Zero-Sum Solidarity

Aug 2025: Analysis

Background

This is a pilot study that tests the effect of losing in a zero-sum game on solidarity.

We devised a minimal groups’ paradigm in which there are 100 circles representing 100 people. 50 are green and 50 are blue. Each person has 10 coins. The participant is told that they are one of the green people.

Then, they are randomly assigned to one of three conditions: (1) lose zs; (2) win zs; and (3) ctrl.

lose zs:
Total resources in this society are limited. This means that as blue people gain more coins, green people end up with less coins, and vice versa.

In recent weeks, more coins have gone to the blue people, and as a result, green people have lost an equal amount of coins. Currently, blue people have 12 coins each and green people have 8 coins each.

You have 8 coins.

win zs:
Total resources in this society are limited. This means that as blue people gain more coins, green people end up with less coins, and vice versa.

In recent weeks, more coins have gone to the green people, and as a result, blue people have lost an equal amount of coins. Currently, green people have 12 coins each and blue people have 8 coins each.

You have 12 coins.

ctrl:
Total resources in this society are limited. This means that as blue people gain more coins, green people end up with less coins, and vice versa.

You have 10 coins.

# Eligibility

There was on bot-check with an invisible attention check.

eligible_n <- df_zss %>% 
  group_by(is_elg) %>% 
  summarise(n = n()) %>% 
  ungroup() %>% 
  filter(is_elg == 1) %>% 
  select(n) %>% 
  unlist() %>% 
  unname()

df_zss %>% 
  group_by(att_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")

att_1	N	Perc
1	227	100

That gives us a total of 227 eligible participants.

Demographics

Race

df_zss_elg %>% 
  group_by(race) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

race	N	Perc
American Indian or Alaska Native	1	0.44
Asian	27	11.89
Black or African American	17	7.49
Hispanic, Latino, or Spanish origin	14	6.17
Middle Eastern or North African	1	0.44
Native Hawaiian or Other Pacific Islander	1	0.44
White	149	65.64
multiracial	17	7.49

Gender

df_zss_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	128	56.39
other	4	1.76
woman	95	41.85

Age

df_zss_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
39.3	11.68

Education

df_zss_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	1	0.44
GED	53	23.35
2yearColl	32	14.10
4yearColl	101	44.49
MA	24	10.57
PHD	14	6.17
NA	2	0.88

Income

df_zss_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_zss_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_zss_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 affiliation

df_zss_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	105	46.26
Independent	71	31.28
Republican	51	22.47

Measures

Identification

Green

I identify with the green people

df_zss_elg %>% 
  ggplot(aes(x = green_id)) +
  geom_histogram(fill = "lightblue",
                 binwidth = 1) +
  scale_x_continuous(breaks = seq(0,100,20),
                     limits = c(-1,101)) +
  ylab("count") +
  geom_vline(xintercept = mean(df_zss_elg$green_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"))

Blue

I identify with the blue people

df_zss_elg %>% 
  ggplot(aes(x = blue_id)) +
  geom_histogram(fill = "lightblue",
                 binwidth = 1) +
  scale_x_continuous(breaks = seq(0,100,20),
                     limits = c(-1,101)) +
  ylab("count") +
  geom_vline(xintercept = mean(df_zss_elg$blue_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"))

Connected

Green

I feel connected with the green people

df_zss_elg %>% 
  ggplot(aes(x = green_connect)) +
  geom_histogram(fill = "lightblue",
                 binwidth = 1) +
  scale_x_continuous(breaks = seq(0,100,20),
                     limits = c(-1,101)) +
  ylab("count") +
  geom_vline(xintercept = mean(df_zss_elg$green_connect,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"))

Blue

I feel connected with the blue people

df_zss_elg %>% 
  ggplot(aes(x = blue_connect)) +
  geom_histogram(fill = "lightblue",
                 binwidth = 1) +
  scale_x_continuous(breaks = seq(0,100,20),
                     limits = c(-1,101)) +
  ylab("count") +
  geom_vline(xintercept = mean(df_zss_elg$blue_connect,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"))

Solidarity

Green

I feel a sense of solidarity with the green people

df_zss_elg %>% 
  ggplot(aes(x = green_soli)) +
  geom_histogram(fill = "lightblue",
                 binwidth = 1) +
  scale_x_continuous(breaks = seq(0,100,20),
                     limits = c(-1,101)) +
  ylab("count") +
  geom_vline(xintercept = mean(df_zss_elg$green_soli,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"))

Blue

I feel a sense of solidarity with the blue people

df_zss_elg %>% 
  ggplot(aes(x = blue_soli)) +
  geom_histogram(fill = "lightblue",
                 binwidth = 1) +
  scale_x_continuous(breaks = seq(0,100,20),
                     limits = c(-1,101)) +
  ylab("count") +
  geom_vline(xintercept = mean(df_zss_elg$blue_soli,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"))

Value

Green

I value other green people

df_zss_elg %>% 
  ggplot(aes(x = green_value)) +
  geom_histogram(fill = "lightblue",
                 binwidth = 1) +
  scale_x_continuous(breaks = seq(0,100,20),
                     limits = c(-1,101)) +
  ylab("count") +
  geom_vline(xintercept = mean(df_zss_elg$green_value,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"))

Blue

I value other blue people

df_zss_elg %>% 
  ggplot(aes(x = blue_value)) +
  geom_histogram(fill = "lightblue",
                 binwidth = 1) +
  scale_x_continuous(breaks = seq(0,100,20),
                     limits = c(-1,101)) +
  ylab("count") +
  geom_vline(xintercept = mean(df_zss_elg$blue_value,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"))

Liking

Green

I like other green people

df_zss_elg %>% 
  ggplot(aes(x = green_like)) +
  geom_histogram(fill = "lightblue",
                 binwidth = 1) +
  scale_x_continuous(breaks = seq(0,100,20),
                     limits = c(-1,101)) +
  ylab("count") +
  geom_vline(xintercept = mean(df_zss_elg$green_like,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"))

Blue

I like other blue people

df_zss_elg %>% 
  ggplot(aes(x = blue_like)) +
  geom_histogram(fill = "lightblue",
                 binwidth = 1) +
  scale_x_continuous(breaks = seq(0,100,20),
                     limits = c(-1,101)) +
  ylab("count") +
  geom_vline(xintercept = mean(df_zss_elg$blue_like,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"))

Threat

In this scenario, to what extent do you feel threatened as a green person? (1 = Not at all to 5 = Extremely)

df_zss_elg %>% 
  ggplot(aes(x = threat)) +
  geom_histogram(fill = "lightblue",
                 binwidth = 1) +
  scale_x_continuous(breaks = seq(1,5,1),
                     limits = c(0,6)) +
  ylab("count") +
  geom_vline(xintercept = mean(df_zss_elg$threat,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"))

Enemy

In this scenario, to what extent do you see blue people as the enemy? (1 = Not at all to 5 = Extremely)

df_zss_elg %>% 
  ggplot(aes(x = enemy)) +
  geom_histogram(fill = "lightblue",
                 binwidth = 1) +
  scale_x_continuous(breaks = seq(1,5,1),
                     limits = c(0,6)) +
  ylab("count") +
  geom_vline(xintercept = mean(df_zss_elg$enemy,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"))

Perceived groups

In this scenario, to what extent do you see green people and blue people as part of the same group? (1 = Not at all to 5 = Extremely)

df_zss_elg %>% 
  ggplot(aes(x = shared)) +
  geom_histogram(fill = "lightblue",
                 binwidth = 1) +
  scale_x_continuous(breaks = seq(1,5,1),
                     limits = c(0,6)) +
  ylab("count") +
  geom_vline(xintercept = mean(df_zss_elg$shared,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

In this scenario, to what extent do you see your fate as linked to the fate of other green people? (1 = Not at all to 5 = Extremely)

df_zss_elg %>% 
  ggplot(aes(x = linked)) +
  geom_histogram(fill = "lightblue",
                 binwidth = 1) +
  scale_x_continuous(breaks = seq(1,5,1),
                     limits = c(0,6)) +
  ylab("count") +
  geom_vline(xintercept = mean(df_zss_elg$linked,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"))

Analysis

Correlation matrix

hmm, can we average the green feeling items and the blue feeling items? They’re correlated af with each other.

Green items alpha

alpha = 0.95

Blue items alpha

alpha = 0.91

Great! I’ll analyze these mean scores as well.

DV: Mean score of Green feelings

df_zss_elg %>% 
  group_by(cond) %>% 
  summarise(N = n(),
            Mean = round(mean(feelgreen,na.rm = T),2),
            SD = round(sd(feelgreen,na.rm = T),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

cond	N	Mean	SD
ctrl	78	77.55	19.40
lose	72	69.07	18.10
win	77	73.09	15.94

m <- t.test(feelgreen ~ cond,data = df_zss_elg %>% filter(cond != "ctrl"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. lose: t(141.7) = -1.43, p = 0.154, Lower CI = -9.56, Upper CI = 1.52, d = -0.24.

m <- t.test(feelgreen ~ cond,data = df_zss_elg %>% filter(cond != "win"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

lose vs. ctrl: t(147.98) = 2.77, p = 0.006, Lower CI = 2.43, Upper CI = 14.53, d = 0.46.

m <- t.test(feelgreen ~ cond,data = df_zss_elg %>% filter(cond != "lose"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. ctrl: t(148.13) = 1.57, p = 0.119, Lower CI = -1.17, Upper CI = 10.09, d = 0.26.

hmm, this is the opposite direction of what we’d expect. losing and winning in a zero-sum game, compared to control, decreases group feelings.

DV: Mean score of Blue feelings

df_zss_elg %>% 
  group_by(cond) %>% 
  summarise(N = n(),
            Mean = round(mean(feelblue,na.rm = T),2),
            SD = round(sd(feelblue,na.rm = T),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

cond	N	Mean	SD
ctrl	78	29.94	19.03
lose	72	39.84	19.20
win	77	49.04	18.00

m <- t.test(feelblue ~ cond,data = df_zss_elg %>% filter(cond != "ctrl"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. lose: t(144.49) = -3.01, p = 0.003, Lower CI = -15.24, Upper CI = -3.17, d = -0.5.

m <- t.test(feelblue ~ cond,data = df_zss_elg %>% filter(cond != "win"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

lose vs. ctrl: t(146.83) = -3.17, p = 0.002, Lower CI = -16.08, Upper CI = -3.73, d = -0.52.

m <- t.test(feelblue ~ cond,data = df_zss_elg %>% filter(cond != "lose"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. ctrl: t(152.72) = -6.42, p = 0, Lower CI = -24.98, Upper CI = -13.23, d = -1.04.

Oh. Interesting. Being on the winning side makes you feel positive feelings toward the losing outgroup. This is more like cross-group solidarity.

DV: Identification with greens

df_zss_elg %>% 
  group_by(cond) %>% 
  summarise(N = n(),
            Mean = round(mean(green_id,na.rm = T),2),
            SD = round(sd(green_id,na.rm = T),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

cond	N	Mean	SD
ctrl	78	79.65	21.24
lose	72	71.47	20.20
win	77	74.64	20.13

m <- t.test(green_id ~ cond,data = df_zss_elg %>% filter(cond != "ctrl"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. lose: t(146.26) = -0.96, p = 0.34, Lower CI = -9.7, Upper CI = 3.37, d = -0.16.

m <- t.test(green_id ~ cond,data = df_zss_elg %>% filter(cond != "win"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

lose vs. ctrl: t(147.87) = 2.42, p = 0.017, Lower CI = 1.49, Upper CI = 14.87, d = 0.4.

m <- t.test(green_id ~ cond,data = df_zss_elg %>% filter(cond != "lose"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. ctrl: t(152.74) = 1.51, p = 0.133, Lower CI = -1.55, Upper CI = 11.58, d = 0.24.

DV: Feeling connected with greens

df_zss_elg %>% 
  group_by(cond) %>% 
  summarise(N = n(),
            Mean = round(mean(green_connect,na.rm = T),2),
            SD = round(sd(green_connect,na.rm = T),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

cond	N	Mean	SD
ctrl	78	77.42	22.74
lose	72	68.85	20.04
win	77	73.92	17.83

m <- t.test(green_connect ~ cond,data = df_zss_elg %>% filter(cond != "ctrl"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. lose: t(142.21) = -1.63, p = 0.106, Lower CI = -11.23, Upper CI = 1.08, d = -0.27.

m <- t.test(green_connect ~ cond,data = df_zss_elg %>% filter(cond != "win"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

lose vs. ctrl: t(147.69) = 2.45, p = 0.015, Lower CI = 1.67, Upper CI = 15.48, d = 0.4.

m <- t.test(green_connect ~ cond,data = df_zss_elg %>% filter(cond != "lose"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. ctrl: t(145.58) = 1.07, p = 0.288, Lower CI = -2.98, Upper CI = 9.98, d = 0.18.

DV: Solidarity with greens

df_zss_elg %>% 
  group_by(cond) %>% 
  summarise(N = n(),
            Mean = round(mean(green_soli,na.rm = T),2),
            SD = round(sd(green_soli,na.rm = T),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

cond	N	Mean	SD
ctrl	78	79.64	21.12
lose	72	70.08	20.88
win	77	73.61	18.18

m <- t.test(green_soli ~ cond,data = df_zss_elg %>% filter(cond != "ctrl"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. lose: t(141.08) = -1.1, p = 0.275, Lower CI = -9.89, Upper CI = 2.83, d = -0.18.

m <- t.test(green_soli ~ cond,data = df_zss_elg %>% filter(cond != "win"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

lose vs. ctrl: t(147.29) = 2.79, p = 0.006, Lower CI = 2.78, Upper CI = 16.34, d = 0.46.

m <- t.test(green_soli ~ cond,data = df_zss_elg %>% filter(cond != "lose"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. ctrl: t(150.22) = 1.91, p = 0.059, Lower CI = -0.22, Upper CI = 12.28, d = 0.31.

DV: Valueing other greens

df_zss_elg %>% 
  group_by(cond) %>% 
  summarise(N = n(),
            Mean = round(mean(green_value,na.rm = T),2),
            SD = round(sd(green_value,na.rm = T),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

cond	N	Mean	SD
ctrl	78	76.41	19.73
lose	72	69.82	19.89
win	77	72.10	16.23

m <- t.test(green_value ~ cond,data = df_zss_elg %>% filter(cond != "ctrl"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. lose: t(137.26) = -0.77, p = 0.446, Lower CI = -8.19, Upper CI = 3.62, d = -0.13.

m <- t.test(green_value ~ cond,data = df_zss_elg %>% filter(cond != "win"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

lose vs. ctrl: t(146.85) = 2.04, p = 0.044, Lower CI = 0.19, Upper CI = 12.99, d = 0.34.

m <- t.test(green_value ~ cond,data = df_zss_elg %>% filter(cond != "lose"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. ctrl: t(148.21) = 1.48, p = 0.14, Lower CI = -1.43, Upper CI = 10.04, d = 0.24.

DV: Liking other greens

df_zss_elg %>% 
  group_by(cond) %>% 
  summarise(N = n(),
            Mean = round(mean(green_like,na.rm = T),2),
            SD = round(sd(green_like,na.rm = T),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

cond	N	Mean	SD
ctrl	78	74.62	20.76
lose	72	65.11	19.04
win	77	71.16	15.67

m <- t.test(green_like ~ cond,data = df_zss_elg %>% filter(cond != "ctrl"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. lose: t(137.78) = -2.11, p = 0.037, Lower CI = -11.71, Upper CI = -0.38, d = -0.36.

m <- t.test(green_like ~ cond,data = df_zss_elg %>% filter(cond != "win"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

lose vs. ctrl: t(147.99) = 2.92, p = 0.004, Lower CI = 3.08, Upper CI = 15.93, d = 0.48.

m <- t.test(green_like ~ cond,data = df_zss_elg %>% filter(cond != "lose"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. ctrl: t(143.18) = 1.17, p = 0.243, Lower CI = -2.38, Upper CI = 9.29, d = 0.2.

DV: Identification with blues

df_zss_elg %>% 
  group_by(cond) %>% 
  summarise(N = n(),
            Mean = round(mean(blue_id,na.rm = T),2),
            SD = round(sd(blue_id,na.rm = T),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

cond	N	Mean	SD
ctrl	78	23.64	22.06
lose	72	34.81	23.47
win	77	41.82	23.00

m <- t.test(blue_id ~ cond,data = df_zss_elg %>% filter(cond != "ctrl"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. lose: t(145.87) = -1.84, p = 0.068, Lower CI = -14.54, Upper CI = 0.52, d = -0.3.

m <- t.test(blue_id ~ cond,data = df_zss_elg %>% filter(cond != "win"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

lose vs. ctrl: t(145.07) = -3, p = 0.003, Lower CI = -18.53, Upper CI = -3.8, d = -0.5.

m <- t.test(blue_id ~ cond,data = df_zss_elg %>% filter(cond != "lose"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. ctrl: t(152.55) = -5.02, p = 0, Lower CI = -25.33, Upper CI = -11.02, d = -0.81.

DV: Feeling connected with blues

df_zss_elg %>% 
  group_by(cond) %>% 
  summarise(N = n(),
            Mean = round(mean(blue_connect,na.rm = T),2),
            SD = round(sd(blue_connect,na.rm = T),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

cond	N	Mean	SD
ctrl	78	26.63	22.94
lose	72	36.10	23.31
win	77	42.39	21.15

m <- t.test(blue_connect ~ cond,data = df_zss_elg %>% filter(cond != "ctrl"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. lose: t(143.15) = -1.72, p = 0.087, Lower CI = -13.52, Upper CI = 0.93, d = -0.29.

m <- t.test(blue_connect ~ cond,data = df_zss_elg %>% filter(cond != "win"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

lose vs. ctrl: t(146.63) = -2.5, p = 0.013, Lower CI = -16.94, Upper CI = -2, d = -0.41.

m <- t.test(blue_connect ~ cond,data = df_zss_elg %>% filter(cond != "lose"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. ctrl: t(152.3) = -4.45, p = 0, Lower CI = -22.76, Upper CI = -8.76, d = -0.72.

DV: Solidarity with blues

df_zss_elg %>% 
  group_by(cond) %>% 
  summarise(N = n(),
            Mean = round(mean(blue_soli,na.rm = T),2),
            SD = round(sd(blue_soli,na.rm = T),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

cond	N	Mean	SD
ctrl	78	23.90	21.46
lose	72	33.17	23.83
win	77	43.26	23.09

m <- t.test(blue_soli ~ cond,data = df_zss_elg %>% filter(cond != "ctrl"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. lose: t(145.57) = -2.62, p = 0.01, Lower CI = -17.7, Upper CI = -2.49, d = -0.43.

m <- t.test(blue_soli ~ cond,data = df_zss_elg %>% filter(cond != "win"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

lose vs. ctrl: t(143.15) = -2.5, p = 0.014, Lower CI = -16.61, Upper CI = -1.93, d = -0.42.

m <- t.test(blue_soli ~ cond,data = df_zss_elg %>% filter(cond != "lose"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. ctrl: t(151.88) = -5.41, p = 0, Lower CI = -26.44, Upper CI = -12.29, d = -0.88.

DV: Valueing other blues

df_zss_elg %>% 
  group_by(cond) %>% 
  summarise(N = n(),
            Mean = round(mean(blue_value,na.rm = T),2),
            SD = round(sd(blue_value,na.rm = T),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

cond	N	Mean	SD
ctrl	78	38.53	24.65
lose	72	47.54	20.69
win	77	59.09	22.61

m <- t.test(blue_value ~ cond,data = df_zss_elg %>% filter(cond != "ctrl"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. lose: t(146.94) = -3.26, p = 0.001, Lower CI = -18.56, Upper CI = -4.54, d = -0.54.

m <- t.test(blue_value ~ cond,data = df_zss_elg %>% filter(cond != "win"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

lose vs. ctrl: t(146.71) = -2.43, p = 0.016, Lower CI = -16.34, Upper CI = -1.69, d = -0.4.

m <- t.test(blue_value ~ cond,data = df_zss_elg %>% filter(cond != "lose"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. ctrl: t(152.18) = -5.41, p = 0, Lower CI = -28.07, Upper CI = -13.06, d = -0.88.

DV: Liking other blues

df_zss_elg %>% 
  group_by(cond) %>% 
  summarise(N = n(),
            Mean = round(mean(blue_like,na.rm = T),2),
            SD = round(sd(blue_like,na.rm = T),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

cond	N	Mean	SD
ctrl	78	37.00	23.40
lose	72	47.60	18.45
win	77	58.66	18.49

m <- t.test(blue_like ~ cond,data = df_zss_elg %>% filter(cond != "ctrl"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. lose: t(146.38) = -3.65, p = 0, Lower CI = -17.05, Upper CI = -5.08, d = -0.6.

m <- t.test(blue_like ~ cond,data = df_zss_elg %>% filter(cond != "win"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

lose vs. ctrl: t(144.55) = -3.09, p = 0.002, Lower CI = -17.37, Upper CI = -3.82, d = -0.51.

m <- t.test(blue_like ~ cond,data = df_zss_elg %>% filter(cond != "lose"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. ctrl: t(146.04) = -6.4, p = 0, Lower CI = -28.35, Upper CI = -14.97, d = -1.06.

DV: Threat

df_zss_elg %>% 
  group_by(cond) %>% 
  summarise(N = n(),
            Mean = round(mean(threat,na.rm = T),2),
            SD = round(sd(threat,na.rm = T),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

cond	N	Mean	SD
ctrl	78	2.38	1.19
lose	72	2.36	0.88
win	77	1.55	0.80

m <- t.test(threat ~ cond,data = df_zss_elg %>% filter(cond != "ctrl"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. lose: t(143.58) = 5.91, p = 0, Lower CI = 0.54, Upper CI = 1.09, d = 0.99.

m <- t.test(threat ~ cond,data = df_zss_elg %>% filter(cond != "win"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

lose vs. ctrl: t(141.39) = 0.14, p = 0.89, Lower CI = -0.31, Upper CI = 0.36, d = 0.02.

m <- t.test(threat ~ cond,data = df_zss_elg %>% filter(cond != "lose"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. ctrl: t(135.52) = 5.16, p = 0, Lower CI = 0.52, Upper CI = 1.16, d = 0.89.

DV: Enemy

df_zss_elg %>% 
  group_by(cond) %>% 
  summarise(N = n(),
            Mean = round(mean(enemy,na.rm = T),2),
            SD = round(sd(enemy,na.rm = T),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

cond	N	Mean	SD
ctrl	78	2.45	1.30
lose	72	2.08	0.90
win	77	1.69	0.75

m <- t.test(enemy ~ cond,data = df_zss_elg %>% filter(cond != "ctrl"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. lose: t(138.4) = 2.9, p = 0.004, Lower CI = 0.13, Upper CI = 0.66, d = 0.49.

m <- t.test(enemy ~ cond,data = df_zss_elg %>% filter(cond != "win"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

lose vs. ctrl: t(137.71) = 2.02, p = 0.046, Lower CI = 0.01, Upper CI = 0.72, d = 0.34.

m <- t.test(enemy ~ cond,data = df_zss_elg %>% filter(cond != "lose"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. ctrl: t(123.5) = 4.48, p = 0, Lower CI = 0.42, Upper CI = 1.1, d = 0.81.

DV: Perceived groups (green and blue are same group)

df_zss_elg %>% 
  group_by(cond) %>% 
  summarise(N = n(),
            Mean = round(mean(shared,na.rm = T),2),
            SD = round(sd(shared,na.rm = T),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

cond	N	Mean	SD
ctrl	78	2.27	1.29
lose	72	2.18	0.97
win	77	2.61	1.23

m <- t.test(shared ~ cond,data = df_zss_elg %>% filter(cond != "ctrl"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. lose: t(143.08) = -2.38, p = 0.019, Lower CI = -0.79, Upper CI = -0.07, d = -0.4.

m <- t.test(shared ~ cond,data = df_zss_elg %>% filter(cond != "win"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

lose vs. ctrl: t(142.41) = 0.48, p = 0.633, Lower CI = -0.28, Upper CI = 0.45, d = 0.08.

m <- t.test(shared ~ cond,data = df_zss_elg %>% filter(cond != "lose"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. ctrl: t(152.82) = -1.69, p = 0.093, Lower CI = -0.74, Upper CI = 0.06, d = -0.27.

DV: Linked fate

df_zss_elg %>% 
  group_by(cond) %>% 
  summarise(N = n(),
            Mean = round(mean(linked,na.rm = T),2),
            SD = round(sd(linked,na.rm = T),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

cond	N	Mean	SD
ctrl	78	3.49	1.11
lose	72	3.21	1.14
win	77	3.43	1.04

m <- t.test(linked ~ cond,data = df_zss_elg %>% filter(cond != "ctrl"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. lose: t(143.65) = -1.23, p = 0.221, Lower CI = -0.57, Upper CI = 0.13, d = -0.21.

m <- t.test(linked ~ cond,data = df_zss_elg %>% filter(cond != "win"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

lose vs. ctrl: t(146.48) = 1.52, p = 0.132, Lower CI = -0.08, Upper CI = 0.64, d = 0.25.

m <- t.test(linked ~ cond,data = df_zss_elg %>% filter(cond != "lose"))
d_mod <- cohens_d(m)
d = d_mod[1,1]

win vs. ctrl: t(152.6) = 0.34, p = 0.736, Lower CI = -0.28, Upper CI = 0.4, d = 0.05.