Background

The purpose of Study 1 is to provide discriminant and internal validity to our theoretical model: Those with a competitive worldview expect dominance to incur less relationship harm and, in turn, they are more likely to behave dominantly.

We want to make sure the design properly addresses the following:

Competitive worldview, relationship expectancies of dominance, and dominance are all measured correctly. To that end, we only use well-validated scales: a. Competitive worldview: a view of the social world as a constant battle for status and resources (It’s a dog-eat-dog world where you have to be ruthless at times; Perry et al., 2013). b. Dominance strategies: use of dominant strategies at attain and maintain status at work (I am willing to use aggressive tactics to get my way at work; Cheng et al., 2010). c. Relationship expectancies of dominance strategies: the same behaviors of the dominance strategies scale were assessed for their perceived impact on relationships with others at work.
It is competitive worldview that predicts relationship expectancies and dominance, over and above related constructs (that have been shown in the past to predict dominance). Therefore, we statistically control for the following: a. Status zero-sum beliefs: the belief that if one gains status, others lose status (Andrews-Fearon & Davidai, 2023). b. Cooperative primal beliefs: the belief that people are naturally and inherently cooperative ( Clifton et al., 2019). c. Coercive theories power: lay theories according to which the way to maintain power is through coercion, intimidation, and force (ten Brinke & Keltner, 2022). d. Collaborative theories of power: lay theories according to which the way to maintain power is through virtue, respect, and empathy (ten Brinke & Keltner, 2022).
It is relationship expectancies, and not relationship motivations or influence expectancies, that is driving the effect of competitive worldview on dominance strategies at work. To that end, we measure: a. Relationship motivations: two items measuring the extent to which participants care about having good relationships at work. b. Influence expectancies of dominance strategies: the same behaviors of the dominance strategies scale were assessed for their perceived impact on influence over others at work.

We also want to be able to ask about people’s daily lives, so we limit our sample to full- and part-time employees.

Preregistered hypotheses

This study was preregistered here: Open Science Framework

Competitive worldview will be positively associated with self-reported dominance strategies.
Competitive worldview will be positively associated with relationship expectancies of dominance strategies.
The relationship between competitive worldview and self dominance strategies will be–at least partially–explained by relationship expectancies of dominance strategies.

Data collection

We collected data on Jan. 27th, 2024 through Connect by CloudResearch; an online participant recruitment platform. We restricted our sample to full- and part-time employees. The target sample was set at 300 participants, lending us sufficient statistical power to find our hypothesized effect.

We excluded participants who failed two preregistered attention checks, as well as those who got through the platforms filter despite not being full- or part-time employes.

n_eligible <- df_s1 %>% 
  group_by(elg) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  filter(elg == 1) %>% 
  select(N) %>% 
  unlist() %>% 
  unname()

passed_checks <- df_s1 %>% 
  group_by(att_1,att_2) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  filter(att_1 == 1 & att_2 == 1) %>% 
  select(N) %>% 
  unlist() %>% 
  unname()

df_s1 %>% 
  group_by(att_1,att_2) %>% 
  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	att_2	N	Perc
0	0	1	0.33
0	1	7	2.33
1	0	13	4.33
1	1	279	93.00

279 participants passed both attention checks.

df_s1 %>% 
  filter(att_1 == 1 & att_2 == 1) %>% 
  group_by(employment) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

employment	N	Perc
Full-time	239	85.66
Full-time, Student	2	0.72
Homemaker	1	0.36
Other	2	0.72
Part-time	32	11.47
Retired	2	0.72
Unemployed	1	0.36

4 are no longer employed.

That leaves us with 275 eligible participants.

Demographics

Race/Ethnicity

df_s1_elg %>% 
  group_by(race) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  arrange(desc(Perc)) %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

race	N	Perc
white	190	69.09
black	29	10.55
asian	26	9.45
multiracial	22	8.00
hispanic	7	2.55
NA	1	0.36

Gender

df_s1_elg %>% 
  mutate(gender = ifelse(is.na(gender) | gender == "","other",gender)) %>% 
  group_by(gender) %>% 
  summarise(N = n()) %>% 
  ungroup() %>% 
  mutate(Perc = round(100*(N/sum(N)),2)) %>% 
  ungroup() %>% 
  arrange(desc(Perc)) %>% 
  kbl() %>% 
  kable_styling(bootstrap_options = "hover",
                full_width = F,
                position = "left")

gender	N	Perc
man	155	56.36
woman	118	42.91
other	2	0.73

Age

df_s1_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
38.31	10.39

Education

df_s1_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
GED	55	20.00
2yearColl	37	13.45
4yearColl	121	44.00
MA	44	16.00
PHD	16	5.82
NA	2	0.73

Income

median_income_num <- df_s1_elg %>% 
  summarise(median = median(income_num,na.rm = T))

median_income <- median_income_num %>% 
  mutate(income_char = case_when(median == 1 ~ "$0-$20,000",
                                 median == 2 ~ "$20,001-$40,000",
                                 median == 3 ~ "$40,001-$60,000",
                                 median == 4 ~ "$60,001-$80,000",
                                 median == 5 ~ "$80,001-$100,000",
                                 median == 6 ~ "$100,001-$120,000",
                                 median == 7 ~ "$120,001-$140,000",
                                 median == 8 ~ "$140,001-$160,000",
                                 median == 9 ~ "$160,001-$180,000",
                                 median == 10 ~ "$180,001-$200,000",
                                 median == 11 ~ "Over $200,000")) %>% 
  select(income_char) %>% 
  unlist() %>% 
  unname()

df_s1_elg %>%
  ggplot(aes(x = income, fill = income == median_income)) +
  geom_bar(show.legend = FALSE) +
  scale_fill_manual(values = c("FALSE" = "grey66", "TRUE" = "red")) +
  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()

Median income: $60,001-$80,000.

Primary measures

Competitive worldview

From Perry et al., 2013

1 = Strongly Disagree to 7 = Strongly Agree

It’s a dog-eat-dog world where you have to be ruthless at times
Life is not governed by the “survival of the fittest.” We should let compassion and moral laws be our guide [R]
There is really no such thing as “right” and “wrong.” It all boils down to what you can get away with
One of the most useful skills a person should develop is how to look someone straight in the eye and lie convincingly
It is better to be loved than to be feared [R]
My knowledge and experience tell me that the social world we live in is basically a competitive “jungle” in which the fittest survive and succeed, in which power, wealth, and winning are everything, and might is right
Do unto others as you would have them do unto you, and never do anything unfair to someone else [R]
Basically people are objects to be quietly and coolly manipulated for one’s own benefit
Honesty is the best policy in all cases [R]
One should give others the benefit of the doubt. Most people are trustworthy if you have faith in them [R]

R indicates a reverse-scored item.

Cronbach’s alpha = 0.83

df_s1_elg %>% 
  ggplot(aes(x = CWV)) +
  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_s1_elg$CWV,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"))

Cooperative primal beliefs

From Clifton et al., 2019

1 = Strongly Disagree to 7 = Strongly Agree

For all life—from the smallest organisms, to plants, animals, and for people too—everything is a cut-throat competition [R]
Instead of being cooperative, life is a brutal contest where you’ve got to do whatever it takes to survive [R]
Instead of being cooperative, the world is a cut-throat and competitive place [R]
The world runs on trust and cooperation way more than suspicion and competition

R indicates a reverse-scored item.

Cronbach’s alpha = 0.86

df_s1_elg %>% 
  ggplot(aes(x = copri)) +
  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_s1_elg$copri,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"))

Status zero-sum beliefs

From Andrews-Fearon & Davidai, 2023

1 = Strongly Disagree to 7 = Strongly Agree

When status for one person is increasing it means that status for another person is decreasing
Status is a limited good—when one person gains in status it inevitably comes at another person’s expense
When one person moves up the social hierarchy it means that another person has to move down the hierarchy
If someone wants to move up the social hierarchy, they have to do so at someone else’s expense
Status is not a finite resource [R]
When one person has a lot of status it doesn’t mean that someone else lacks status [R]
Not everyone can be high status. If one person has higher status, someone else must have lower status
When one person gains in status, it does not mean that someone else is losing status [R]

R indicates a reverse-scored item.

Cronbach’s alpha = 0.89

df_s1_elg %>% 
  ggplot(aes(x = ZSB)) +
  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_s1_elg$ZSB,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"))

Coercive theories of power

From ten Brinke & Keltner, 2022

1 = Strongly Disagree to 7 = Strongly Agree

Maintaining power requires ruthlessness
People keep power by being feared by others
People gain power through the use of manipulation and deception
People mainly gain power by force
To maintain power, a person must be willing to do whatever is necessary, including breaking the rules, using force, and coercion
People most typically gain power by reducing the status of other people
Often it requires aggression to gain power
An influential individual is typically intimidating
Having power means always having the “final say”
Power is usually vertically arranged, with a few people at the top having most of the influence and many at the bottom having little to none

R indicates a reverse-scored item.

Cronbach’s alpha = 0.91

df_s1_elg %>% 
  ggplot(aes(x = TOPS_coer)) +
  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_s1_elg$TOPS_coer,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"))

Collaborative theories of power

From ten Brinke & Keltner, 2022

1 = Strongly Disagree to 7 = Strongly Agree

Maintaining power requires the ability to collaborate and compromise with others
Maintaining power requires compassion for others
People rise in power through virtue and respect
Having high ethical and moral standards is necessary to keep power
Powerful individuals focus on the needs of group members
Influential individuals need to be approachable and empathetic
Gaining power requires collaboration with other individuals
People most typically gain power by being given responsibilities and opportunities by others
In a group, there can be many influential people
Power is often shared by many individuals in a group

R indicates a reverse-scored item.

Cronbach’s alpha = 0.89

df_s1_elg %>% 
  ggplot(aes(x = TOPS_coll)) +
  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_s1_elg$TOPS_coll,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"))

Dominance strategies

From Cheng et al., 2010

We’re now going to shift to some of your other experiences at work.

Please indicate the extent to which each statement below accurately describes you at work, using any of the points on the 7 point scale…

1 = Not at all to 7 = Very much

I enjoy (or would enjoy) having control over others at work
I often try to get my own way at work regardless of what others may want
I am willing to use aggressive tactics to get my way at work
I try to control others rather than permit them to control me at work
I do NOT have a forceful or dominant personality at work [R]
Others know it is better to let me have my way at work
I do NOT enjoying having authority over other people at work [R]
Some people at work are afraid of me
Others at work do NOT enjoying hanging out with me

R indicates a reverse-scored item.

Cronbach’s alpha = 0.86

df_s1_elg %>% 
  ggplot(aes(x = self_dominance)) +
  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_s1_elg$self_dominance,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"))

Relationship expectancies of dominance

Adapted from Cheng et al., 2010

What leads someone to have good relationships?

Below, we list some attributes and behaviors that a person might display in a group of other people. What do you think is the impact of each of these things on whether that person has good relationships with others in that group?

1 = Strong negative effect on relationships to 7 = Strong positive effect on relationships

Enjoying having control over other members of the group
Often trying to get their own way regardless of what others in the group may want
Being willing to use aggressive tactics to get their way
Trying to control others rather than permit others to control them
NOT having a forceful or dominant personality [R]
Having members of the group know it is better to let him/her have his/her way
NOT enjoying having authority over other members of the group [R]
Having members of their group being afraid of them
Others NOT enjoying hanging out with them

R indicates a reverse-scored item.

Cronbach’s alpha = 0.85

df_s1_elg %>% 
  ggplot(aes(x = rel_dominance)) +
  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_s1_elg$rel_dominance,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"))

Influence expectancies of dominance

Adapted from Cheng et al., 2010

What leads someone to have influence?

1 = Strong negative effect on influence to 7 = Strong positive effect on influence

Enjoying having control over other members of the group
Often trying to get their own way regardless of what others in the group may want
Being willing to use aggressive tactics to get their way
Trying to control others rather than permit others to control them
NOT having a forceful or dominant personality [R]
Having members of the group know it is better to let him/her have his/her way
NOT enjoying having authority over other members of the group [R]
Having members of their group being afraid of them
Others NOT enjoying hanging out with them

R indicates a reverse-scored item.

Cronbach’s alpha = 0.86

df_s1_elg %>% 
  ggplot(aes(x = infl_dominance)) +
  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_s1_elg$infl_dominance,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"))

Relationship motivations at work

Mean score of the following two items:

In your work life, to what extent do you care about having good relationships with the people you work with? (1 = I don’t care about this at all to 5 = I care about this a great deal)
In your work life, to what extent would it bother you if you did NOT have good relationships with other people at work? (1 = I would not be bothered at all if I didn’t have good relationships to 5 = I would be greatly bothered if I didn’t have good relationships)

r = 0.64

df_s1_elg %>% 
  ggplot(aes(x = care_rel)) +
  geom_density(fill = "lightblue",
                 color = NA) +
  scale_x_continuous(breaks = seq(1,5,1),
                     limits = c(1,5)) +
  ylab("density") +
  geom_vline(xintercept = mean(df_s1_elg$care_rel,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"))

Additional measures

Prestige strategies

From Cheng et al., 2010

We’re now going to shift to some of your other experiences at work.

Please indicate the extent to which each statement below accurately describes you at work, using any of the points on the 7 point scale…

1 = Not at all to 7 = Very much

My peers at work respect and admire me
Others at work always expect me to be successful
Others do NOT value my opinion at work [R]
I am held in high esteem by those I know at work
I am considered an expert on some matters by others at work
My unique talents and abilities are recognized by others at work
Others seek my advice on a variety of matters at work

R indicates a reverse-scored item.

Cronbach’s alpha = 0.88

df_s1_elg %>% 
  ggplot(aes(x = self_prestige)) +
  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_s1_elg$self_prestige,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"))

Relationship expectancies of prestige

Adapted from Cheng et al., 2010

What leads someone to have good relationships?

1 = Strong negative effect on relationships to 7 = Strong positive effect on relationships

Having members of their group respect and admire them
Having members of their group always expect him/her to be successful.
Having members of their group do NOT value their opinion [R]
Being held in high esteem by members of the group
Being considered an expert on some matters by members of the group
Having their unique talents and abilities are recognized by others in the group
Having members of their group seek their advice on a variety of matters

R indicates a reverse-scored item.

Cronbach’s alpha = 0.76

df_s1_elg %>% 
  ggplot(aes(x = rel_prestige)) +
  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_s1_elg$rel_prestige,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"))

Influence expectancies of prestige

Adapted from Cheng et al., 2010

What leads someone to have influence?

1 = Strong negative effect on influence to 7 = Strong positive effect on influence

Having members of their group respect and admire them
Having members of their group always expect him/her to be successful.
Having members of their group do NOT value their opinion [R]
Being held in high esteem by members of the group
Being considered an expert on some matters by members of the group
Having their unique talents and abilities are recognized by others in the group
Having members of their group seek their advice on a variety of matters

R indicates a reverse-scored item.

Cronbach’s alpha = 0.82

df_s1_elg %>% 
  ggplot(aes(x = infl_prestige)) +
  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_s1_elg$infl_prestige,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"))

Influence motivations at work

Mean score of the following two items:

In your work life, to what extent do you care about having influence over the people you work with? (1 = I don’t care about having influence at all to 5 = I care about having influence a great deal)
In your work life, to what extent would it bother you if you did NOT have much influence of other people at work? (1 = I would not be bothered at all if I didn’t have influence to 5 = I would be greatly bothered if I didn’t have influence)

r = 0.69

df_s1_elg %>% 
  ggplot(aes(x = care_infl)) +
  geom_density(fill = "lightblue",
                 color = NA) +
  scale_x_continuous(breaks = seq(1,5,1),
                     limits = c(1,5)) +
  ylab("density") +
  geom_vline(xintercept = mean(df_s1_elg$care_infl,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

EXPLORE CORRELATIONS ↗

df_s1_elg %>% 
  dplyr::select(CWV,
                ZSB,
                copri,
                self_dominance,
                self_prestige,
                rel_dominance,
                rel_prestige,
                infl_dominance,
                infl_prestige,
                TOPS_coer,
                TOPS_coll,
                care_rel,
                care_infl) %>%
  corPlot(upper = TRUE,stars = TRUE,xsrt = 270)

Key:

CWV: Competitive worldview
ZSB: Status zero-sum beliefs
copri: Cooperative primal beliefs
self_dominance: Dominance strategies
self_prestige: Prestige strategies
rel_dominance: Relationship expectancies of dominance
rel_prestige: Relationship expectancies of prestige
infl_dominance: Influence expectancies of dominance
infl_prestige: Influence expectancies of prestige
TOPS_coer: Coercive theories of power
TOPS_coll: Collaborative theories of power
care_rel: Relationship motivations at work
care_infl: Influence motivations at work

Linear Models

Here, I report the models that ended up in the paper. Feel free to explore different linear models:

EXPLORE MODELS ↗

Linear Model 1

Predictor: Competitive worldview.

Outcome: Dominance strategies.

Controls: Status zero-sum beliefs, cooperative primal beliefs, relationship concerns, coercive theories of power, collaborative theories of power, age, race (white = 1; non-white = 0), gender (man = 1; non-man = 0), education (numeric), income (numeric).

term	estimate	conf.int	statistic	df	p.value	eta2
Intercept	-1.47	[-2.96, 0.02]	-1.94	254	.053	NA
CWV	0.75	[0.58, 0.91]	8.85	254	< .001	0.324
ZSB	-0.02	[-0.13, 0.09]	-0.40	254	.688	0.008
Copri	0.08	[-0.02, 0.19]	1.51	254	.131	0.011
Rel care	0.05	[-0.08, 0.18]	0.79	254	.429	0.012
TOPS coer	0.07	[-0.05, 0.18]	1.09	254	.278	0.000
TOPS coll	0.19	[0.06, 0.33]	2.83	254	.005	0.029
Age	0.00	[-0.01, 0.01]	-0.24	254	.808	0.000
White	0.08	[-0.16, 0.31]	0.63	254	.526	0.001
Man	0.10	[-0.11, 0.32]	0.95	254	.343	0.004
Edu num	0.04	[-0.07, 0.14]	0.69	254	.488	0.005
Income num	0.02	[-0.02, 0.07]	0.98	254	.326	0.004

Linear Model 2

Predictor: Competitive worldview.

Outcome: Relationship expectancies of dominance.

term	estimate	conf.int	statistic	df	p.value	eta2
Intercept	-1.43	[-2.63, -0.23]	-2.35	254	.020	NA
CWV	0.60	[0.47, 0.73]	8.85	254	< .001	0.318
ZSB	0.06	[-0.03, 0.15]	1.34	254	.182	0.001
Copri	0.19	[0.10, 0.27]	4.24	254	< .001	0.074
Rel care	-0.03	[-0.14, 0.07]	-0.63	254	.529	0.000
TOPS coer	0.09	[0.00, 0.19]	1.96	254	.051	0.008
TOPS coll	0.09	[-0.02, 0.20]	1.57	254	.117	0.005
Age	0.00	[-0.01, 0.01]	0.16	254	.874	0.001
White	0.23	[0.04, 0.42]	2.42	254	.016	0.022
Man	0.19	[0.01, 0.36]	2.13	254	.034	0.016
Edu num	0.05	[-0.04, 0.13]	1.08	254	.281	0.003
Income num	-0.02	[-0.05, 0.02]	-0.84	254	.400	0.003

Mediation Models

Here, I report the mediation models from the paper. Feel free to explore more:

EXPLORE MEDIATION MODELS ↗

Mediation Model 1

Predictor: Competitive worldview.

Mediator: Relationship expectancies.

Outcome: Dominance strategies.

Model specification: 10,000-bootstrap mediation model, using the mediation package.

Effect	Estimate	X95..CI.Lower	X95..CI.Upper
ACME (indirect)	0.334	0.221	0.461
ADE (direct)	0.412	0.216	0.603
Total Effect	0.746	0.565	0.922
Prop. Mediated	0.448	0.293	0.655

Mediation Model 2

Predictor: Competitive worldview.

Mediators: Relationship expectancies (1-path), influence expectancies (2-path).

Outcome: Dominance strategies.

Model specification: 10,000-bootstrapped simultaneous mediation model, using the lavaan package.

label	est	se	ci.lower	ci.upper	pvalue
a1	0.6006189	0.0705352	0.4662157	0.7430874	0.0000000
a2	0.4233135	0.1021868	0.2205351	0.6190881	0.0000343
b1	0.5116453	0.0884293	0.3370414	0.6825413	0.0000000
b2	0.0842991	0.0600858	-0.0301597	0.2064034	0.1606232
c_prime	0.4025318	0.0966363	0.2171641	0.5914590	0.0000311
ind1	0.3073039	0.0644918	0.1878413	0.4406249	0.0000019
ind2	0.0356850	0.0263695	-0.0138688	0.0922527	0.1759701
ind_total	0.3429888	0.0611032	0.2301861	0.4687316	0.0000000
total	0.7455206	0.0898818	0.5677739	0.9227622	0.0000000

Dominance Through the Lens of a Competitive Worldview

Study 1: Analysis Report

Background

Preregistered hypotheses

Data collection

Demographics

Race/Ethnicity

Gender

Age

Education

Income

Primary measures

Competitive worldview

Cooperative primal beliefs

Status zero-sum beliefs

Coercive theories of power

Collaborative theories of power

Dominance strategies

Relationship expectancies of dominance

Influence expectancies of dominance

Relationship motivations at work

Additional measures

Prestige strategies

Relationship expectancies of prestige

Influence expectancies of prestige

Influence motivations at work

Correlations

Linear Models

Linear Model 1

Linear Model 2

Mediation Models

Mediation Model 1

Mediation Model 2