Load packages

library(tidyverse)

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## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.4     
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Load data

data <- read.csv("~/Google drive/My Drive/YEAR 2/PROJECTS/DEREK/Zero Sum Beliefs & Inclusivity/Study 1/data_June_6_2025.csv")

Exclusions

data <- data %>% 
  slice(-c(1:6)) %>% 
  filter(attn_bots != "14285733") %>% 
  filter(attn == 24)  #%>%
  # unite(geolocation, LocationLatitude, LocationLongitude) %>%
   #group_by(geolocation) %>%
  # mutate(geo_frequency = n()) %>%
   #filter(geo_frequency < 3) %>%
  # ungroup()

# We lose half the data with geolocation exclusion

Data cleaning

Gather and clean numeric data

df_numeric <- data %>%
  select(c(ResponseId, zsm_1:inclusive_5, coop_1:sdo_8R, trust_1:trust_5, help_type_bipolar:jwb_personal_7, Extra_1:Open_10R, SWL)) %>% 
  mutate(across(-ResponseId, as.numeric))

df_numeric <- df_numeric %>%
  mutate(across(ends_with("R"), ~ 8 - ., .names = "{.col}_Recoded")) %>% 
  select(-help_type_bipolar_Recoded)

Create average scores

df_numeric <- df_numeric %>%
  mutate(
    zsm_avg = rowMeans(select(., 
      zsm_1, 
      zsm_2, 
      zsm_3, 
      zsm_4_Recoded, 
      zsm_5, 
      zsm_6, 
      zsm_7_Recoded
    ), na.rm = TRUE)
  )

df_numeric <- df_numeric %>%
  mutate(
    bzs_avg = rowMeans(select(., 
      bzs_1, 
      bzs_2, 
      bzs_3, 
      bzs_4, 
      bzs_5, 
      bzs_6
    ), na.rm = TRUE)
  )

df_numeric <- df_numeric %>%
  mutate(
    inclusive_avg = rowMeans(select(., 
      inclusive_1, 
      inclusive_2, 
      inclusive_3, 
      inclusive_4, 
      inclusive_5
    ), na.rm = TRUE)
  )

df_numeric <- df_numeric %>%
  mutate(
    coop_avg = rowMeans(select(., 
      coop_1, 
      coop_2_Recoded
    ), na.rm = TRUE)
  )

df_numeric <- df_numeric %>%
  mutate(
    comp_avg = rowMeans(select(., 
      hyper_comp_1, 
      hyper_comp_2,
      hyper_comp_3
    ), na.rm = TRUE)
  )

df_numeric <- df_numeric %>%
  mutate(
    sdo_avg = rowMeans(select(., 
      sdo_1, 
      sdo_2,
      sdo_3R_Recoded,
      sdo_4R_Recoded, 
      sdo_5,
      sdo_6,
      sdo_7R_Recoded,
      sdo_8R_Recoded
    ), na.rm = TRUE)
  )

df_numeric <- df_numeric %>%
  mutate(
    trust_avg = rowMeans(select(., 
      trust_1, 
      trust_2,
      trust_3,
      trust_4,
      trust_5
    ), na.rm = TRUE)
  )

df_numeric <- df_numeric %>%
  mutate(
    sra_avg = rowMeans(select(., 
      sra_1,
      sra_2,
      sra_3,
      sra_4,
      sra_6,
      sra_9
    ), na.rm = TRUE)
  )

df_numeric <- df_numeric %>%
  mutate(
    jwb_general_avg = rowMeans(select(., 
      jwb_general_1,
      jwb_general_2,
      jwb_general_3,
      jwb_general_4,
      jwb_general_5,
      jwb_general_6
    ), na.rm = TRUE)
  )

df_numeric <- df_numeric %>%
  mutate(
    jwb_personal_avg = rowMeans(select(., 
      jwb_personal_1,
      jwb_personal_2,
      jwb_personal_3,
      jwb_personal_4,
      jwb_personal_5,
      jwb_personal_6,
      jwb_personal_7
    ), na.rm = TRUE)
  )

df_numeric <- df_numeric %>% 
  mutate(
    extraversion = rowMeans(select(., Extra_1, Extra_6R_Recoded), na.rm = TRUE),
    agreeableness = rowMeans(select(., Agree_2, Agree_7R_Recoded), na.rm = TRUE),
    conscientiousness = rowMeans(select(., Con_3, Con_8R_Recoded), na.rm = TRUE),
    emotional_stability = rowMeans(select(., Neuro_4, Neuro_9R_Recoded), na.rm = TRUE),
    openness = rowMeans(select(., Open_5, Open_10R_Recoded), na.rm = TRUE)
  )

Gather and clean demographic information

df_demo <- data %>% 
  select(c(ResponseId, gender:edu)) %>% 
  filter(gender == 1 | gender == 2) %>% 
  mutate(gender = ifelse(gender == 1, "man", "woman")) %>% 
  select(-gender_4_TEXT) %>% 
  filter(race == 1 | race == 2) %>% 
  mutate(race = ifelse(race == 1, "white", "black")) %>% 
  select(-c(race_8_TEXT, race_9_TEXT)) %>% 
  mutate(across(-c(ResponseId, gender, race, ancestry), as.numeric))

Combine

df_full <- df_demo %>% 
  left_join(df_numeric, by = "ResponseId")

Analysis

1. Do women and black participants have a higher zero sum mindset than male and white participants in our sample?

Zero sum mindset measure

summary_table <- df_full %>%
  group_by(gender, race) %>%
  summarise(mean_zsm = mean(zsm_avg, na.rm = TRUE)) %>%
  ungroup()

## `summarise()` has grouped output by 'gender'. You can override using the
## `.groups` argument.

print(summary_table)

## # A tibble: 4 × 3
##   gender race  mean_zsm
##   <chr>  <chr>    <dbl>
## 1 man    black     2.97
## 2 man    white     2.68
## 3 woman  black     3.03
## 4 woman  white     2.99

Chinoy ZSB measure

summary_table <- df_full %>%
  group_by(gender, race) %>%
  summarise(mean_zsb_chinoy = mean(zsb_chinoy_1, na.rm = TRUE)) %>%
  ungroup()

## `summarise()` has grouped output by 'gender'. You can override using the
## `.groups` argument.

print(summary_table)

## # A tibble: 4 × 3
##   gender race  mean_zsb_chinoy
##   <chr>  <chr>           <dbl>
## 1 man    black            3.22
## 2 man    white            2.91
## 3 woman  black            3.56
## 4 woman  white            3.52

Davidai ZSB measure

summary_table <- df_full %>%
  group_by(gender, race) %>%
  summarise(mean_zsb_davidai = mean(bzs_avg, na.rm = TRUE)) %>%
  ungroup()

## `summarise()` has grouped output by 'gender'. You can override using the
## `.groups` argument.

print(summary_table)

## # A tibble: 4 × 3
##   gender race  mean_zsb_davidai
##   <chr>  <chr>            <dbl>
## 1 man    black             5.31
## 2 man    white             4.73
## 3 woman  black             5.07
## 4 woman  white             4.83

Focusing on ZSM going forward, but can always pull in the Chinoy or Davidai in future analyses.

2. Are we seeing a ceiling effect for inclusive behavior?

df_full %>%
  summarise(
    mean = mean(inclusive_avg, na.rm = TRUE),
    sd = sd(inclusive_avg, na.rm = TRUE),
    min = min(inclusive_avg, na.rm = TRUE),
    max = max(inclusive_avg, na.rm = TRUE),
    n = sum(!is.na(inclusive_avg))
  )

##       mean       sd min max   n
## 1 5.478981 1.161604   1   7 471

ggplot(df_full, aes(x = inclusive_avg)) +
  geom_histogram(binwidth = 0.2, fill = "steelblue", color = "white") +
  labs(
    title = "Distribution of Inclusivity Reports",
    x = "Inclusivity Average",
    y = "Frequency"
  ) +
  theme_minimal()

## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_bin()`).

Yeah… As expected. Some variance but very skewed.

3. Do people’s zero-sum mindsets predict the extent to which they perceive themselves as inclusive?

model1 <- lm(inclusive_avg ~ zsm_avg,
                 data = df_full)

summary(model1)

## 
## Call:
## lm(formula = inclusive_avg ~ zsm_avg, data = df_full)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7936 -0.5473  0.1936  0.7997  1.9460 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  5.95808    0.14699  40.534  < 2e-16 ***
## zsm_avg     -0.16444    0.04707  -3.493 0.000522 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.148 on 469 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.02536,    Adjusted R-squared:  0.02328 
## F-statistic:  12.2 on 1 and 469 DF,  p-value: 0.000522

Yes. This varies from the LEAD data. The higher their zero-sum mindset, the less inclusive they report being.

4a. What does this relationship look like across white / black participants?

white_sample <- df_full %>% 
  filter(race == "white")

black_sample <- df_full %>% 
  filter(race == "black")

model2 <- lm(inclusive_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + extraversion + openness + emotional_stability + agreeableness + conscientiousness,
                 data = white_sample)

summary(model2)

## 
## Call:
## lm(formula = inclusive_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + 
##     extraversion + openness + emotional_stability + agreeableness + 
##     conscientiousness, data = white_sample)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2370 -0.6054  0.0768  0.7241  2.2186 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          3.87725    0.73343   5.286 2.82e-07 ***
## zsm_avg             -0.06593    0.06159  -1.070  0.28550    
## jwb_general_avg      0.06145    0.08419   0.730  0.46615    
## jwb_personal_avg     0.05863    0.09217   0.636  0.52529    
## extraversion         0.07631    0.05134   1.486  0.13848    
## openness             0.20511    0.05678   3.612  0.00037 ***
## emotional_stability  0.08380    0.05712   1.467  0.14367    
## agreeableness       -0.36914    0.06985  -5.285 2.84e-07 ***
## conscientiousness    0.08032    0.07359   1.091  0.27617    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.019 on 238 degrees of freedom
## Multiple R-squared:  0.2929, Adjusted R-squared:  0.2691 
## F-statistic: 12.32 on 8 and 238 DF,  p-value: 9.643e-15

No relationship for white participants, es expected.

model3 <- lm(inclusive_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + extraversion + openness + emotional_stability + agreeableness + conscientiousness,
                 data = black_sample)

summary(model3)

## 
## Call:
## lm(formula = inclusive_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + 
##     extraversion + openness + emotional_stability + agreeableness + 
##     conscientiousness, data = black_sample)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4789 -0.5392  0.0847  0.5927  2.0746 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          4.03825    0.73819   5.471 1.24e-07 ***
## zsm_avg             -0.12265    0.06040  -2.031 0.043512 *  
## jwb_general_avg      0.33136    0.08519   3.890 0.000134 ***
## jwb_personal_avg    -0.09636    0.08642  -1.115 0.266071    
## extraversion         0.01041    0.04813   0.216 0.829030    
## openness             0.14195    0.07749   1.832 0.068376 .  
## emotional_stability  0.11412    0.06547   1.743 0.082752 .  
## agreeableness       -0.38485    0.07609  -5.058 9.07e-07 ***
## conscientiousness    0.09995    0.08458   1.182 0.238658    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.914 on 215 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.3618, Adjusted R-squared:  0.338 
## F-statistic: 15.23 on 8 and 215 DF,  p-value: < 2.2e-16

Negative relationship for black participants. Opposite of LEAD data.

4b. What does this relationship look like across men / women participants?

man_sample <- df_full %>% 
  filter(gender == "man")

woman_sample <- df_full %>% 
  filter(gender == "woman")

model4 <- lm(inclusive_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + extraversion + openness + emotional_stability + agreeableness + conscientiousness,
                 data = man_sample)

summary(model4)

## 
## Call:
## lm(formula = inclusive_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + 
##     extraversion + openness + emotional_stability + agreeableness + 
##     conscientiousness, data = man_sample)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3159 -0.5420  0.0740  0.6526  2.3497 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          2.65577    0.77132   3.443 0.000685 ***
## zsm_avg             -0.03744    0.06320  -0.592 0.554224    
## jwb_general_avg      0.24547    0.08545   2.873 0.004456 ** 
## jwb_personal_avg    -0.03021    0.09200  -0.328 0.742918    
## extraversion         0.11099    0.05248   2.115 0.035540 *  
## openness             0.21885    0.06631   3.300 0.001121 ** 
## emotional_stability  0.14772    0.07230   2.043 0.042193 *  
## agreeableness       -0.40836    0.07888  -5.177 4.97e-07 ***
## conscientiousness    0.14639    0.08194   1.787 0.075329 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.028 on 227 degrees of freedom
##   (1 observation deleted due to missingness)
## Multiple R-squared:  0.3641, Adjusted R-squared:  0.3416 
## F-statistic: 16.24 on 8 and 227 DF,  p-value: < 2.2e-16

No relationship for men participants, as expected.

model5 <- lm(inclusive_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + extraversion + openness + emotional_stability + agreeableness + conscientiousness,
                 data = woman_sample)

summary(model5)

## 
## Call:
## lm(formula = inclusive_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + 
##     extraversion + openness + emotional_stability + agreeableness + 
##     conscientiousness, data = woman_sample)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9306 -0.5472  0.1207  0.6101  1.8092 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          5.34687    0.66409   8.051 4.65e-14 ***
## zsm_avg             -0.17060    0.05511  -3.096  0.00221 ** 
## jwb_general_avg      0.04597    0.08313   0.553  0.58082    
## jwb_personal_avg     0.05378    0.08278   0.650  0.51656    
## extraversion        -0.02697    0.04453  -0.606  0.54536    
## openness             0.15666    0.05680   2.758  0.00629 ** 
## emotional_stability -0.01417    0.05044  -0.281  0.77902    
## agreeableness       -0.27160    0.06660  -4.078 6.30e-05 ***
## conscientiousness    0.04587    0.07105   0.646  0.51922    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.8675 on 226 degrees of freedom
## Multiple R-squared:  0.3163, Adjusted R-squared:  0.2921 
## F-statistic: 13.07 on 8 and 226 DF,  p-value: 1.852e-15

Negative relationship for women participants. Opposite of LEAD data.

5. How about for altruism?

Race

model6 <- lm(sra_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + extraversion + openness + emotional_stability + agreeableness + conscientiousness,
                 data = white_sample)

summary(model6)

## 
## Call:
## lm(formula = sra_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + 
##     extraversion + openness + emotional_stability + agreeableness + 
##     conscientiousness, data = white_sample)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2525 -0.5568  0.0117  0.7253  2.6164 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          2.22226    0.73072   3.041  0.00262 ** 
## zsm_avg              0.06893    0.06137   1.123  0.26243    
## jwb_general_avg      0.52329    0.08388   6.238    2e-09 ***
## jwb_personal_avg    -0.12333    0.09183  -1.343  0.18053    
## extraversion         0.15835    0.05115   3.096  0.00220 ** 
## openness             0.14449    0.05657   2.554  0.01127 *  
## emotional_stability -0.09052    0.05691  -1.591  0.11304    
## agreeableness       -0.01925    0.06959  -0.277  0.78231    
## conscientiousness   -0.12036    0.07332  -1.642  0.10198    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.016 on 238 degrees of freedom
## Multiple R-squared:  0.3698, Adjusted R-squared:  0.3486 
## F-statistic: 17.46 on 8 and 238 DF,  p-value: < 2.2e-16

model7 <- lm(sra_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + extraversion + openness + emotional_stability + agreeableness + conscientiousness,
                 data = black_sample)

summary(model7)

## 
## Call:
## lm(formula = sra_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + 
##     extraversion + openness + emotional_stability + agreeableness + 
##     conscientiousness, data = black_sample)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -2.65565 -0.72991 -0.00969  0.79011  2.54095 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          3.149193   0.846495   3.720 0.000254 ***
## zsm_avg              0.074829   0.069553   1.076 0.283195    
## jwb_general_avg      0.327098   0.098157   3.332 0.001013 ** 
## jwb_personal_avg     0.093970   0.099591   0.944 0.346447    
## extraversion         0.060279   0.055468   1.087 0.278364    
## openness            -0.007669   0.089246  -0.086 0.931600    
## emotional_stability -0.101415   0.075213  -1.348 0.178951    
## agreeableness       -0.106979   0.086587  -1.236 0.217986    
## conscientiousness   -0.033740   0.097324  -0.347 0.729172    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.053 on 216 degrees of freedom
## Multiple R-squared:  0.2135, Adjusted R-squared:  0.1844 
## F-statistic:  7.33 on 8 and 216 DF,  p-value: 1.3e-08

Gender

model8 <- lm(sra_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + extraversion + openness + emotional_stability + agreeableness + conscientiousness,
                 data = man_sample)

summary(model8)

## 
## Call:
## lm(formula = sra_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + 
##     extraversion + openness + emotional_stability + agreeableness + 
##     conscientiousness, data = man_sample)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.4016 -0.7010 -0.0011  0.7146  2.5238 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          1.754251   0.802266   2.187   0.0298 *  
## zsm_avg              0.110295   0.065903   1.674   0.0956 .  
## jwb_general_avg      0.444529   0.089156   4.986 1.22e-06 ***
## jwb_personal_avg    -0.002652   0.096002  -0.028   0.9780    
## extraversion         0.122007   0.054771   2.228   0.0269 *  
## openness             0.133335   0.069126   1.929   0.0550 .  
## emotional_stability -0.031276   0.075260  -0.416   0.6781    
## agreeableness       -0.062893   0.081428  -0.772   0.4407    
## conscientiousness   -0.077899   0.085494  -0.911   0.3632    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.072 on 228 degrees of freedom
## Multiple R-squared:  0.2965, Adjusted R-squared:  0.2718 
## F-statistic: 12.01 on 8 and 228 DF,  p-value: 2.787e-14

model9 <- lm(sra_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + extraversion + openness + emotional_stability + agreeableness + conscientiousness,
                 data = woman_sample)

summary(model9)

## 
## Call:
## lm(formula = sra_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + 
##     extraversion + openness + emotional_stability + agreeableness + 
##     conscientiousness, data = woman_sample)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4836 -0.6682  0.0513  0.7163  2.3609 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          3.34782    0.76085   4.400 1.67e-05 ***
## zsm_avg              0.02947    0.06314   0.467 0.641107    
## jwb_general_avg      0.36392    0.09524   3.821 0.000172 ***
## jwb_personal_avg    -0.01157    0.09485  -0.122 0.903005    
## extraversion         0.07794    0.05102   1.527 0.128035    
## openness             0.09015    0.06507   1.385 0.167313    
## emotional_stability -0.21930    0.05779  -3.795 0.000190 ***
## agreeableness        0.01598    0.07631   0.209 0.834268    
## conscientiousness   -0.08105    0.08140  -0.996 0.320458    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9939 on 226 degrees of freedom
## Multiple R-squared:  0.3131, Adjusted R-squared:  0.2888 
## F-statistic: 12.88 on 8 and 226 DF,  p-value: 3.058e-15

No relationships between ZSM and altruism.

6. How about for cooperation, sdo, trust, and competitive orientation (DVs from Fearon)

Cooperation

Race

model10 <- lm(coop_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + extraversion + openness + emotional_stability + agreeableness + conscientiousness,
                 data = white_sample)

summary(model10)

## 
## Call:
## lm(formula = coop_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + 
##     extraversion + openness + emotional_stability + agreeableness + 
##     conscientiousness, data = white_sample)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3837 -0.7228  0.3210  0.8724  2.1703 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          7.02726    0.93485   7.517 1.13e-12 ***
## zsm_avg             -0.30317    0.07851  -3.862 0.000145 ***
## jwb_general_avg     -0.34861    0.10731  -3.249 0.001327 ** 
## jwb_personal_avg     0.17986    0.11748   1.531 0.127101    
## extraversion        -0.02445    0.06544  -0.374 0.708947    
## openness             0.07710    0.07238   1.065 0.287840    
## emotional_stability -0.02707    0.07281  -0.372 0.710371    
## agreeableness       -0.28457    0.08903  -3.196 0.001581 ** 
## conscientiousness    0.08509    0.09380   0.907 0.365257    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.299 on 238 degrees of freedom
## Multiple R-squared:  0.2071, Adjusted R-squared:  0.1804 
## F-statistic: 7.769 on 8 and 238 DF,  p-value: 2.949e-09

model11 <- lm(coop_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + extraversion + openness + emotional_stability + agreeableness + conscientiousness,
                 data = black_sample)

summary(model11)

## 
## Call:
## lm(formula = coop_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + 
##     extraversion + openness + emotional_stability + agreeableness + 
##     conscientiousness, data = black_sample)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4790 -0.6196  0.2022  0.7461  3.4717 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          7.24577    0.97245   7.451 2.20e-12 ***
## zsm_avg             -0.41031    0.07990  -5.135 6.28e-07 ***
## jwb_general_avg      0.19213    0.11276   1.704  0.08985 .  
## jwb_personal_avg    -0.13950    0.11441  -1.219  0.22406    
## extraversion        -0.12815    0.06372  -2.011  0.04555 *  
## openness             0.11632    0.10253   1.135  0.25783    
## emotional_stability  0.03283    0.08640   0.380  0.70431    
## agreeableness       -0.32124    0.09947  -3.230  0.00143 ** 
## conscientiousness   -0.03960    0.11180  -0.354  0.72355    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.21 on 216 degrees of freedom
## Multiple R-squared:  0.2553, Adjusted R-squared:  0.2277 
## F-statistic: 9.256 on 8 and 216 DF,  p-value: 5.979e-11

Gender

model12 <- lm(coop_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + extraversion + openness + emotional_stability + agreeableness + conscientiousness,
                 data = man_sample)

summary(model12)

## 
## Call:
## lm(formula = coop_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + 
##     extraversion + openness + emotional_stability + agreeableness + 
##     conscientiousness, data = man_sample)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.7083 -0.6751  0.2455  0.8585  3.1298 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          6.04062    0.94108   6.419 7.87e-10 ***
## zsm_avg             -0.27607    0.07731  -3.571 0.000433 ***
## jwb_general_avg     -0.11542    0.10458  -1.104 0.270901    
## jwb_personal_avg    -0.01514    0.11261  -0.134 0.893145    
## extraversion         0.01242    0.06425   0.193 0.846894    
## openness             0.11743    0.08109   1.448 0.148926    
## emotional_stability -0.03542    0.08828  -0.401 0.688622    
## agreeableness       -0.13232    0.09552  -1.385 0.167327    
## conscientiousness    0.10510    0.10029   1.048 0.295760    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.258 on 228 degrees of freedom
## Multiple R-squared:  0.1529, Adjusted R-squared:  0.1232 
## F-statistic: 5.145 on 8 and 228 DF,  p-value: 6.538e-06

model13 <- lm(coop_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + extraversion + openness + emotional_stability + agreeableness + conscientiousness,
                 data = woman_sample)

summary(model13)

## 
## Call:
## lm(formula = coop_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + 
##     extraversion + openness + emotional_stability + agreeableness + 
##     conscientiousness, data = woman_sample)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2430 -0.6675  0.2380  0.7343  3.3908 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          8.558068   0.967445   8.846 2.65e-16 ***
## zsm_avg             -0.393819   0.080287  -4.905 1.78e-06 ***
## jwb_general_avg     -0.066635   0.121102  -0.550   0.5827    
## jwb_personal_avg     0.064012   0.120600   0.531   0.5961    
## extraversion        -0.121076   0.064875  -1.866   0.0633 .  
## openness             0.056609   0.082742   0.684   0.4946    
## emotional_stability  0.002513   0.073483   0.034   0.9728    
## agreeableness       -0.469153   0.097025  -4.835 2.46e-06 ***
## conscientiousness   -0.113444   0.103503  -1.096   0.2742    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.264 on 226 degrees of freedom
## Multiple R-squared:  0.2828, Adjusted R-squared:  0.2575 
## F-statistic: 11.14 on 8 and 226 DF,  p-value: 2.964e-13

Higher ZSM predicts less cooperation across groups.

SDO

Race

model14 <- lm(sdo_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + extraversion + openness + emotional_stability + agreeableness + conscientiousness,
                 data = white_sample)

summary(model14)

## 
## Call:
## lm(formula = sdo_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + 
##     extraversion + openness + emotional_stability + agreeableness + 
##     conscientiousness, data = white_sample)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.5870 -0.7901 -0.1703  0.6550  4.4705 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          1.30272    0.80562   1.617 0.107195    
## zsm_avg              0.28359    0.06766   4.192 3.91e-05 ***
## jwb_general_avg      0.34355    0.09248   3.715 0.000253 ***
## jwb_personal_avg    -0.08303    0.10124  -0.820 0.412963    
## extraversion         0.02023    0.05639   0.359 0.720043    
## openness            -0.10829    0.06237  -1.736 0.083817 .  
## emotional_stability -0.07432    0.06274  -1.184 0.237410    
## agreeableness        0.35137    0.07672   4.580 7.51e-06 ***
## conscientiousness   -0.12852    0.08083  -1.590 0.113174    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.12 on 238 degrees of freedom
## Multiple R-squared:  0.2814, Adjusted R-squared:  0.2572 
## F-statistic: 11.65 on 8 and 238 DF,  p-value: 5.892e-14

model15 <- lm(sdo_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + extraversion + openness + emotional_stability + agreeableness + conscientiousness,
                 data = black_sample)

summary(model15)

## 
## Call:
## lm(formula = sdo_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + 
##     extraversion + openness + emotional_stability + agreeableness + 
##     conscientiousness, data = black_sample)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.1353 -0.6190 -0.1074  0.6385  3.6985 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          1.98999    0.72846   2.732  0.00682 ** 
## zsm_avg              0.34899    0.05985   5.831 1.99e-08 ***
## jwb_general_avg      0.07951    0.08447   0.941  0.34762    
## jwb_personal_avg     0.11548    0.08570   1.347  0.17927    
## extraversion        -0.01847    0.04773  -0.387  0.69916    
## openness            -0.08322    0.07680  -1.084  0.27976    
## emotional_stability -0.01035    0.06472  -0.160  0.87313    
## agreeableness        0.17231    0.07451   2.312  0.02170 *  
## conscientiousness   -0.22414    0.08375  -2.676  0.00802 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.9065 on 216 degrees of freedom
## Multiple R-squared:  0.3887, Adjusted R-squared:  0.366 
## F-statistic: 17.17 on 8 and 216 DF,  p-value: < 2.2e-16

Gender

model16 <- lm(sdo_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + extraversion + openness + emotional_stability + agreeableness + conscientiousness,
                 data = man_sample)

summary(model16)

## 
## Call:
## lm(formula = sdo_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + 
##     extraversion + openness + emotional_stability + agreeableness + 
##     conscientiousness, data = man_sample)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.1229 -0.6693 -0.1263  0.6476  3.5282 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          1.95808    0.76242   2.568 0.010860 *  
## zsm_avg              0.32472    0.06263   5.185 4.77e-07 ***
## jwb_general_avg      0.12003    0.08473   1.417 0.157938    
## jwb_personal_avg     0.01753    0.09123   0.192 0.847769    
## extraversion         0.04888    0.05205   0.939 0.348651    
## openness            -0.09486    0.06569  -1.444 0.150097    
## emotional_stability -0.03559    0.07152  -0.498 0.619216    
## agreeableness        0.27903    0.07738   3.606 0.000382 ***
## conscientiousness   -0.20997    0.08125  -2.584 0.010381 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.019 on 228 degrees of freedom
## Multiple R-squared:  0.2998, Adjusted R-squared:  0.2753 
## F-statistic:  12.2 on 8 and 228 DF,  p-value: 1.684e-14

model17 <- lm(sdo_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + extraversion + openness + emotional_stability + agreeableness + conscientiousness,
                 data = woman_sample)

summary(model17)

## 
## Call:
## lm(formula = sdo_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + 
##     extraversion + openness + emotional_stability + agreeableness + 
##     conscientiousness, data = woman_sample)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.4207 -0.7058 -0.1478  0.5714  4.3296 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          1.166096   0.797143   1.463  0.14490    
## zsm_avg              0.320506   0.066154   4.845 2.35e-06 ***
## jwb_general_avg      0.309764   0.099784   3.104  0.00215 ** 
## jwb_personal_avg     0.006833   0.099371   0.069  0.94524    
## extraversion        -0.042101   0.053455  -0.788  0.43176    
## openness            -0.126283   0.068177  -1.852  0.06529 .  
## emotional_stability -0.056225   0.060548  -0.929  0.35409    
## agreeableness        0.280239   0.079945   3.505  0.00055 ***
## conscientiousness   -0.110405   0.085283  -1.295  0.19679    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.041 on 226 degrees of freedom
## Multiple R-squared:  0.3353, Adjusted R-squared:  0.3117 
## F-statistic: 14.25 on 8 and 226 DF,  p-value: < 2.2e-16

Competitiveness

Race

model18 <- lm(comp_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + extraversion + openness + emotional_stability + agreeableness + conscientiousness,
                 data = white_sample)

summary(model18)

## 
## Call:
## lm(formula = comp_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + 
##     extraversion + openness + emotional_stability + agreeableness + 
##     conscientiousness, data = white_sample)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.3006 -0.8578 -0.1777  0.8109  4.5843 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         -1.01844    0.97139  -1.048 0.295501    
## zsm_avg              0.53308    0.08158   6.535 3.84e-10 ***
## jwb_general_avg      0.51570    0.11151   4.625 6.16e-06 ***
## jwb_personal_avg    -0.12792    0.12207  -1.048 0.295747    
## extraversion         0.06928    0.06799   1.019 0.309312    
## openness             0.10012    0.07520   1.331 0.184340    
## emotional_stability -0.05994    0.07565  -0.792 0.429010    
## agreeableness        0.34467    0.09251   3.726 0.000243 ***
## conscientiousness   -0.08025    0.09747  -0.823 0.411135    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.35 on 238 degrees of freedom
## Multiple R-squared:  0.3497, Adjusted R-squared:  0.3279 
## F-statistic:    16 on 8 and 238 DF,  p-value: < 2.2e-16

model19 <- lm(comp_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + extraversion + openness + emotional_stability + agreeableness + conscientiousness,
                 data = black_sample)

summary(model19)

## 
## Call:
## lm(formula = comp_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + 
##     extraversion + openness + emotional_stability + agreeableness + 
##     conscientiousness, data = black_sample)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.779 -1.153 -0.025  1.078  3.271 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          0.099862   1.167246   0.086  0.93190    
## zsm_avg              0.423961   0.095908   4.421 1.56e-05 ***
## jwb_general_avg      0.090993   0.135350   0.672  0.50212    
## jwb_personal_avg     0.252365   0.137327   1.838  0.06748 .  
## extraversion         0.002724   0.076485   0.036  0.97162    
## openness             0.080855   0.123063   0.657  0.51187    
## emotional_stability  0.311473   0.103712   3.003  0.00299 ** 
## agreeableness       -0.015382   0.119397  -0.129  0.89761    
## conscientiousness   -0.112360   0.134201  -0.837  0.40338    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.452 on 216 degrees of freedom
## Multiple R-squared:  0.2418, Adjusted R-squared:  0.2137 
## F-statistic:  8.61 on 8 and 216 DF,  p-value: 3.564e-10

Gender

model20 <- lm(comp_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + extraversion + openness + emotional_stability + agreeableness + conscientiousness,
                 data = man_sample)

summary(model20)

## 
## Call:
## lm(formula = comp_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + 
##     extraversion + openness + emotional_stability + agreeableness + 
##     conscientiousness, data = man_sample)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1269 -0.9858 -0.1846  1.0907  3.8412 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         -0.95420    1.02139  -0.934 0.351180    
## zsm_avg              0.38845    0.08390   4.630 6.15e-06 ***
## jwb_general_avg      0.43327    0.11351   3.817 0.000174 ***
## jwb_personal_avg    -0.10426    0.12222  -0.853 0.394543    
## extraversion         0.11602    0.06973   1.664 0.097515 .  
## openness             0.04753    0.08801   0.540 0.589655    
## emotional_stability  0.14315    0.09582   1.494 0.136540    
## agreeableness        0.10157    0.10367   0.980 0.328267    
## conscientiousness    0.07425    0.10884   0.682 0.495850    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.365 on 228 degrees of freedom
## Multiple R-squared:  0.2413, Adjusted R-squared:  0.2147 
## F-statistic: 9.063 on 8 and 228 DF,  p-value: 8.474e-11

model21 <- lm(comp_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + extraversion + openness + emotional_stability + agreeableness + conscientiousness,
                 data = woman_sample)

summary(model21)

## 
## Call:
## lm(formula = comp_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + 
##     extraversion + openness + emotional_stability + agreeableness + 
##     conscientiousness, data = woman_sample)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9903 -1.0489 -0.1964  1.0487  4.2541 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)         -0.80719    1.11856  -0.722   0.4713    
## zsm_avg              0.61254    0.09283   6.599 2.91e-10 ***
## jwb_general_avg      0.36300    0.14002   2.592   0.0101 *  
## jwb_personal_avg     0.06264    0.13944   0.449   0.6537    
## extraversion        -0.02148    0.07501  -0.286   0.7749    
## openness             0.16824    0.09567   1.759   0.0800 .  
## emotional_stability  0.02500    0.08496   0.294   0.7688    
## agreeableness        0.27228    0.11218   2.427   0.0160 *  
## conscientiousness   -0.20310    0.11967  -1.697   0.0910 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.461 on 226 degrees of freedom
## Multiple R-squared:  0.3351, Adjusted R-squared:  0.3116 
## F-statistic: 14.24 on 8 and 226 DF,  p-value: < 2.2e-16

Trust

Race

model22 <- lm(trust_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + extraversion + openness + emotional_stability + agreeableness + conscientiousness,
                 data = white_sample)

summary(model22)

## 
## Call:
## lm(formula = trust_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + 
##     extraversion + openness + emotional_stability + agreeableness + 
##     conscientiousness, data = white_sample)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9906 -0.6285  0.1538  0.6125  3.0936 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          2.602272   0.763493   3.408 0.000767 ***
## zsm_avg             -0.048371   0.064118  -0.754 0.451349    
## jwb_general_avg      0.288609   0.087643   3.293 0.001142 ** 
## jwb_personal_avg     0.321184   0.095949   3.347 0.000948 ***
## extraversion         0.014419   0.053442   0.270 0.787543    
## openness             0.070361   0.059109   1.190 0.235088    
## emotional_stability -0.009966   0.059462  -0.168 0.867036    
## agreeableness       -0.225382   0.072713  -3.100 0.002171 ** 
## conscientiousness   -0.054746   0.076606  -0.715 0.475532    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.061 on 238 degrees of freedom
## Multiple R-squared:  0.3708, Adjusted R-squared:  0.3496 
## F-statistic: 17.53 on 8 and 238 DF,  p-value: < 2.2e-16

model23 <- lm(trust_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + extraversion + openness + emotional_stability + agreeableness + conscientiousness,
                 data = black_sample)

summary(model23)

## 
## Call:
## lm(formula = trust_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + 
##     extraversion + openness + emotional_stability + agreeableness + 
##     conscientiousness, data = black_sample)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1875 -0.6289  0.1416  0.7041  2.5707 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          3.96196    0.90775   4.365 1.97e-05 ***
## zsm_avg             -0.16801    0.07459  -2.253   0.0253 *  
## jwb_general_avg      0.56696    0.10526   5.386 1.87e-07 ***
## jwb_personal_avg     0.18290    0.10680   1.713   0.0882 .  
## extraversion        -0.03005    0.05948  -0.505   0.6139    
## openness            -0.11037    0.09570  -1.153   0.2501    
## emotional_stability -0.03369    0.08066  -0.418   0.6766    
## agreeableness       -0.17086    0.09285  -1.840   0.0671 .  
## conscientiousness   -0.18133    0.10437  -1.737   0.0837 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.13 on 216 degrees of freedom
## Multiple R-squared:  0.3526, Adjusted R-squared:  0.3287 
## F-statistic: 14.71 on 8 and 216 DF,  p-value: < 2.2e-16

Gender

model24 <- lm(trust_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + extraversion + openness + emotional_stability + agreeableness + conscientiousness,
                 data = man_sample)

summary(model24)

## 
## Call:
## lm(formula = trust_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + 
##     extraversion + openness + emotional_stability + agreeableness + 
##     conscientiousness, data = man_sample)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2576 -0.6039  0.1101  0.6702  3.0019 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          2.96104    0.85133   3.478 0.000605 ***
## zsm_avg             -0.15380    0.06993  -2.199 0.028868 *  
## jwb_general_avg      0.42740    0.09461   4.518    1e-05 ***
## jwb_personal_avg     0.23467    0.10187   2.304 0.022149 *  
## extraversion         0.03011    0.05812   0.518 0.604905    
## openness             0.01747    0.07335   0.238 0.811935    
## emotional_stability  0.04324    0.07986   0.541 0.588726    
## agreeableness       -0.17351    0.08641  -2.008 0.045824 *  
## conscientiousness   -0.13512    0.09072  -1.489 0.137774    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.138 on 228 degrees of freedom
## Multiple R-squared:  0.3094, Adjusted R-squared:  0.2852 
## F-statistic: 12.77 on 8 and 228 DF,  p-value: 3.82e-15

model25 <- lm(trust_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + extraversion + openness + emotional_stability + agreeableness + conscientiousness,
                 data = woman_sample)

summary(model25)

## 
## Call:
## lm(formula = trust_avg ~ zsm_avg + jwb_general_avg + jwb_personal_avg + 
##     extraversion + openness + emotional_stability + agreeableness + 
##     conscientiousness, data = woman_sample)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.7885 -0.5756  0.1896  0.6550  3.3619 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          3.55470    0.81578   4.357    2e-05 ***
## zsm_avg             -0.02095    0.06770  -0.309 0.757273    
## jwb_general_avg      0.31578    0.10212   3.092 0.002236 ** 
## jwb_personal_avg     0.35460    0.10169   3.487 0.000587 ***
## extraversion        -0.05203    0.05471  -0.951 0.342530    
## openness            -0.04372    0.06977  -0.627 0.531528    
## emotional_stability -0.10067    0.06196  -1.625 0.105617    
## agreeableness       -0.22474    0.08181  -2.747 0.006500 ** 
## conscientiousness   -0.09200    0.08728  -1.054 0.292967    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.066 on 226 degrees of freedom
## Multiple R-squared:  0.3921, Adjusted R-squared:  0.3706 
## F-statistic: 18.22 on 8 and 226 DF,  p-value: < 2.2e-16

ZSB Study 1

Samantha Grayson

2025-06-09

Load packages

Load data

Exclusions

Data cleaning

Gather and clean numeric data

Create average scores

Gather and clean demographic information

Combine

Analysis

1. Do women and black participants have a higher zero sum mindset than male and white participants in our sample?

Zero sum mindset measure

Chinoy ZSB measure

Davidai ZSB measure

2. Are we seeing a ceiling effect for inclusive behavior?

3. Do people’s zero-sum mindsets predict the extent to which they perceive themselves as inclusive?

4a. What does this relationship look like across white / black participants?

4b. What does this relationship look like across men / women participants?

5. How about for altruism?

Race

Gender

6. How about for cooperation, sdo, trust, and competitive orientation (DVs from Fearon)

Cooperation

Race

Gender

SDO

Race

Gender

Competitiveness

Race

Gender

Trust

Race

Gender