Class Zero-Sum Beliefs
February 2026A: Analysis
Eligibility
total_n = nrow(df_cbzs)Total N = 200
Preregistered exclusions:
- check_1: select somewhat disagree
- check_2: bot-check
elg_n = nrow(df_cbzs_elg)
df_cbzs %>%
group_by(att_1,att_2) %>%
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")## `summarise()` has grouped output by 'att_1'. You can override using the
## `.groups` argument.| att_1 | att_2 | N | Perc |
|---|---|---|---|
| 1 | 1 | 193 | 96.5 |
| 1 | 0 | 7 | 3.5 |
Eligible N = 193
Demographics
Gender
df_cbzs_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 |
|---|---|---|
| woman | 107 | 55.44 |
| man | 84 | 43.52 |
| other | 2 | 1.04 |
Race
| race | N | Perc |
|---|---|---|
| White | 184 | 95.34 |
| multiracial | 9 | 4.66 |
Age
Mean age: 42.83.
Income
median_income_num <- df_cbzs_elg %>%
mutate(income_num = as.numeric(income)) %>%
summarise(median = median(income_num, na.rm = TRUE)) %>%
pull(median)
df_cbzs_elg %>%
ggplot(aes(x = income)) +
geom_bar() +
geom_vline(xintercept = median_income_num,
color = "lightblue", 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_blank(),
axis.title.y = element_blank()) +
coord_flip()
Education
| edu | N | Perc |
|---|---|---|
| noHS | 1 | 0.52 |
| GED | 52 | 26.94 |
| 2yearColl | 19 | 9.84 |
| 4yearColl | 88 | 45.60 |
| MA | 24 | 12.44 |
| PHD | 7 | 3.63 |
| NA | 2 | 1.04 |
SES
| ses | N | Perc |
|---|---|---|
| Lower Class | 15 | 7.77 |
| Lower Middle Class | 73 | 37.82 |
| Middle Class | 89 | 46.11 |
| Upper Middle Class | 15 | 7.77 |
| Upper Class | 1 | 0.52 |
Working-class identification
To what extent do you identify as working-class? (0 = Not at All Working-Class; 50 = Moderately Working-Class; 100 = Very Strongly Working-Class)
df_cbzs_elg %>%
ggplot(aes(x = class_id)) +
geom_histogram(fill = "lightblue",
binwidth = 5,
color = NA) +
scale_x_continuous(breaks = seq(0,100,10),
limits = c(-10,110)) +
ylab("count") +
geom_vline(xintercept = mean(df_cbzs_elg$class_id,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
Political 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_cbzs_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_cbzs_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",color = NA) +
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 affiliation
| party_id | N | Perc |
|---|---|---|
| Independent | 70 | 36.27 |
| Democrat | 63 | 32.64 |
| Republican | 60 | 31.09 |
Measures
Class-based Zero-Sum Beliefs
- If the upper class becomes richer, this comes at the expense of the working class
- If the upper class makes more money, then the working class makes less money
- If the upper class does better economically, this does NOT come at the expense of the working class [R]
alpha = 0.91
df_cbzs_elg %>%
ggplot(aes(x = zs_class)) +
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_cbzs_elg$zs_class,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"))
Race-based Zero-Sum Beliefs: Black
- If black people become richer, this comes at the expense of white people
- If black people make more money, then white people make less money
- If black people do better economically, this does NOT come at the expense of white people [R]
alpha = 0.84
df_cbzs_elg %>%
ggplot(aes(x = zs_race_b)) +
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_cbzs_elg$zs_race_b,na.rm = T),
color = "black",
linetype = "dashed",
size = 1.1) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_text(color = "black",
face = "bold"))
Race-based Zero-Sum Beliefs: Asian
- If Asian people become richer, this comes at the expense of white people
- If Asian people make more money, then white people make less money
- If Asian people do better economically, this does NOT come at the expense of white people [R]
alpha = 0.86
df_cbzs_elg %>%
ggplot(aes(x = zs_race_a)) +
geom_density(fill = "lightblue",
color = NA) +
scale_x_continuous(breaks = seq(1,7,1),
limits = c(1,7)) +
ylab("density") +
geom_vline(xintercept = mean(df_cbzs_elg$zs_race_a,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"))
Race-based Zero-Sum Beliefs: Hispanic
- If Hispanic people become richer, this comes at the expense of white people
- If Hispanic people make more money, then white people make less money
- If Hispanic people do better economically, this does NOT come at the expense of white people [R]
alpha = 0.82
df_cbzs_elg %>%
ggplot(aes(x = zs_race_h)) +
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_cbzs_elg$zs_race_h,na.rm = T),
color = "black",
linetype = "dashed",
size = 1.1) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_text(color = "black",
face = "bold"))
Class Solidarity
- I feel a sense of solidarity with non-White (e.g., Black, Asian, Hispanic) working class people.
- The problems facing White and non-White working class people are similar enough for us to be allies.
- What happens to non-White working class communities has something to do with what happens to White working class people.
alpha = 0.66
df_cbzs_elg %>%
ggplot(aes(x = crs)) +
geom_density(fill = "lightblue",
color = NA) +
scale_x_continuous(breaks = seq(1,7,1),
limits = c(1,7)) +
ylab("density") +
geom_vline(xintercept = mean(df_cbzs_elg$crs,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 redistributive policy
Participants saw one of the following policies and indicated their support for it (1 = Strongly Oppose to 7 = Strongly Support)
Minimum wage increase
Congress has not increased the federal minimum wage, currently set at $7.25, since 2009. Some Congresspeople are proposing a policy that would gradually raise the federal minimum wage to $20 an hour by 2028. After 2028, the minimum wage would be adjusted each year to keep pace with growth in the median wage, a measure of wages for typical workers.
Student debt relief
Some Congresspeople are proposing a policy that would help to address the student loan debt crisis by forgiving up to $50,000 in loans per borrower. Approximately 42 million Americans, or about 1 in 6 American adults, owe a cumulative $1.6 trillion in student loans. Student loans are now the second-largest slice of household debt after mortgages, bigger than credit card debt.
Housing
Some Congresspeople are proposing a housing affordability policy that would help ensure that every American has a place to live. The policy would allow for smaller, lower cost homes like duplexes, townhouses, and garden apartments to be built and developed, allowing new nonprofit homes and reducing overall housing prices.
Climate change
Some Congresspeople are proposing a Green New Deal bill which would phase out the use of fossil fuels, with the government providing clean energy jobs for people who can’t find employment in the private sector. All jobs would pay at least $20 an hour, and include healthcare benefits and collective bargaining rights.
df_cbzs_elg %>%
ggplot(aes(x = support)) +
geom_histogram(fill = "lightblue",
binwidth = 1,
color = NA) +
scale_x_continuous(breaks = seq(1,7,1),
limits = c(0,8)) +
ylab("count") +
geom_vline(xintercept = mean(df_cbzs_elg$support,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"))
Linked Fate: Black
- What happens to working-class Black people in this country will have something to do with what happens to working-class people of my racial group
- Working-class Black people and working-class people from my racial group share a common destiny
r = 0.49
df_cbzs_elg %>%
ggplot(aes(x = lf_b)) +
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_cbzs_elg$lf_b,na.rm = T),
color = "black",
linetype = "dashed",
size = 1.1) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_text(color = "black",
face = "bold"))
Linked Fate: Asian
- What happens to working-class Asian people in this country will have something to do with what happens to working-class people of my racial group
- Working-class Asian people and working-class people from my racial group share a common destiny
r = 0.59
df_cbzs_elg %>%
ggplot(aes(x = lf_a)) +
geom_density(fill = "lightblue",
color = NA) +
scale_x_continuous(breaks = seq(1,7,1),
limits = c(1,7)) +
ylab("density") +
geom_vline(xintercept = mean(df_cbzs_elg$lf_a,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"))
Linked Fate: Hispanic
- What happens to working-class Hispanic people in this country will have something to do with what happens to working-class people of my racial group
- Working-class Hispanic people and working-class people from my racial group share a common destiny
r = 0.59
df_cbzs_elg %>%
ggplot(aes(x = lf_h)) +
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_cbzs_elg$lf_h,na.rm = T),
color = "black",
linetype = "dashed",
size = 1.1) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_text(color = "black",
face = "bold"))
Similarity
- I think I’m very similar to most non-White working class people.
- I have a lot in common with the average non-White working class person.
r = 0.86
df_cbzs_elg %>%
ggplot(aes(x = similar)) +
geom_density(fill = "lightblue",
color = NA) +
scale_x_continuous(breaks = seq(1,7,1),
limits = c(1,7)) +
ylab("density") +
geom_vline(xintercept = mean(df_cbzs_elg$similar,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"))
ZSB: Race - Wilkins measure
- When non-white people get jobs, white people lose jobs.
- When non-white people get rights, they are taking rights away from white people.
- Rights for non-white people mean white people lose out.
- As non-white people face less racism, white people end up facing more racism.
- Less discrimination against non-white people means more discrimination against white people.
- Efforts to reduce discrimination against non-white people have led to increased discrimination against white people.
alpha = 0.94
df_cbzs_elg %>%
ggplot(aes(x = zs_race_wilkins)) +
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_cbzs_elg$zs_race_wilkins,na.rm = T),
color = "black",
linetype = "dashed",
size = 1.1) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(color = "grey66"),
axis.text.y = element_text(color = "black"),
axis.text.x = element_text(color = "black",
face = "bold"),
axis.title.x = element_text(color = "black",
face = "bold"))
Correlations
Analysis
Mediation Model
Predictor: Class ZSB
Mediator: Cross-race solidarity
Outcome: Support for policy
Bootstraps: 10,000
form.m <- reformulate("zs_class", response = "crs")
form.y <- reformulate(c("zs_class", "crs"), response = "support")
# Fit linear models
m.fit <- lm(form.m, data = df_cbzs_elg) # a-path
y.fit <- lm(form.y, data = df_cbzs_elg) # b and c'-paths
# Fit outcome model WITHOUT mediator to get c-path (total effect)
y.fit.total <- lm(
reformulate("zs_class", response = "support"),
data = df_cbzs_elg
)
# Mediation analysis with bootstrapping (10,000 sims)
med.fit <- mediation::mediate(
model.m = m.fit,
model.y = y.fit,
treat = "zs_class",
mediator = "crs",
boot = TRUE,
sims = 10000
)
med_tbl <- tibble(
Effect = c("ACME (indirect)", "ADE (direct)",
"Total Effect", "Prop. Mediated"),
Estimate = c(med.fit$d0, med.fit$z0,
med.fit$tau.coef, med.fit$n0),
CI.lower = c(med.fit$d0.ci[1], med.fit$z0.ci[1],
med.fit$tau.ci[1], med.fit$n0.ci[1]),
CI.upper = c(med.fit$d0.ci[2], med.fit$z0.ci[2],
med.fit$tau.ci[2], med.fit$n0.ci[2]),
p.value = c(med.fit$d0.p, med.fit$z0.p,
med.fit$tau.p, med.fit$n0.p)
)
kbl(med_tbl) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| Effect | Estimate | CI.lower | CI.upper | p.value |
|---|---|---|---|---|
| ACME (indirect) | 0.0492645 | -0.0075436 | 0.1192509 | 0.0888 |
| ADE (direct) | 0.3070470 | 0.1400034 | 0.4729324 | 0.0002 |
| Total Effect | 0.3563115 | 0.1999013 | 0.5106287 | 0.0000 |
| Prop. Mediated | 0.1382624 | -0.0217287 | 0.3912559 | 0.0888 |
# Helper to generate significance stars
p_stars <- function(p) {
if (p < .001) return("***")
if (p < .01) return("**")
if (p < .05) return("*")
return("")
}
# Extract coefficients & p-values
a_coef <- coef(m.fit)["zs_class"]
a_p <- summary(m.fit)$coefficients["zs_class", "Pr(>|t|)"]
b_coef <- coef(y.fit)["crs"]
b_p <- summary(y.fit)$coefficients["crs", "Pr(>|t|)"]
cprime <- coef(y.fit)["zs_class"]
cprime_p <- summary(y.fit)$coefficients["zs_class", "Pr(>|t|)"]
c_total <- coef(y.fit.total)["zs_class"]
c_total_p <- summary(y.fit.total)$coefficients["zs_class", "Pr(>|t|)"]
# Paste coefficient + stars
a_label <- paste0("a = ", round(a_coef, 3), p_stars(a_p))
b_label <- paste0("b = ", round(b_coef, 3), p_stars(b_p))
cprime_label <- paste0("c' = ", round(cprime, 3), p_stars(cprime_p))
c_label <- paste0("c = ", round(c_total, 3), p_stars(c_total_p))
# Plot
ggplot() +
xlim(0, 3) + ylim(0, 2) +
# Nodes
annotate("text", x = 0.5, y = 1, label = "zs_class", fontface = "bold") +
annotate("text", x = 1.5, y = 1, label = "crs", fontface = "bold") +
annotate("text", x = 2.5, y = 1, label = "support", fontface = "bold") +
# a-path (X → M)
annotate("segment",
x = 0.7, xend = 1.3, y = 1, yend = 1,
arrow = arrow(length = unit(0.20, "cm"))) +
annotate("text", x = 1.0, y = 1.15, label = a_label) +
# b-path (M → Y)
annotate("segment",
x = 1.7, xend = 2.3, y = 1, yend = 1,
arrow = arrow(length = unit(0.20, "cm"))) +
annotate("text", x = 2.0, y = 1.15, label = b_label) +
# c'-path (direct effect)
annotate("segment",
x = 0.5, xend = 2.5, y = 0.9, yend = 0.9,
arrow = arrow(length = unit(0.20, "cm"))) +
annotate("text", x = 1.5, y = 0.75, label = cprime_label) +
# c-path (total effect, dashed)
annotate("segment",
x = 0.5, xend = 2.5, y = 1.1, yend = 1.1,
linetype = "dashed",
arrow = arrow(length = unit(0.20, "cm"))) +
annotate("text", x = 1.5, y = 1.25, label = c_label) +
theme_void()
Linear Model 1
Predictor: Class ZSB
Moderator: Race ZSB: Black
Outcome: Cross-Race Solidarity
m1 <- lm(crs ~ zs_class*zs_race_b,data = df_cbzs_elg)
eta_table <- eta_squared(m1)
etas_for_table <- c(NA,eta_table$Eta2)
apa_lm <- apa_print(m1)
table_for_print <- apa_lm$table %>%
mutate(eta2 = round(etas_for_table,3))
kbl(table_for_print) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value | eta2 |
|---|---|---|---|---|---|---|
| Intercept | 5.09 | [4.21, 5.97] | 11.42 | 189 | < .001 | NA |
| Zs class | 0.18 | [0.02, 0.35] | 2.23 | 189 | .027 | 0.134 |
| Zs race b | -0.43 | [-0.81, -0.04] | -2.20 | 189 | .029 | 0.121 |
| Zs class \(\times\) Zs race b | 0.03 | [-0.04, 0.10] | 0.82 | 189 | .415 | 0.004 |
interact_plot(m1,
pred = "zs_class",
modx = "zs_race_b",
interval = T)
Linear Model 2
Predictor: Class ZSB
Moderator: Race ZSB: Asian
Outcome: Cross-Race Solidarity
m1 <- lm(crs ~ zs_class*zs_race_a,data = df_cbzs_elg)
eta_table <- eta_squared(m1)
etas_for_table <- c(NA,eta_table$Eta2)
apa_lm <- apa_print(m1)
table_for_print <- apa_lm$table %>%
mutate(eta2 = round(etas_for_table,3))
kbl(table_for_print) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value | eta2 |
|---|---|---|---|---|---|---|
| Intercept | 5.11 | [4.22, 5.99] | 11.35 | 189 | < .001 | NA |
| Zs class | 0.15 | [-0.01, 0.32] | 1.83 | 189 | .069 | 0.128 |
| Zs race a | -0.42 | [-0.79, -0.06] | -2.28 | 189 | .024 | 0.071 |
| Zs class \(\times\) Zs race a | 0.04 | [-0.03, 0.11] | 1.22 | 189 | .226 | 0.008 |
interact_plot(m1,
pred = "zs_class",
modx = "zs_race_a",
interval = T)
Linear Model 3
Predictor: Class ZSB
Moderator: Race ZSB: Hispanic
Outcome: Cross-Race Solidarity
m1 <- lm(crs ~ zs_class*zs_race_h,data = df_cbzs_elg)
eta_table <- eta_squared(m1)
etas_for_table <- c(NA,eta_table$Eta2)
apa_lm <- apa_print(m1)
table_for_print <- apa_lm$table %>%
mutate(eta2 = round(etas_for_table,3))
kbl(table_for_print) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value | eta2 |
|---|---|---|---|---|---|---|
| Intercept | 5.10 | [4.23, 5.96] | 11.61 | 189 | < .001 | NA |
| Zs class | 0.18 | [0.02, 0.34] | 2.20 | 189 | .029 | 0.133 |
| Zs race h | -0.40 | [-0.77, -0.04] | -2.19 | 189 | .030 | 0.116 |
| Zs class \(\times\) Zs race h | 0.03 | [-0.04, 0.10] | 0.77 | 189 | .445 | 0.003 |
interact_plot(m1,
pred = "zs_class",
modx = "zs_race_h",
interval = T)
Linear Model 4
Predictor: Class ZSB
Moderator: Race ZSB: Black
Outcome: Linked fate: Black
m1 <- lm(lf_b ~ zs_class*zs_race_b,data = df_cbzs_elg)
eta_table <- eta_squared(m1)
etas_for_table <- c(NA,eta_table$Eta2)
apa_lm <- apa_print(m1)
table_for_print <- apa_lm$table %>%
mutate(eta2 = round(etas_for_table,3))
kbl(table_for_print) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value | eta2 |
|---|---|---|---|---|---|---|
| Intercept | 4.50 | [3.36, 5.64] | 7.80 | 189 | < .001 | NA |
| Zs class | 0.18 | [-0.03, 0.39] | 1.71 | 189 | .089 | 0.045 |
| Zs race b | -0.14 | [-0.64, 0.35] | -0.56 | 189 | .575 | 0.031 |
| Zs class \(\times\) Zs race b | -0.01 | [-0.10, 0.09] | -0.13 | 189 | .898 | 0.000 |
interact_plot(m1,
pred = "zs_class",
modx = "zs_race_b",
interval = T)
Linear Model 5
Predictor: Class ZSB
Moderator: Race ZSB: Asian
Outcome: Linked fate: Asian
m1 <- lm(lf_a ~ zs_class*zs_race_a,data = df_cbzs_elg)
eta_table <- eta_squared(m1)
etas_for_table <- c(NA,eta_table$Eta2)
apa_lm <- apa_print(m1)
table_for_print <- apa_lm$table %>%
mutate(eta2 = round(etas_for_table,3))
kbl(table_for_print) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value | eta2 |
|---|---|---|---|---|---|---|
| Intercept | 4.86 | [3.71, 6.01] | 8.31 | 189 | < .001 | NA |
| Zs class | 0.09 | [-0.13, 0.30] | 0.80 | 189 | .424 | 0.061 |
| Zs race a | -0.42 | [-0.90, 0.05] | -1.76 | 189 | .080 | 0.016 |
| Zs class \(\times\) Zs race a | 0.06 | [-0.03, 0.15] | 1.30 | 189 | .196 | 0.009 |
interact_plot(m1,
pred = "zs_class",
modx = "zs_race_a",
interval = T)
Linear Model 6
Predictor: Class ZSB
Moderator: Race ZSB: Hispanic
Outcome: Linked fate: Hispanic
m1 <- lm(lf_h ~ zs_class*zs_race_h,data = df_cbzs_elg)
eta_table <- eta_squared(m1)
etas_for_table <- c(NA,eta_table$Eta2)
apa_lm <- apa_print(m1)
table_for_print <- apa_lm$table %>%
mutate(eta2 = round(etas_for_table,3))
kbl(table_for_print) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value | eta2 |
|---|---|---|---|---|---|---|
| Intercept | 5.26 | [4.10, 6.43] | 8.89 | 189 | < .001 | NA |
| Zs class | 0.01 | [-0.20, 0.23] | 0.14 | 189 | .893 | 0.039 |
| Zs race h | -0.50 | [-0.99, -0.01] | -2.03 | 189 | .044 | 0.016 |
| Zs class \(\times\) Zs race h | 0.07 | [-0.02, 0.17] | 1.58 | 189 | .116 | 0.013 |
interact_plot(m1,
pred = "zs_class",
modx = "zs_race_h",
interval = T)
Linear Model 7
Predictor: Class ZSB
Moderator: Race ZSB Wilkins
Outcome: Cross-Race Class Solidarity
m1 <- lm(crs ~ zs_class*zs_race_wilkins,data = df_cbzs_elg)
eta_table <- eta_squared(m1)
etas_for_table <- c(NA,eta_table$Eta2)
apa_lm <- apa_print(m1)
table_for_print <- apa_lm$table %>%
mutate(eta2 = round(etas_for_table,3))
kbl(table_for_print) %>%
kable_styling(bootstrap_options = "hover",
full_width = F,
position = "left")| term | estimate | conf.int | statistic | df | p.value | eta2 |
|---|---|---|---|---|---|---|
| Intercept | 5.01 | [4.07, 5.96] | 10.47 | 189 | < .001 | NA |
| Zs class | 0.19 | [0.02, 0.36] | 2.17 | 189 | .031 | 0.132 |
| Zs race wilkins | -0.26 | [-0.60, 0.08] | -1.51 | 189 | .133 | 0.106 |
| Zs class \(\times\) Zs race wilkins | 0.00 | [-0.06, 0.07] | 0.06 | 189 | .952 | 0.000 |
interact_plot(m1,
pred = "zs_class",
modx = "zs_race_wilkins",
interval = T)