .875
County gini z -0.04 [-0.12, 0.04] -0.91 828 .362
County density z 0.00 [-0.08, 0.08] 0.09 828 .931

Freedom: Interaction with ideology

m1 <- lm(change_z ~ freedom_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-54)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.01 [-0.08, 0.05] -0.44 932 .658
Freedom z -0.22 [-0.29, -0.16] -6.89 932 < .001
Ideo con z -0.15 [-0.22, -0.09] -4.78 932 < .001
Freedom z \(\times\) Ideo con z 0.02 [-0.04, 0.08] 0.75 932 .451

Individualism: No controls

m1 <- lm(change_z ~ individualism_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-55)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.06] -0.04 991 .968
Individualism z 0.00 [-0.07, 0.06] -0.10 991 .921

Individualism: With controls

m1 <- lm(change_z ~ individualism_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-56)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -1.14 [-3.07, 0.80] -1.15 828 .249
Individualism z -0.02 [-0.09, 0.05] -0.56 828 .575
SDO z -0.19 [-0.26, -0.12] -5.24 828 < .001
TIPI agree z -0.08 [-0.15, -0.01] -2.34 828 .020
Genderwoman 0.25 [0.11, 0.39] 3.51 828 < .001
RaceAsian 1.07 [-0.89, 3.02] 1.07 828 .284
RaceBlack or African American 1.16 [-0.78, 3.09] 1.17 828 .242
RaceMiddle Eastern or North African 0.90 [-1.82, 3.62] 0.65 828 .518
Racemultiracial 1.27 [-0.68, 3.23] 1.28 828 .201
RaceOther please specify 2.09 [-0.28, 4.45] 1.73 828 .083
RaceWhite 0.90 [-1.03, 2.83] 0.92 828 .360
Hispanic 0.10 [-0.15, 0.36] 0.80 828 .426
Income num z -0.03 [-0.10, 0.04] -0.78 828 .436
Edu num z -0.07 [-0.14, 0.00] -1.87 828 .062
Age z -0.16 [-0.23, -0.08] -4.32 828 < .001
County medianincome z 0.01 [-0.06, 0.08] 0.23 828 .816
County gini z -0.03 [-0.12, 0.05] -0.78 828 .435
County density z -0.01 [-0.10, 0.07] -0.27 828 .790

Individualism: Interaction with ideology

m1 <- lm(change_z ~ individualism_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-57)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.01 [-0.08, 0.05] -0.36 932 .718
Individualism z 0.00 [-0.07, 0.06] -0.14 932 .888
Ideo con z -0.19 [-0.25, -0.12] -5.78 932 < .001
Individualism z \(\times\) Ideo con z -0.01 [-0.06, 0.04] -0.39 932 .693

Justice: No controls

m1 <- lm(change_z ~ justice_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-58)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.06] -0.06 990 .950
Justice z -0.29 [-0.35, -0.24] -9.71 990 < .001

Justice: With controls

m1 <- lm(change_z ~ justice_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-59)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -1.36 [-3.23, 0.51] -1.43 827 .153
Justice z -0.27 [-0.33, -0.20] -7.92 827 < .001
SDO z -0.16 [-0.23, -0.10] -4.78 827 < .001
TIPI agree z -0.03 [-0.10, 0.04] -0.87 827 .384
Genderwoman 0.17 [0.04, 0.31] 2.50 827 .013
RaceAsian 1.28 [-0.61, 3.17] 1.33 827 .184
RaceBlack or African American 1.44 [-0.43, 3.31] 1.51 827 .132
RaceMiddle Eastern or North African 1.29 [-1.34, 3.92] 0.96 827 .336
Racemultiracial 1.45 [-0.43, 3.33] 1.51 827 .131
RaceOther please specify 2.12 [-0.16, 4.40] 1.82 827 .069
RaceWhite 1.18 [-0.68, 3.05] 1.24 827 .214
Hispanic 0.03 [-0.22, 0.28] 0.23 827 .822
Income num z -0.02 [-0.09, 0.05] -0.60 827 .551
Edu num z -0.05 [-0.12, 0.02] -1.45 827 .148
Age z -0.15 [-0.22, -0.08] -4.41 827 < .001
County medianincome z 0.01 [-0.06, 0.08] 0.38 827 .707
County gini z -0.03 [-0.11, 0.06] -0.61 827 .541
County density z 0.00 [-0.09, 0.08] -0.12 827 .908

Justice: Interaction with ideology

m1 <- lm(change_z ~ justice_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-60)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.02 [-0.08, 0.04] -0.65 931 .515
Justice z -0.28 [-0.35, -0.22] -8.82 931 < .001
Ideo con z -0.15 [-0.21, -0.08] -4.62 931 < .001
Justice z \(\times\) Ideo con z 0.05 [-0.02, 0.11] 1.46 931 .144

Pursuit of Happiness: No controls

m1 <- lm(change_z ~ happiness_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-61)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.06] -0.06 990 .951
Happiness z -0.22 [-0.28, -0.16] -6.96 990 < .001

Pursuit of Happiness: With controls

m1 <- lm(change_z ~ happiness_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-62)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -1.23 [-3.12, 0.67] -1.27 828 .205
Happiness z -0.20 [-0.26, -0.13] -5.69 828 < .001
SDO z -0.16 [-0.23, -0.09] -4.61 828 < .001
TIPI agree z -0.07 [-0.14, 0.00] -1.85 828 .065
Genderwoman 0.21 [0.07, 0.34] 3.00 828 .003
RaceAsian 1.18 [-0.73, 3.10] 1.21 828 .226
RaceBlack or African American 1.26 [-0.64, 3.16] 1.30 828 .194
RaceMiddle Eastern or North African 0.94 [-1.73, 3.61] 0.69 828 .491
Racemultiracial 1.37 [-0.55, 3.28] 1.40 828 .162
RaceOther please specify 2.07 [-0.25, 4.39] 1.76 828 .080
RaceWhite 1.02 [-0.88, 2.92] 1.06 828 .291
Hispanic 0.05 [-0.21, 0.30] 0.36 828 .717
Income num z -0.02 [-0.09, 0.05] -0.51 828 .607
Edu num z -0.08 [-0.15, 0.00] -2.05 828 .041
Age z -0.15 [-0.22, -0.08] -4.33 828 < .001
County medianincome z 0.00 [-0.07, 0.07] 0.06 828 .951
County gini z -0.03 [-0.12, 0.05] -0.74 828 .461
County density z -0.01 [-0.09, 0.08] -0.13 828 .899

Pursuit of Happiness: Interaction with ideology

m1 <- lm(change_z ~ happiness_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-63)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.07, 0.06] -0.11 931 .912
Happiness z -0.18 [-0.25, -0.12] -5.57 931 < .001
Ideo con z -0.15 [-0.22, -0.09] -4.73 931 < .001
Happiness z \(\times\) Ideo con z -0.04 [-0.10, 0.02] -1.27 931 .205

Right to bear arms: No controls

m1 <- lm(change_z ~ arms_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-64)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.06] -0.02 991 .984
Arms z 0.08 [0.02, 0.14] 2.51 991 .012

Right to bear arms: With controls

m1 <- lm(change_z ~ arms_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-65)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -1.11 [-3.04, 0.83] -1.12 828 .262
Arms z 0.04 [-0.02, 0.11] 1.28 828 .200
SDO z -0.19 [-0.26, -0.12] -5.22 828 < .001
TIPI agree z -0.08 [-0.15, -0.01] -2.33 828 .020
Genderwoman 0.25 [0.11, 0.39] 3.57 828 < .001
RaceAsian 1.04 [-0.91, 3.00] 1.05 828 .296
RaceBlack or African American 1.12 [-0.81, 3.06] 1.14 828 .256
RaceMiddle Eastern or North African 0.91 [-1.81, 3.62] 0.65 828 .513
Racemultiracial 1.23 [-0.72, 3.18] 1.24 828 .215
RaceOther please specify 2.08 [-0.28, 4.45] 1.73 828 .084
RaceWhite 0.87 [-1.06, 2.80] 0.89 828 .376
Hispanic 0.08 [-0.18, 0.34] 0.60 828 .551
Income num z -0.03 [-0.10, 0.05] -0.71 828 .480
Edu num z -0.07 [-0.14, 0.01] -1.82 828 .069
Age z -0.16 [-0.23, -0.09] -4.36 828 < .001
County medianincome z 0.01 [-0.07, 0.08] 0.18 828 .854
County gini z -0.04 [-0.13, 0.04] -0.94 828 .349
County density z -0.01 [-0.09, 0.08] -0.15 828 .884

Right to bear arms: Interaction with ideology

m1 <- lm(change_z ~ arms_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-66)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.02 [-0.08, 0.04] -0.59 931 .556
Arms z 0.02 [-0.05, 0.09] 0.56 931 .578
Ideo con z -0.18 [-0.25, -0.12] -5.72 931 < .001
Arms z \(\times\) Ideo con z -0.12 [-0.19, -0.05] -3.48 931 < .001 
interact_plot(m1,
              pred = "arms_z",
              modx = "ideo_con_z")

Tolerance: No controls

m1 <- lm(change_z ~ tolerance_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-68)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.06] -0.04 991 .968
Tolerance z -0.10 [-0.16, -0.03] -3.05 991 .002

Tolerance: With controls

m1 <- lm(change_z ~ tolerance_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-69)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -1.22 [-3.13, 0.69] -1.26 828 .209
Tolerance z -0.20 [-0.28, -0.11] -4.64 828 < .001
SDO z -0.18 [-0.25, -0.11] -5.18 828 < .001
TIPI agree z -0.08 [-0.15, -0.01] -2.15 828 .032
Genderwoman 0.23 [0.09, 0.37] 3.32 828 < .001
RaceAsian 1.14 [-0.80, 3.07] 1.15 828 .249
RaceBlack or African American 1.25 [-0.66, 3.16] 1.28 828 .200
RaceMiddle Eastern or North African 0.99 [-1.70, 3.67] 0.72 828 .471
Racemultiracial 1.34 [-0.58, 3.27] 1.37 828 .171
RaceOther please specify 2.10 [-0.23, 4.44] 1.77 828 .077
RaceWhite 1.00 [-0.91, 2.91] 1.02 828 .306
Hispanic 0.07 [-0.19, 0.32] 0.51 828 .610
Income num z -0.02 [-0.10, 0.05] -0.63 828 .531
Edu num z -0.07 [-0.14, 0.01] -1.79 828 .074
Age z -0.16 [-0.23, -0.09] -4.60 828 < .001
County medianincome z 0.01 [-0.06, 0.08] 0.30 828 .768
County gini z -0.04 [-0.12, 0.05] -0.86 828 .389
County density z 0.00 [-0.08, 0.08] 0.01 828 .989

Tolerance: Interaction with ideology

m1 <- lm(change_z ~ tolerance_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-70)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.01 [-0.07, 0.05] -0.31 932 .753
Tolerance z -0.11 [-0.19, -0.03] -2.69 932 .007
Ideo con z -0.18 [-0.25, -0.12] -5.72 932 < .001
Tolerance z \(\times\) Ideo con z -0.03 [-0.09, 0.04] -0.84 932 .403

Anti-establishment sentiment

Democracy: No controls

m1 <- lm(antiest_z ~ democracy_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-71)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.06] 0.00 992 > .999
Democracy z -0.29 [-0.35, -0.23] -9.62 992 < .001

Democracy: With controls

m1 <- lm(antiest_z ~ democracy_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-72)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.33 [-1.49, 2.15] 0.36 829 .722
Democracy z -0.25 [-0.32, -0.19] -7.73 829 < .001
SDO z -0.14 [-0.21, -0.08] -4.22 829 < .001
TIPI agree z -0.10 [-0.17, -0.04] -3.01 829 .003
Genderwoman 0.13 [0.00, 0.26] 2.02 829 .044
RaceAsian -0.11 [-1.95, 1.73] -0.11 829 .909
RaceBlack or African American -0.56 [-2.38, 1.27] -0.60 829 .549
RaceMiddle Eastern or North African -2.04 [-4.60, 0.52] -1.56 829 .119
Racemultiracial -0.38 [-2.22, 1.46] -0.41 829 .684
RaceOther please specify -0.25 [-2.47, 1.98] -0.22 829 .829
RaceWhite -0.42 [-2.23, 1.40] -0.45 829 .654
Hispanic 0.15 [-0.09, 0.39] 1.23 829 .218
Income num z -0.07 [-0.14, -0.01] -2.11 829 .035
Edu num z -0.12 [-0.19, -0.05] -3.41 829 < .001
Age z 0.00 [-0.07, 0.06] -0.07 829 .943
County medianincome z -0.04 [-0.11, 0.03] -1.11 829 .269
County gini z -0.12 [-0.20, -0.04] -3.00 829 .003
County density z 0.02 [-0.05, 0.10] 0.59 829 .552

Democracy: Interaction with ideology

m1 <- lm(antiest_z ~ democracy_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-73)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.01 [-0.08, 0.05] -0.48 932 .634
Democracy z -0.34 [-0.40, -0.27] -10.57 932 < .001
Ideo con z -0.16 [-0.22, -0.10] -5.21 932 < .001
Democracy z \(\times\) Ideo con z -0.04 [-0.10, 0.02] -1.31 932 .190

Equality: No controls

m1 <- lm(antiest_z ~ equality_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-74)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.06] 0.00 992 > .999
Equality z -0.23 [-0.29, -0.17] -7.54 992 < .001

Equality: With controls

m1 <- lm(antiest_z ~ equality_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-75)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.04 [-1.87, 1.80] -0.04 829 .969
Equality z -0.22 [-0.28, -0.15] -6.57 829 < .001
SDO z -0.11 [-0.18, -0.05] -3.40 829 < .001
TIPI agree z -0.11 [-0.18, -0.04] -3.14 829 .002
Genderwoman 0.13 [0.00, 0.26] 2.00 829 .046
RaceAsian 0.21 [-1.65, 2.06] 0.22 829 .826
RaceBlack or African American -0.20 [-2.04, 1.64] -0.21 829 .831
RaceMiddle Eastern or North African -1.77 [-4.35, 0.81] -1.35 829 .178
Racemultiracial 0.02 [-1.83, 1.87] 0.02 829 .984
RaceOther please specify 0.40 [-1.84, 2.64] 0.35 829 .728
RaceWhite -0.05 [-1.88, 1.79] -0.05 829 .959
Hispanic 0.12 [-0.12, 0.37] 0.98 829 .328
Income num z -0.08 [-0.15, 0.00] -2.10 829 .036
Edu num z -0.14 [-0.21, -0.07] -4.03 829 < .001
Age z -0.02 [-0.09, 0.05] -0.64 829 .522
County medianincome z -0.03 [-0.10, 0.04] -0.93 829 .351
County gini z -0.12 [-0.20, -0.04] -2.97 829 .003
County density z 0.02 [-0.06, 0.10] 0.46 829 .646

Equality: Interaction with ideology

m1 <- lm(antiest_z ~ equality_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-76)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.01 [-0.08, 0.05] -0.38 932 .702
Equality z -0.21 [-0.28, -0.15] -6.46 932 < .001
Ideo con z -0.10 [-0.17, -0.04] -3.21 932 .001
Equality z \(\times\) Ideo con z 0.01 [-0.05, 0.07] 0.33 932 .745

Freedom: No controls

m1 <- lm(antiest_z ~ freedom_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-77)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.06] -0.05 991 .957
Freedom z -0.21 [-0.27, -0.15] -6.67 991 < .001

Freedom: With controls

m1 <- lm(antiest_z ~ freedom_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-78)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.19 [-1.65, 2.03] 0.21 828 .836
Freedom z -0.21 [-0.27, -0.14] -6.30 828 < .001
SDO z -0.10 [-0.17, -0.04] -3.08 828 .002
TIPI agree z -0.11 [-0.17, -0.04] -3.10 828 .002
Genderwoman 0.14 [0.01, 0.27] 2.12 828 .035
RaceAsian 0.00 [-1.86, 1.86] 0.00 828 > .999
RaceBlack or African American -0.46 [-2.30, 1.38] -0.49 828 .624
RaceMiddle Eastern or North African -1.89 [-4.48, 0.70] -1.43 828 .152
Racemultiracial -0.21 [-2.07, 1.64] -0.23 828 .822
RaceOther please specify 0.31 [-1.94, 2.55] 0.27 828 .789
RaceWhite -0.28 [-2.12, 1.55] -0.30 828 .762
Hispanic 0.16 [-0.08, 0.41] 1.31 828 .191
Income num z -0.06 [-0.13, 0.01] -1.78 828 .076
Edu num z -0.17 [-0.24, -0.10] -4.90 828 < .001
Age z -0.01 [-0.08, 0.05] -0.38 828 .702
County medianincome z -0.03 [-0.10, 0.03] -0.98 828 .327
County gini z -0.13 [-0.21, -0.05] -3.15 828 .002
County density z 0.02 [-0.06, 0.10] 0.53 828 .598

Freedom: Interaction with ideology

m1 <- lm(antiest_z ~ freedom_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-79)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.02 [-0.08, 0.04] -0.61 932 .543
Freedom z -0.19 [-0.26, -0.13] -5.93 932 < .001
Ideo con z -0.10 [-0.17, -0.04] -3.13 932 .002
Freedom z \(\times\) Ideo con z 0.05 [-0.01, 0.11] 1.71 932 .087

Individualism: No controls

m1 <- lm(antiest_z ~ individualism_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-80)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.06] -0.05 991 .958
Individualism z -0.01 [-0.07, 0.06] -0.17 991 .863

Individualism: With controls

m1 <- lm(antiest_z ~ individualism_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-81)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.12 [-2.00, 1.76] -0.13 828 .898
Individualism z 0.00 [-0.07, 0.06] -0.11 828 .916
SDO z -0.12 [-0.19, -0.05] -3.45 828 < .001
TIPI agree z -0.13 [-0.20, -0.06] -3.78 828 < .001
Genderwoman 0.15 [0.02, 0.29] 2.20 828 .028
RaceAsian 0.31 [-1.59, 2.21] 0.32 828 .750
RaceBlack or African American -0.12 [-2.01, 1.76] -0.13 828 .900
RaceMiddle Eastern or North African -1.48 [-4.13, 1.16] -1.10 828 .272
Racemultiracial 0.11 [-1.79, 2.01] 0.11 828 .909
RaceOther please specify 0.27 [-2.03, 2.57] 0.23 828 .819
RaceWhite 0.02 [-1.86, 1.90] 0.02 828 .987
Hispanic 0.19 [-0.06, 0.45] 1.52 828 .130
Income num z -0.07 [-0.15, 0.00] -2.00 828 .046
Edu num z -0.16 [-0.23, -0.09] -4.50 828 < .001
Age z 0.00 [-0.07, 0.06] -0.13 828 .895
County medianincome z -0.03 [-0.10, 0.04] -0.91 828 .361
County gini z -0.13 [-0.21, -0.04] -3.00 828 .003
County density z 0.01 [-0.07, 0.09] 0.22 828 .827

Individualism: Interaction with ideology

m1 <- lm(antiest_z ~ individualism_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-82)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.01 [-0.08, 0.05] -0.39 932 .696
Individualism z 0.00 [-0.07, 0.06] -0.12 932 .902
Ideo con z -0.13 [-0.20, -0.07] -4.01 932 < .001
Individualism z \(\times\) Ideo con z -0.02 [-0.07, 0.03] -0.73 932 .466

Justice: No controls

m1 <- lm(antiest_z ~ justice_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-83)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.05] -0.10 990 .916
Justice z -0.36 [-0.42, -0.30] -12.22 990 < .001

Justice: With controls

m1 <- lm(antiest_z ~ justice_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-84)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.40 [-2.18, 1.39] -0.44 827 .663
Justice z -0.31 [-0.38, -0.25] -9.76 827 < .001
SDO z -0.09 [-0.16, -0.03] -2.88 827 .004
TIPI agree z -0.07 [-0.14, 0.00] -2.03 827 .042
Genderwoman 0.05 [-0.07, 0.18] 0.84 827 .404
RaceAsian 0.56 [-1.24, 2.36] 0.61 827 .540
RaceBlack or African American 0.21 [-1.57, 2.00] 0.23 827 .815
RaceMiddle Eastern or North African -1.02 [-3.53, 1.48] -0.80 827 .424
Racemultiracial 0.32 [-1.47, 2.12] 0.35 827 .723
RaceOther please specify 0.32 [-1.86, 2.49] 0.29 827 .775
RaceWhite 0.36 [-1.42, 2.14] 0.40 827 .693
Hispanic 0.11 [-0.13, 0.35] 0.93 827 .354
Income num z -0.06 [-0.13, 0.01] -1.82 827 .070
Edu num z -0.14 [-0.21, -0.08] -4.17 827 < .001
Age z 0.00 [-0.07, 0.06] -0.05 827 .962
County medianincome z -0.03 [-0.09, 0.04] -0.75 827 .453
County gini z -0.11 [-0.19, -0.04] -2.85 827 .005
County density z 0.02 [-0.06, 0.09] 0.41 827 .684

Justice: Interaction with ideology

m1 <- lm(antiest_z ~ justice_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-85)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.02 [-0.08, 0.04] -0.67 931 .502
Justice z -0.36 [-0.43, -0.30] -11.49 931 < .001
Ideo con z -0.08 [-0.14, -0.02] -2.53 931 .012
Justice z \(\times\) Ideo con z 0.03 [-0.03, 0.09] 1.02 931 .310

Pursuit of Happiness: No controls

m1 <- lm(antiest_z ~ happiness_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-86)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.06] -0.07 990 .943
Happiness z -0.19 [-0.25, -0.12] -5.95 990 < .001

Pursuit of Happiness: With controls

m1 <- lm(antiest_z ~ happiness_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-87)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.21 [-2.06, 1.63] -0.23 828 .820
Happiness z -0.19 [-0.26, -0.13] -5.75 828 < .001
SDO z -0.10 [-0.16, -0.03] -2.82 828 .005
TIPI agree z -0.11 [-0.18, -0.05] -3.33 828 < .001
Genderwoman 0.11 [-0.02, 0.24] 1.63 828 .103
RaceAsian 0.42 [-1.44, 2.29] 0.45 828 .655
RaceBlack or African American -0.02 [-1.86, 1.83] -0.02 828 .987
RaceMiddle Eastern or North African -1.45 [-4.04, 1.15] -1.10 828 .273
Racemultiracial 0.20 [-1.66, 2.07] 0.22 828 .830
RaceOther please specify 0.26 [-2.00, 2.51] 0.22 828 .822
RaceWhite 0.14 [-1.71, 1.98] 0.15 828 .883
Hispanic 0.14 [-0.10, 0.39] 1.13 828 .258
Income num z -0.06 [-0.14, 0.01] -1.78 828 .075
Edu num z -0.17 [-0.24, -0.10] -4.71 828 < .001
Age z 0.00 [-0.07, 0.06] -0.09 828 .928
County medianincome z -0.04 [-0.11, 0.03] -1.08 828 .282
County gini z -0.12 [-0.21, -0.04] -2.98 828 .003
County density z 0.01 [-0.07, 0.09] 0.36 828 .719

Pursuit of Happiness: Interaction with ideology

m1 <- lm(antiest_z ~ happiness_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-88)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.01 [-0.07, 0.06] -0.27 931 .785
Happiness z -0.16 [-0.22, -0.09] -4.66 931 < .001
Ideo con z -0.10 [-0.17, -0.04] -3.11 931 .002
Happiness z \(\times\) Ideo con z -0.01 [-0.08, 0.05] -0.47 931 .641

Right to bear arms: No controls

m1 <- lm(antiest_z ~ arms_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-89)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.06] -0.02 991 .980
Arms z 0.12 [0.06, 0.19] 3.91 991 < .001

Right to bear arms: With controls

m1 <- lm(antiest_z ~ arms_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-90)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.08 [-1.95, 1.80] -0.08 828 .936
Arms z 0.08 [0.02, 0.15] 2.53 828 .012
SDO z -0.12 [-0.19, -0.05] -3.45 828 < .001
TIPI agree z -0.13 [-0.20, -0.06] -3.77 828 < .001
Genderwoman 0.15 [0.02, 0.28] 2.21 828 .027
RaceAsian 0.27 [-1.63, 2.16] 0.28 828 .782
RaceBlack or African American -0.17 [-2.05, 1.71] -0.18 828 .858
RaceMiddle Eastern or North African -1.48 [-4.12, 1.15] -1.10 828 .270
Racemultiracial 0.04 [-1.85, 1.93] 0.04 828 .965
RaceOther please specify 0.28 [-2.01, 2.57] 0.24 828 .812
RaceWhite -0.02 [-1.90, 1.85] -0.02 828 .981
Hispanic 0.16 [-0.10, 0.41] 1.21 828 .226
Income num z -0.07 [-0.14, 0.00] -1.93 828 .054
Edu num z -0.16 [-0.23, -0.09] -4.31 828 < .001
Age z -0.01 [-0.08, 0.06] -0.22 828 .828
County medianincome z -0.03 [-0.10, 0.04] -0.91 828 .364
County gini z -0.13 [-0.22, -0.05] -3.13 828 .002
County density z 0.01 [-0.07, 0.10] 0.35 828 .724

Right to bear arms: Interaction with ideology

m1 <- lm(antiest_z ~ arms_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-91)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.01 [-0.08, 0.05] -0.46 931 .649
Arms z 0.11 [0.04, 0.18] 3.11 931 .002
Ideo con z -0.12 [-0.19, -0.06] -3.84 931 < .001
Arms z \(\times\) Ideo con z -0.02 [-0.09, 0.05] -0.48 931 .634

Tolerance: No controls

m1 <- lm(antiest_z ~ tolerance_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-92)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.06] -0.05 991 .958
Tolerance z -0.14 [-0.20, -0.08] -4.42 991 < .001

Tolerance: With controls

m1 <- lm(antiest_z ~ tolerance_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-93)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.23 [-2.08, 1.61] -0.25 828 .804
Tolerance z -0.24 [-0.32, -0.16] -5.92 828 < .001
SDO z -0.12 [-0.18, -0.05] -3.40 828 < .001
TIPI agree z -0.12 [-0.19, -0.06] -3.61 828 < .001
Genderwoman 0.13 [0.00, 0.26] 1.90 828 .058
RaceAsian 0.39 [-1.47, 2.26] 0.42 828 .678
RaceBlack or African American 0.00 [-1.85, 1.85] 0.00 828 .999
RaceMiddle Eastern or North African -1.38 [-3.97, 1.21] -1.04 828 .296
Racemultiracial 0.20 [-1.66, 2.06] 0.21 828 .832
RaceOther please specify 0.29 [-1.96, 2.54] 0.25 828 .800
RaceWhite 0.14 [-1.70, 1.98] 0.15 828 .881
Hispanic 0.15 [-0.09, 0.40] 1.22 828 .222
Income num z -0.07 [-0.14, 0.00] -1.87 828 .062
Edu num z -0.16 [-0.23, -0.09] -4.43 828 < .001
Age z -0.02 [-0.08, 0.05] -0.46 828 .646
County medianincome z -0.03 [-0.10, 0.04] -0.81 828 .417
County gini z -0.13 [-0.21, -0.05] -3.12 828 .002
County density z 0.02 [-0.06, 0.10] 0.57 828 .567

Tolerance: Interaction with ideology

m1 <- lm(antiest_z ~ tolerance_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-94)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.01 [-0.07, 0.05] -0.25 932 .799
Tolerance z -0.23 [-0.31, -0.15] -5.61 932 < .001
Ideo con z -0.12 [-0.19, -0.06] -3.86 932 < .001
Tolerance z \(\times\) Ideo con z -0.13 [-0.19, -0.06] -3.79 932 < .001 
interact_plot(m1,
              pred = "tolerance_z",
              modx = "ideo_con_z")

Trust in democratic institutions

Democracy: No controls

m1 <- lm(trust_deminst_z ~ democracy_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-96)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.06] 0.00 992 > .999
Democracy z 0.33 [0.27, 0.39] 11.06 992 < .001

Democracy: With controls

m1 <- lm(trust_deminst_z ~ democracy_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-97)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.73 [-1.09, 2.55] 0.79 829 .431
Democracy z 0.26 [0.20, 0.33] 8.13 829 < .001
SDO z 0.06 [-0.01, 0.12] 1.70 829 .089
TIPI agree z 0.16 [0.10, 0.23] 4.77 829 < .001
Genderwoman -0.15 [-0.28, -0.02] -2.26 829 .024
RaceAsian -0.76 [-2.60, 1.08] -0.81 829 .418
RaceBlack or African American -0.44 [-2.26, 1.38] -0.47 829 .637
RaceMiddle Eastern or North African 0.23 [-2.33, 2.78] 0.17 829 .862
Racemultiracial -0.91 [-2.74, 0.93] -0.97 829 .331
RaceOther please specify -0.71 [-2.93, 1.51] -0.63 829 .531
RaceWhite -0.66 [-2.47, 1.16] -0.71 829 .476
Hispanic -0.09 [-0.33, 0.15] -0.75 829 .454
Income num z 0.02 [-0.05, 0.09] 0.61 829 .544
Edu num z 0.09 [0.02, 0.16] 2.65 829 .008
Age z -0.01 [-0.08, 0.05] -0.33 829 .744
County medianincome z 0.04 [-0.03, 0.10] 1.04 829 .296
County gini z 0.06 [-0.02, 0.15] 1.59 829 .113
County density z 0.04 [-0.04, 0.12] 1.07 829 .285

Democracy: Interaction with ideology

m1 <- lm(trust_deminst_z ~ democracy_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-98)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.06] 0.09 932 .927
Democracy z 0.40 [0.34, 0.46] 12.88 932 < .001
Ideo con z 0.13 [0.08, 0.19] 4.48 932 < .001
Democracy z \(\times\) Ideo con z 0.07 [0.01, 0.13] 2.41 932 .016 
interact_plot(m1,
              pred = "democracy_z",
              modx = "ideo_con_z")

Equality: No controls

m1 <- lm(trust_deminst_z ~ equality_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-100)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.06] 0.00 992 > .999
Equality z 0.31 [0.25, 0.37] 10.20 992 < .001

Equality: With controls

m1 <- lm(trust_deminst_z ~ equality_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-101)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 1.09 [-0.71, 2.90] 1.19 829 .235
Equality z 0.28 [0.22, 0.35] 8.79 829 < .001
SDO z 0.03 [-0.04, 0.09] 0.83 829 .408
TIPI agree z 0.16 [0.10, 0.23] 4.78 829 < .001
Genderwoman -0.14 [-0.27, -0.02] -2.21 829 .027
RaceAsian -1.06 [-2.89, 0.76] -1.14 829 .253
RaceBlack or African American -0.79 [-2.60, 1.01] -0.86 829 .389
RaceMiddle Eastern or North African 0.03 [-2.51, 2.56] 0.02 829 .984
Racemultiracial -1.31 [-3.13, 0.51] -1.41 829 .159
RaceOther please specify -1.42 [-3.62, 0.78] -1.26 829 .207
RaceWhite -1.03 [-2.83, 0.77] -1.12 829 .263
Hispanic -0.04 [-0.28, 0.20] -0.34 829 .732
Income num z 0.02 [-0.05, 0.09] 0.63 829 .526
Edu num z 0.11 [0.04, 0.18] 3.20 829 .001
Age z 0.01 [-0.05, 0.08] 0.43 829 .667
County medianincome z 0.03 [-0.04, 0.10] 0.86 829 .389
County gini z 0.06 [-0.02, 0.14] 1.54 829 .125
County density z 0.05 [-0.03, 0.12] 1.16 829 .245

Equality: Interaction with ideology

m1 <- lm(trust_deminst_z ~ equality_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-102)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.06] -0.03 932 .978
Equality z 0.30 [0.24, 0.36] 9.42 932 < .001
Ideo con z 0.06 [0.00, 0.12] 1.86 932 .063
Equality z \(\times\) Ideo con z -0.03 [-0.09, 0.04] -0.81 932 .417

Freedom: No controls

m1 <- lm(trust_deminst_z ~ freedom_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-103)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.06] 0.03 991 .975
Freedom z 0.26 [0.20, 0.32] 8.49 991 < .001

Freedom: With controls

m1 <- lm(trust_deminst_z ~ freedom_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-104)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.80 [-1.01, 2.62] 0.87 828 .386
Freedom z 0.26 [0.20, 0.32] 8.01 828 < .001
SDO z 0.02 [-0.05, 0.08] 0.47 828 .639
TIPI agree z 0.16 [0.09, 0.23] 4.75 828 < .001
Genderwoman -0.15 [-0.28, -0.02] -2.32 828 .021
RaceAsian -0.81 [-2.64, 1.03] -0.86 828 .390
RaceBlack or African American -0.47 [-2.29, 1.35] -0.50 828 .614
RaceMiddle Eastern or North African 0.16 [-2.40, 2.71] 0.12 828 .905
Racemultiracial -1.02 [-2.85, 0.82] -1.09 828 .277
RaceOther please specify -1.30 [-3.51, 0.92] -1.15 828 .252
RaceWhite -0.73 [-2.55, 1.08] -0.79 828 .428
Hispanic -0.10 [-0.34, 0.14] -0.79 828 .430
Income num z 0.01 [-0.06, 0.08] 0.23 828 .817
Edu num z 0.15 [0.08, 0.22] 4.34 828 < .001
Age z 0.00 [-0.06, 0.07] 0.04 828 .968
County medianincome z 0.03 [-0.03, 0.10] 0.95 828 .341
County gini z 0.07 [-0.01, 0.16] 1.83 828 .068
County density z 0.04 [-0.04, 0.12] 1.05 828 .296

Freedom: Interaction with ideology

m1 <- lm(trust_deminst_z ~ freedom_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-105)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.01 [-0.05, 0.08] 0.47 932 .637
Freedom z 0.26 [0.20, 0.32] 8.08 932 < .001
Ideo con z 0.06 [0.00, 0.12] 1.86 932 .063
Freedom z \(\times\) Ideo con z -0.12 [-0.18, -0.06] -3.90 932 < .001 
interact_plot(m1,
              pred = "freedom_z",
              modx = "ideo_con_z")

Individualism: No controls

m1 <- lm(trust_deminst_z ~ individualism_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-107)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.06] 0.03 991 .976
Individualism z 0.04 [-0.03, 0.10] 1.17 991 .244

Individualism: With controls

m1 <- lm(trust_deminst_z ~ individualism_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-108)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 1.21 [-0.68, 3.09] 1.26 828 .209
Individualism z 0.02 [-0.05, 0.09] 0.62 828 .533
SDO z 0.04 [-0.03, 0.10] 1.03 828 .303
TIPI agree z 0.19 [0.13, 0.26] 5.54 828 < .001
Genderwoman -0.16 [-0.30, -0.03] -2.36 828 .018
RaceAsian -1.20 [-3.10, 0.71] -1.23 828 .218
RaceBlack or African American -0.90 [-2.79, 0.99] -0.93 828 .350
RaceMiddle Eastern or North African -0.35 [-3.00, 2.30] -0.26 828 .794
Racemultiracial -1.43 [-3.33, 0.48] -1.47 828 .142
RaceOther please specify -1.25 [-3.55, 1.05] -1.07 828 .287
RaceWhite -1.12 [-3.00, 0.77] -1.16 828 .245
Hispanic -0.14 [-0.39, 0.11] -1.10 828 .271
Income num z 0.02 [-0.05, 0.09] 0.58 828 .560
Edu num z 0.14 [0.07, 0.21] 3.79 828 < .001
Age z -0.01 [-0.08, 0.06] -0.22 828 .826
County medianincome z 0.03 [-0.04, 0.10] 0.83 828 .408
County gini z 0.07 [-0.01, 0.15] 1.65 828 .100
County density z 0.06 [-0.02, 0.14] 1.39 828 .164

Individualism: Interaction with ideology

m1 <- lm(trust_deminst_z ~ individualism_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-109)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.07, 0.06] -0.06 932 .952
Individualism z 0.03 [-0.03, 0.10] 1.07 932 .284
Ideo con z 0.10 [0.03, 0.16] 3.03 932 .003
Individualism z \(\times\) Ideo con z 0.03 [-0.02, 0.08] 1.17 932 .243

Justice: No controls

m1 <- lm(trust_deminst_z ~ justice_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-110)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.05, 0.06] 0.08 990 .940
Justice z 0.44 [0.39, 0.50] 15.53 990 < .001

Justice: With controls

m1 <- lm(trust_deminst_z ~ justice_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-111)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 1.54 [-0.19, 3.27] 1.75 827 .081
Justice z 0.39 [0.33, 0.45] 12.50 827 < .001
SDO z 0.00 [-0.06, 0.07] 0.13 827 .900
TIPI agree z 0.12 [0.05, 0.18] 3.52 827 < .001
Genderwoman -0.05 [-0.17, 0.08] -0.73 827 .464
RaceAsian -1.51 [-3.26, 0.24] -1.69 827 .091
RaceBlack or African American -1.31 [-3.04, 0.42] -1.49 827 .138
RaceMiddle Eastern or North African -0.93 [-3.36, 1.50] -0.75 827 .454
Racemultiracial -1.69 [-3.43, 0.06] -1.90 827 .058
RaceOther please specify -1.31 [-3.42, 0.81] -1.21 827 .226
RaceWhite -1.54 [-3.26, 0.19] -1.74 827 .082
Hispanic -0.03 [-0.26, 0.20] -0.29 827 .775
Income num z 0.01 [-0.06, 0.08] 0.25 827 .802
Edu num z 0.11 [0.05, 0.18] 3.40 827 < .001
Age z -0.01 [-0.08, 0.05] -0.39 827 .695
County medianincome z 0.02 [-0.04, 0.09] 0.67 827 .504
County gini z 0.06 [-0.02, 0.13] 1.42 827 .155
County density z 0.05 [-0.03, 0.12] 1.28 827 .201

Justice: Interaction with ideology

m1 <- lm(trust_deminst_z ~ justice_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-112)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.01 [-0.05, 0.07] 0.27 931 .791
Justice z 0.44 [0.38, 0.50] 14.49 931 < .001
Ideo con z 0.03 [-0.02, 0.09] 1.15 931 .249
Justice z \(\times\) Ideo con z -0.04 [-0.10, 0.02] -1.39 931 .165

Pursuit of Happiness: No controls

m1 <- lm(trust_deminst_z ~ happiness_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-113)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.06] 0.05 990 .962
Happiness z 0.23 [0.17, 0.30] 7.59 990 < .001

Pursuit of Happiness: With controls

m1 <- lm(trust_deminst_z ~ happiness_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-114)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 1.31 [-0.52, 3.15] 1.41 828 .160
Happiness z 0.23 [0.17, 0.30] 7.03 828 < .001
SDO z 0.01 [-0.06, 0.07] 0.19 828 .845
TIPI agree z 0.17 [0.11, 0.24] 5.04 828 < .001
Genderwoman -0.12 [-0.25, 0.02] -1.72 828 .086
RaceAsian -1.34 [-3.19, 0.52] -1.41 828 .157
RaceBlack or African American -1.02 [-2.86, 0.81] -1.09 828 .274
RaceMiddle Eastern or North African -0.40 [-2.97, 2.18] -0.30 828 .761
Racemultiracial -1.54 [-3.39, 0.31] -1.63 828 .103
RaceOther please specify -1.23 [-3.47, 1.00] -1.08 828 .279
RaceWhite -1.26 [-3.09, 0.57] -1.35 828 .177
Hispanic -0.07 [-0.32, 0.17] -0.58 828 .559
Income num z 0.01 [-0.06, 0.08] 0.26 828 .796
Edu num z 0.14 [0.07, 0.21] 4.08 828 < .001
Age z -0.01 [-0.08, 0.06] -0.33 828 .744
County medianincome z 0.04 [-0.03, 0.10] 1.07 828 .287
County gini z 0.07 [-0.01, 0.15] 1.62 828 .106
County density z 0.05 [-0.03, 0.13] 1.26 828 .210

Pursuit of Happiness: Interaction with ideology

m1 <- lm(trust_deminst_z ~ happiness_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-115)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.01 [-0.06, 0.07] 0.25 931 .804
Happiness z 0.24 [0.17, 0.30] 7.22 931 < .001
Ideo con z 0.06 [-0.01, 0.12] 1.71 931 .087
Happiness z \(\times\) Ideo con z -0.06 [-0.12, 0.00] -2.10 931 .036

Right to bear arms: No controls

m1 <- lm(trust_deminst_z ~ arms_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-116)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.06] 0.02 991 .987
Arms z -0.16 [-0.22, -0.10] -5.10 991 < .001

Right to bear arms: With controls

m1 <- lm(trust_deminst_z ~ arms_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-117)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 1.13 [-0.73, 3.00] 1.19 828 .234
Arms z -0.14 [-0.20, -0.07] -4.17 828 < .001
SDO z 0.03 [-0.03, 0.10] 0.95 828 .340
TIPI agree z 0.19 [0.12, 0.26] 5.52 828 < .001
Genderwoman -0.16 [-0.30, -0.03] -2.42 828 .016
RaceAsian -1.13 [-3.02, 0.76] -1.18 828 .240
RaceBlack or African American -0.82 [-2.69, 1.05] -0.86 828 .391
RaceMiddle Eastern or North African -0.36 [-2.98, 2.27] -0.27 828 .790
Racemultiracial -1.32 [-3.20, 0.56] -1.37 828 .170
RaceOther please specify -1.27 [-3.55, 1.01] -1.09 828 .275
RaceWhite -1.05 [-2.92, 0.81] -1.11 828 .268
Hispanic -0.07 [-0.32, 0.18] -0.57 828 .567
Income num z 0.02 [-0.06, 0.09] 0.43 828 .666
Edu num z 0.13 [0.05, 0.20] 3.47 828 < .001
Age z -0.01 [-0.07, 0.06] -0.17 828 .868
County medianincome z 0.03 [-0.04, 0.10] 0.82 828 .413
County gini z 0.07 [-0.01, 0.16] 1.75 828 .080
County density z 0.05 [-0.03, 0.13] 1.25 828 .212

Right to bear arms: Interaction with ideology

m1 <- lm(trust_deminst_z ~ arms_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-118)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.06] 0.04 931 .969
Arms z -0.14 [-0.21, -0.07] -4.00 931 < .001
Ideo con z 0.09 [0.03, 0.15] 2.79 931 .005
Arms z \(\times\) Ideo con z 0.04 [-0.03, 0.11] 1.05 931 .295

Tolerance: No controls

m1 <- lm(trust_deminst_z ~ tolerance_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-119)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 0.00 [-0.06, 0.06] 0.03 991 .976
Tolerance z 0.10 [0.03, 0.16] 3.01 991 .003

Tolerance: With controls

m1 <- lm(trust_deminst_z ~ tolerance_z + SDO_z + TIPI_agree_z + gender + race + 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)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-120)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept 1.27 [-0.60, 3.14] 1.33 828 .184
Tolerance z 0.15 [0.06, 0.23] 3.51 828 < .001
SDO z 0.03 [-0.04, 0.10] 0.93 828 .351
TIPI agree z 0.19 [0.12, 0.26] 5.41 828 < .001
Genderwoman -0.15 [-0.29, -0.02] -2.22 828 .027
RaceAsian -1.25 [-3.14, 0.65] -1.29 828 .197
RaceBlack or African American -0.97 [-2.84, 0.91] -1.01 828 .311
RaceMiddle Eastern or North African -0.42 [-3.05, 2.21] -0.31 828 .753
Racemultiracial -1.48 [-3.37, 0.41] -1.54 828 .125
RaceOther please specify -1.26 [-3.55, 1.03] -1.08 828 .280
RaceWhite -1.19 [-3.06, 0.68] -1.24 828 .214
Hispanic -0.11 [-0.36, 0.14] -0.87 828 .384
Income num z 0.02 [-0.06, 0.09] 0.45 828 .651
Edu num z 0.14 [0.06, 0.21] 3.75 828 < .001
Age z 0.00 [-0.07, 0.07] -0.08 828 .938
County medianincome z 0.03 [-0.04, 0.10] 0.81 828 .421
County gini z 0.07 [-0.01, 0.16] 1.72 828 .085
County density z 0.05 [-0.03, 0.13] 1.18 828 .238

Tolerance: Interaction with ideology

m1 <- lm(trust_deminst_z ~ tolerance_z*ideo_con_z,data = df_bsc_elg)

apa_lm <- apa_print(m1)
apa_table(
  apa_lm$table, 
  placement = "H"
)
(#tab:unnamed-chunk-121)
Predictor \(b\) 95% CI \(t\) \(\mathit{df}\) \(p\)
Intercept -0.01 [-0.07, 0.06] -0.22 932 .826
Tolerance z 0.21 [0.13, 0.29] 5.28 932 < .001
Ideo con z 0.09 [0.03, 0.15] 2.82 932 .005
Tolerance z \(\times\) Ideo con z 0.17 [0.10, 0.23] 4.99 932 < .001 
interact_plot(m1,
              pred = "tolerance_z",
              modx = "ideo_con_z")