Load packages

library(tidyverse)

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## ✔ 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/Interpretation of the Zero Sum Game/Study 1 Pilot/pilot_data_7.26.25.csv")

Exclusions

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

Data cleaning

Gather and clean numeric data

df_numeric <- data %>%
  select(c(ResponseId, zsm_1:sdo_8R, trust_1:trust_5, negotiation_1:negotiation_4, social_inclusion_1:SJB_8, Extra_1:Open_10R, SWL)) %>% 
  mutate(across(-ResponseId, as.numeric))

df_numeric <- df_numeric %>%
  mutate(across(ends_with("R"), ~ 8 - ., .names = "{.col}_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(
    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(
    social_incl_avg = rowMeans(select(., 
      social_inclusion_1,
      social_inclusion_2,
      social_inclusion_3,
      social_inclusion_4
    ), na.rm = TRUE)
  )

df_numeric <- df_numeric %>%
  mutate(
    pro_incl_avg = rowMeans(select(., 
      professional_incl_1,
      professional_incl_2,
      professional_incl_3,
      professional_incl_4,
      professional_incl_5
    ), na.rm = TRUE)
  )

df_numeric <- df_numeric %>%
  mutate(
    SJB_avg = rowMeans(select(., 
      SJB_1,
      SJB_2,
      SJB_3R_Recoded,
      SJB_4,
      SJB_5,
      SJB_6,
      SJB_7R_Recoded,
      SJB_8
    ), na.rm = TRUE)
  )

df_numeric <- df_numeric %>%
  mutate(
    extraversion = rowMeans(select(., 
      Extra_1,
      Extra_6R_Recoded
    ), na.rm = TRUE)
  )

df_numeric <- df_numeric %>%
  mutate(
    agreeable = rowMeans(select(., 
      Agree_2,
      Agree_7R_Recoded
    ), na.rm = TRUE)
  )

df_numeric <- df_numeric %>%
  mutate(
    open = rowMeans(select(., 
      Open_5,
      Open_10R_Recoded
    ), na.rm = TRUE)
  )

df_numeric <- df_numeric %>%
  mutate(
    conscientious = rowMeans(select(., 
      Con_3,
      Con_8R_Recoded
    ), na.rm = TRUE)
  )

df_numeric <- df_numeric %>%
  mutate(
    neuroticism = rowMeans(select(., 
      Neuro_4,
      Neuro_9R_Recoded
    ), na.rm = TRUE)
  )

Combine with relevant order and condition info

order <- data %>% 
  select(ResponseId, FL_39_DO, FL_38_DO) %>% 
  rename(order = FL_39_DO) %>% 
  rename(condition = FL_38_DO) %>% 
  mutate(order = ifelse(order == "Attribution|FL_38", "Attribution First", "ZS First")) %>% 
  mutate(condition = ifelse(condition == "intergroupzerosummindset", "Intergroup ZSM", "Domain ZSB Chinoy"))

df <- df_numeric %>% 
  left_join(order, by = "ResponseId")

Break down by condition

zsm <- df %>% 
  filter(condition == "Intergroup ZSM")

chinoy <- df %>% 
  filter(condition == "Domain ZSB Chinoy")

Variation in Attribution?

ggplot(df, aes(x = factor(ZS_attribution._1))) +
  geom_bar(fill = "steelblue", color = "white") +
  labs(
    title = "Distribution of ZS Attribution Responses",
    x = "ZS Attribution",
    y = "Count"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(hjust = 0.5)
  )

Variation in Inclusion?

ggplot(df, aes(x = factor(social_incl_avg))) +
  geom_bar(fill = "steelblue", color = "white") +
  labs(
    title = "Distribution of Social Inclusion Responses",
    x = "Social Inclusion",
    y = "Count"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(hjust = 0.5)
  )

ggplot(df, aes(x = factor(pro_incl_avg))) +
  geom_bar(fill = "steelblue", color = "white") +
  labs(
    title = "Distribution of Professional Inclusion Responses",
    x = "Professional Inclusion",
    y = "Count"
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(hjust = 0.5)
  )

What do these attributions mean for…

Inclusion

# DV: social inclusion
model_social <- lm(social_incl_avg ~ ZS_attribution._1, data = df)

# DV: professional inclusion
model_prof <- lm(pro_incl_avg ~ ZS_attribution._1, data = df)

summary(model_social)

## 
## Call:
## lm(formula = social_incl_avg ~ ZS_attribution._1, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5267 -0.7156  0.2844  0.9733  2.0399 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        5.52675    0.15038  36.753   <2e-16 ***
## ZS_attribution._1  0.18888    0.08425   2.242   0.0273 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.468 on 95 degrees of freedom
## Multiple R-squared:  0.05025,    Adjusted R-squared:  0.04025 
## F-statistic: 5.026 on 1 and 95 DF,  p-value: 0.0273

summary(model_prof)

## 
## Call:
## lm(formula = pro_incl_avg ~ ZS_attribution._1, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4590 -0.6590  0.1258  1.0334  1.8182 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        5.45903    0.12432  43.910   <2e-16 ***
## ZS_attribution._1  0.09242    0.06966   1.327    0.188    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.214 on 95 degrees of freedom
## Multiple R-squared:  0.01819,    Adjusted R-squared:  0.007859 
## F-statistic:  1.76 on 1 and 95 DF,  p-value: 0.1878

Seems to suggest that people with more interpersonal attributions for zero-sum situations (higher ZS_attribution_1 scores) tend to report greater social inclusion.

Cooperation under scarcity

model_coop <- lm(coop_avg ~ ZS_attribution._1, data = df)

summary(model_coop)

## 
## Call:
## lm(formula = coop_avg ~ ZS_attribution._1, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7409 -0.8250  0.3631  0.8830  1.8830 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        5.42895    0.14111  38.472   <2e-16 ***
## ZS_attribution._1 -0.10399    0.07906  -1.315    0.192    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.377 on 95 degrees of freedom
## Multiple R-squared:  0.01789,    Adjusted R-squared:  0.007548 
## F-statistic:  1.73 on 1 and 95 DF,  p-value: 0.1916

Competitiveness

model_comp <- lm(comp_avg ~ ZS_attribution._1, data = df)

summary(model_comp)

## 
## Call:
## lm(formula = comp_avg ~ ZS_attribution._1, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5916 -1.3068 -0.3543  0.9316  4.4558 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        2.44923    0.15039  16.285   <2e-16 ***
## ZS_attribution._1 -0.04747    0.08426  -0.563    0.575    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.468 on 95 degrees of freedom
## Multiple R-squared:  0.003329,   Adjusted R-squared:  -0.007162 
## F-statistic: 0.3174 on 1 and 95 DF,  p-value: 0.5745

SDO

model_sdo <- lm(sdo_avg ~ ZS_attribution._1, data = df)

summary(model_sdo)

## 
## Call:
## lm(formula = sdo_avg ~ ZS_attribution._1, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5597 -1.0597 -0.4347  0.7736  4.9403 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)        2.30971    0.13993  16.506   <2e-16 ***
## ZS_attribution._1  0.08333    0.07840   1.063    0.291    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.366 on 95 degrees of freedom
## Multiple R-squared:  0.01175,    Adjusted R-squared:  0.001348 
## F-statistic:  1.13 on 1 and 95 DF,  p-value: 0.2906

Trust

model_trust <- lm(trust_avg ~ ZS_attribution._1, data = df)

summary(model_trust)

## 
## Call:
## lm(formula = trust_avg ~ ZS_attribution._1, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5229 -0.9282  0.2929  1.0771  2.4876 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       4.522898   0.154969  29.186   <2e-16 ***
## ZS_attribution._1 0.005264   0.086826   0.061    0.952    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.513 on 95 degrees of freedom
## Multiple R-squared:  3.869e-05,  Adjusted R-squared:  -0.01049 
## F-statistic: 0.003676 on 1 and 95 DF,  p-value: 0.9518

library(modelsummary)

models <- list(
  "Social Inclusion"       = model_social,
  "Professional Inclusion" = model_prof,
  "Cooperation"            = model_coop,
  "Competitiveness"        = model_comp,
  "SDO"                    = model_sdo,
  "Trust"                  = model_trust
)

modelsummary(models,
             stars = TRUE,
             coef_map = c("ZS_attribution._1" = "ZS Attribution"),
             gof_omit = "IC|Log|Adj|F|RMSE",
             output = "markdown")

	Social Inclusion	Professional Inclusion	Cooperation	Competitiveness	SDO	Trust
+ p < 0.1, * p < 0.05, p < 0.01, * p < 0.001
ZS Attribution	0.189*	0.092	-0.104	-0.047	0.083	0.005
	(0.084)	(0.070)	(0.079)	(0.084)	(0.078)	(0.087)
Num.Obs.	97	97	97	97	97	97
R2	0.050	0.018	0.018	0.003	0.012	0.000

Interactions with ZSM

Social inclusion

model_zsm_soc_incl <- lm(social_incl_avg ~ ZS_attribution._1 * zsm_avg, data = zsm)

summary(model_zsm_soc_incl)

## 
## Call:
## lm(formula = social_incl_avg ~ ZS_attribution._1 * zsm_avg, data = zsm)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.2973 -0.4850  0.2783  0.8494  2.0397 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                6.15019    0.44684  13.764   <2e-16 ***
## ZS_attribution._1         -0.16208    0.25923  -0.625   0.5350    
## zsm_avg                   -0.19370    0.14082  -1.376   0.1759    
## ZS_attribution._1:zsm_avg  0.15375    0.08068   1.906   0.0632 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.258 on 44 degrees of freedom
## Multiple R-squared:  0.2008, Adjusted R-squared:  0.1463 
## F-statistic: 3.685 on 3 and 44 DF,  p-value: 0.01882

library(sjPlot)

lm(social_incl_avg ~ ZS_attribution._1 * zsm_avg, data = zsm) %>% 
  plot_model(type="pred", terms=c("ZS_attribution._1", "zsm_avg"))

Professional inclusion

model_zsm_pro_incl <- lm(pro_incl_avg ~ ZS_attribution._1 * zsm_avg, data = zsm)

summary(model_zsm_pro_incl)

## 
## Call:
## lm(formula = pro_incl_avg ~ ZS_attribution._1 * zsm_avg, data = zsm)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.1980 -0.6183  0.0910  0.8560  1.8862 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                5.95522    0.40580  14.675   <2e-16 ***
## ZS_attribution._1         -0.17025    0.23542  -0.723    0.473    
## zsm_avg                   -0.13697    0.12788  -1.071    0.290    
## ZS_attribution._1:zsm_avg  0.12868    0.07327   1.756    0.086 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.142 on 44 degrees of freedom
## Multiple R-squared:  0.1483, Adjusted R-squared:  0.09021 
## F-statistic: 2.553 on 3 and 44 DF,  p-value: 0.06756

Cooperation

model_zsm_coop <- lm(coop_avg ~ ZS_attribution._1 * zsm_avg, data = zsm)

summary(model_zsm_coop)

## 
## Call:
## lm(formula = coop_avg ~ ZS_attribution._1 * zsm_avg, data = zsm)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.9104 -0.5041  0.2342  0.7973  2.2386 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                6.64837    0.41446  16.041  < 2e-16 ***
## ZS_attribution._1          0.09978    0.24044   0.415  0.68017    
## zsm_avg                   -0.41694    0.13062  -3.192  0.00261 ** 
## ZS_attribution._1:zsm_avg -0.05235    0.07483  -0.700  0.48788    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.167 on 44 degrees of freedom
## Multiple R-squared:   0.21,  Adjusted R-squared:  0.1561 
## F-statistic: 3.898 on 3 and 44 DF,  p-value: 0.01486

Competition

model_zsm_comp <- lm(comp_avg ~ ZS_attribution._1 * zsm_avg, data = zsm)

summary(model_zsm_comp)

## 
## Call:
## lm(formula = comp_avg ~ ZS_attribution._1 * zsm_avg, data = zsm)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.5549 -1.2452 -0.2685  1.0542  4.3218 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                2.05300    0.57006   3.601   0.0008 ***
## ZS_attribution._1         -0.13306    0.33071  -0.402   0.6894    
## zsm_avg                    0.20429    0.17965   1.137   0.2616    
## ZS_attribution._1:zsm_avg -0.02892    0.10292  -0.281   0.7800    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.605 on 44 degrees of freedom
## Multiple R-squared:  0.06871,    Adjusted R-squared:  0.005214 
## F-statistic: 1.082 on 3 and 44 DF,  p-value: 0.3666

SDO

model_zsm_sdo <- lm(sdo_avg ~ ZS_attribution._1 * zsm_avg, data = zsm)

summary(model_zsm_sdo)

## 
## Call:
## lm(formula = sdo_avg ~ ZS_attribution._1 * zsm_avg, data = zsm)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3048 -0.7516 -0.2554  0.7154  2.2572 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                1.83539    0.36091   5.085 7.27e-06 ***
## ZS_attribution._1          0.20111    0.20938   0.961    0.342    
## zsm_avg                    0.06657    0.11374   0.585    0.561    
## ZS_attribution._1:zsm_avg -0.05916    0.06516  -0.908    0.369    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.016 on 44 degrees of freedom
## Multiple R-squared:  0.02916,    Adjusted R-squared:  -0.03703 
## F-statistic: 0.4405 on 3 and 44 DF,  p-value: 0.7252

Trust

model_zsm_trust <- lm(trust_avg ~ ZS_attribution._1 * zsm_avg, data = zsm)

summary(model_zsm_trust)

## 
## Call:
## lm(formula = trust_avg ~ ZS_attribution._1 * zsm_avg, data = zsm)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.000 -1.066  0.394  1.049  2.267 
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                4.35577    0.52330   8.324 1.39e-10 ***
## ZS_attribution._1          0.19888    0.30358   0.655    0.516    
## zsm_avg                    0.09776    0.16491   0.593    0.556    
## ZS_attribution._1:zsm_avg -0.07689    0.09448  -0.814    0.420    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.473 on 44 degrees of freedom
## Multiple R-squared:  0.02242,    Adjusted R-squared:  -0.04424 
## F-statistic: 0.3363 on 3 and 44 DF,  p-value: 0.7991

models <- list(
  "Social Inclusion"       = model_zsm_soc_incl,
  "Professional Inclusion" = model_zsm_pro_incl,
  "Cooperation"            = model_zsm_coop,
  "Competitiveness"        = model_zsm_comp,
  "SDO"                    = model_zsm_sdo,
  "Trust"                  = model_zsm_trust
)

modelsummary(models,
             stars = TRUE,
             coef_map = c("ZS_attribution._1" = "ZS Attribution",
                          "zsm_avg" = "ZSM",
                          "ZS_attribution._1:zsm_avg" = "ZS Attribution × ZSM"),
             gof_omit = "IC|Log|Adj|F|RMSE",
             output = "markdown")

	Social Inclusion	Professional Inclusion	Cooperation	Competitiveness	SDO	Trust
+ p < 0.1, * p < 0.05, p < 0.01, * p < 0.001
ZS Attribution	-0.162	-0.170	0.100	-0.133	0.201	0.199
	(0.259)	(0.235)	(0.240)	(0.331)	(0.209)	(0.304)
ZSM	-0.194	-0.137	-0.417**	0.204	0.067	0.098
	(0.141)	(0.128)	(0.131)	(0.180)	(0.114)	(0.165)
ZS Attribution × ZSM	0.154+	0.129+	-0.052	-0.029	-0.059	-0.077
	(0.081)	(0.073)	(0.075)	(0.103)	(0.065)	(0.094)
Num.Obs.	48	48	48	48	48	48
R2	0.201	0.148	0.210	0.069	0.029	0.022

Interactions with Chinoy (ethnicity)

Social inclusion

model_eth_soc_incl <- lm(social_incl_avg ~ ZS_attribution._1 * chinoy_ethnicity, data = chinoy)

summary(model_eth_soc_incl)

## 
## Call:
## lm(formula = social_incl_avg ~ ZS_attribution._1 * chinoy_ethnicity, 
##     data = chinoy)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6370 -0.7297  0.3918  1.0070  2.0389 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                         4.95800    0.54338   9.124 8.46e-12 ***
## ZS_attribution._1                   0.33428    0.30419   1.099    0.278    
## chinoy_ethnicity                    0.11316    0.13261   0.853    0.398    
## ZS_attribution._1:chinoy_ethnicity -0.04627    0.07012  -0.660    0.513    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.635 on 45 degrees of freedom
## Multiple R-squared:  0.05272,    Adjusted R-squared:  -0.01043 
## F-statistic: 0.8348 on 3 and 45 DF,  p-value: 0.4819

Professional inclusion

model_eth_pro_incl <- lm(pro_incl_avg ~ ZS_attribution._1 * chinoy_ethnicity, data = chinoy)

summary(model_eth_pro_incl)

## 
## Call:
## lm(formula = pro_incl_avg ~ ZS_attribution._1 * chinoy_ethnicity, 
##     data = chinoy)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1536 -0.6605  0.0364  1.2199  1.6414 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                         4.96541    0.41999  11.823 2.13e-15 ***
## ZS_attribution._1                  -0.04036    0.23512  -0.172    0.864    
## chinoy_ethnicity                    0.09412    0.10250   0.918    0.363    
## ZS_attribution._1:chinoy_ethnicity  0.01768    0.05420   0.326    0.746    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.264 on 45 degrees of freedom
## Multiple R-squared:  0.01928,    Adjusted R-squared:  -0.0461 
## F-statistic: 0.2948 on 3 and 45 DF,  p-value: 0.8289

Cooperation

model_eth_coop <- lm(coop_avg ~ ZS_attribution._1 * chinoy_ethnicity, data = chinoy)

summary(model_eth_coop)

## 
## Call:
## lm(formula = coop_avg ~ ZS_attribution._1 * chinoy_ethnicity, 
##     data = chinoy)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0374 -0.5571  0.1544  0.9485  2.0332 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                         5.17720    0.49162  10.531    1e-13 ***
## ZS_attribution._1                  -0.54496    0.27521  -1.980   0.0538 .  
## chinoy_ethnicity                    0.08549    0.11998   0.713   0.4798    
## ZS_attribution._1:chinoy_ethnicity  0.11300    0.06344   1.781   0.0816 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.479 on 45 degrees of freedom
## Multiple R-squared:  0.0856, Adjusted R-squared:  0.02464 
## F-statistic: 1.404 on 3 and 45 DF,  p-value: 0.2539

Comp

model_eth_comp <- lm(comp_avg ~ ZS_attribution._1 * chinoy_ethnicity, data = chinoy)

summary(model_eth_comp)

## 
## Call:
## lm(formula = comp_avg ~ ZS_attribution._1 * chinoy_ethnicity, 
##     data = chinoy)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8271 -0.8271 -0.3156  0.6719  4.1366 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                         1.42826    0.41832   3.414  0.00136 **
## ZS_attribution._1                  -0.02291    0.23418  -0.098  0.92251   
## chinoy_ethnicity                    0.23314    0.10209   2.284  0.02717 * 
## ZS_attribution._1:chinoy_ethnicity  0.02852    0.05398   0.528  0.59988   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.259 on 45 degrees of freedom
## Multiple R-squared:  0.1086, Adjusted R-squared:  0.04922 
## F-statistic: 1.828 on 3 and 45 DF,  p-value: 0.1556

SDO

model_eth_sdo <- lm(sdo_avg ~ ZS_attribution._1 * chinoy_ethnicity, data = chinoy)

summary(model_eth_sdo)

## 
## Call:
## lm(formula = sdo_avg ~ ZS_attribution._1 * chinoy_ethnicity, 
##     data = chinoy)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.0499 -1.3020 -0.3187  0.7976  3.9501 
## 
## Coefficients:
##                                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                         2.67674    0.53115   5.040 8.06e-06 ***
## ZS_attribution._1                   0.57563    0.29734   1.936   0.0592 .  
## chinoy_ethnicity                   -0.03312    0.12963  -0.255   0.7995    
## ZS_attribution._1:chinoy_ethnicity -0.11104    0.06854  -1.620   0.1122    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.598 on 45 degrees of freedom
## Multiple R-squared:  0.07943,    Adjusted R-squared:  0.01806 
## F-statistic: 1.294 on 3 and 45 DF,  p-value: 0.288

Trust

model_eth_trust <- lm(trust_avg ~ ZS_attribution._1 * chinoy_ethnicity, data = chinoy)

summary(model_eth_trust)

## 
## Call:
## lm(formula = trust_avg ~ ZS_attribution._1 * chinoy_ethnicity, 
##     data = chinoy)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5666 -0.8201  0.4225  1.2446  2.4334 
## 
## Coefficients:
##                                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                         4.617149   0.539166   8.563 5.29e-11 ***
## ZS_attribution._1                   0.038256   0.301830   0.127    0.900    
## chinoy_ethnicity                   -0.050587   0.131587  -0.384    0.702    
## ZS_attribution._1:chinoy_ethnicity -0.008534   0.069578  -0.123    0.903    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.622 on 45 degrees of freedom
## Multiple R-squared:  0.003576,   Adjusted R-squared:  -0.06285 
## F-statistic: 0.05384 on 3 and 45 DF,  p-value: 0.9833

models <- list(
  "Social Inclusion"       = model_eth_soc_incl,
  "Professional Inclusion" = model_eth_pro_incl,
  "Cooperation"            = model_eth_coop,
  "Competitiveness"        = model_eth_comp,
  "SDO"                    = model_eth_sdo,
  "Trust"                  = model_eth_trust
)

modelsummary(models,
             stars = TRUE,
             coef_map = c("ZS_attribution._1" = "ZS Attribution",
                          "chinoy_ethnicity" = "Chinoy Ethnicity ZSB",
                          "ZS_attribution._1:chinoy_ethnicity" = "ZS Attribution × Chinoy Ethnicity"),
             gof_omit = "IC|Log|Adj|F|RMSE",
             output = "markdown")

	Social Inclusion	Professional Inclusion	Cooperation	Competitiveness	SDO	Trust
+ p < 0.1, * p < 0.05, p < 0.01, * p < 0.001
ZS Attribution	0.334	-0.040	-0.545+	-0.023	0.576+	0.038
	(0.304)	(0.235)	(0.275)	(0.234)	(0.297)	(0.302)
Chinoy Ethnicity ZSB	0.113	0.094	0.085	0.233*	-0.033	-0.051
	(0.133)	(0.103)	(0.120)	(0.102)	(0.130)	(0.132)
ZS Attribution × Chinoy Ethnicity	-0.046	0.018	0.113+	0.029	-0.111	-0.009
	(0.070)	(0.054)	(0.063)	(0.054)	(0.069)	(0.070)
Num.Obs.	49	49	49	49	49	49
R2	0.053	0.019	0.086	0.109	0.079	0.004

Interactions with Chinoy (citizenship)

Social inclusion

model_cit_soc_incl <- lm(social_incl_avg ~ ZS_attribution._1 * chinoy_citizenship, data = chinoy)

summary(model_cit_soc_incl)

## 
## Call:
## lm(formula = social_incl_avg ~ ZS_attribution._1 * chinoy_citizenship, 
##     data = chinoy)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3915 -0.4141  0.1621  1.1094  2.2750 
## 
## Coefficients:
##                                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                           5.3925114  0.4998526  10.788 4.58e-14 ***
## ZS_attribution._1                     0.3785970  0.2578053   1.469    0.149    
## chinoy_citizenship                   -0.0009616  0.1291119  -0.007    0.994    
## ZS_attribution._1:chinoy_citizenship -0.0730801  0.0662528  -1.103    0.276    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.638 on 45 degrees of freedom
## Multiple R-squared:  0.04899,    Adjusted R-squared:  -0.01441 
## F-statistic: 0.7727 on 3 and 45 DF,  p-value: 0.5153

Professional inclusion

model_cit_pro_incl <- lm(pro_incl_avg ~ ZS_attribution._1 * chinoy_citizenship, data = chinoy)

summary(model_cit_pro_incl)

## 
## Call:
## lm(formula = pro_incl_avg ~ ZS_attribution._1 * chinoy_citizenship, 
##     data = chinoy)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3978 -0.6949 -0.0520  1.0385  1.5835 
## 
## Coefficients:
##                                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                           5.43403    0.38825  13.996   <2e-16 ***
## ZS_attribution._1                    -0.03330    0.20025  -0.166    0.869    
## chinoy_citizenship                   -0.03626    0.10029  -0.362    0.719    
## ZS_attribution._1:chinoy_citizenship  0.01295    0.05146   0.252    0.802    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.273 on 45 degrees of freedom
## Multiple R-squared:  0.0057, Adjusted R-squared:  -0.06059 
## F-statistic: 0.08599 on 3 and 45 DF,  p-value: 0.9674

Cooperation

model_cit_coop <- lm(coop_avg ~ ZS_attribution._1 * chinoy_citizenship, data = chinoy)

summary(model_cit_coop)

## 
## Call:
## lm(formula = coop_avg ~ ZS_attribution._1 * chinoy_citizenship, 
##     data = chinoy)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5059 -0.6739  0.2364  0.8877  2.3810 
## 
## Coefficients:
##                                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                           6.393347   0.438000  14.597   <2e-16 ***
## ZS_attribution._1                    -0.098517   0.225904  -0.436   0.6648    
## chinoy_citizenship                   -0.281056   0.113136  -2.484   0.0168 *  
## ZS_attribution._1:chinoy_citizenship -0.007852   0.058055  -0.135   0.8930    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.436 on 45 degrees of freedom
## Multiple R-squared:  0.1389, Adjusted R-squared:  0.08151 
## F-statistic:  2.42 on 3 and 45 DF,  p-value: 0.07844

Comp

model_cit_comp <- lm(comp_avg ~ ZS_attribution._1 * chinoy_citizenship, data = chinoy)

summary(model_cit_comp)

## 
## Call:
## lm(formula = comp_avg ~ ZS_attribution._1 * chinoy_citizenship, 
##     data = chinoy)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8755 -0.8878  0.0072  0.5947  3.7630 
## 
## Coefficients:
##                                      Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                           1.24505    0.36563   3.405  0.00140 **
## ZS_attribution._1                    -0.01579    0.18858  -0.084  0.93365   
## chinoy_citizenship                    0.30449    0.09444   3.224  0.00235 **
## ZS_attribution._1:chinoy_citizenship  0.02477    0.04846   0.511  0.61182   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.198 on 45 degrees of freedom
## Multiple R-squared:  0.1921, Adjusted R-squared:  0.1382 
## F-statistic: 3.567 on 3 and 45 DF,  p-value: 0.02127

SDO

model_cit_sdo <- lm(sdo_avg ~ ZS_attribution._1 * chinoy_citizenship, data = chinoy)

summary(model_cit_sdo)

## 
## Call:
## lm(formula = sdo_avg ~ ZS_attribution._1 * chinoy_citizenship, 
##     data = chinoy)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2879 -0.9249 -0.3086  0.7891  4.0047 
## 
## Coefficients:
##                                      Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                           1.51494    0.46566   3.253  0.00217 **
## ZS_attribution._1                     0.05743    0.24017   0.239  0.81209   
## chinoy_citizenship                    0.32341    0.12028   2.689  0.01002 * 
## ZS_attribution._1:chinoy_citizenship  0.02911    0.06172   0.472  0.63951   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.526 on 45 degrees of freedom
## Multiple R-squared:  0.1606, Adjusted R-squared:  0.1046 
## F-statistic: 2.869 on 3 and 45 DF,  p-value: 0.04681

Trust

model_cit_trust <- lm(trust_avg ~ ZS_attribution._1 * chinoy_citizenship, data = chinoy)

summary(model_cit_trust)

## 
## Call:
## lm(formula = trust_avg ~ ZS_attribution._1 * chinoy_citizenship, 
##     data = chinoy)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0946 -0.6883  0.3366  1.0225  2.6964 
## 
## Coefficients:
##                                      Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                           5.36468    0.46670  11.495 5.55e-15 ***
## ZS_attribution._1                    -0.09513    0.24071  -0.395   0.6946    
## chinoy_citizenship                   -0.27004    0.12055  -2.240   0.0301 *  
## ZS_attribution._1:chinoy_citizenship  0.02641    0.06186   0.427   0.6714    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.53 on 45 degrees of freedom
## Multiple R-squared:  0.1143, Adjusted R-squared:  0.05524 
## F-statistic: 1.936 on 3 and 45 DF,  p-value: 0.1374

models <- list(
  "Social Inclusion"       = model_cit_soc_incl,
  "Professional Inclusion" = model_cit_pro_incl,
  "Cooperation"            = model_cit_coop,
  "Competitiveness"        = model_cit_comp,
  "SDO"                    = model_cit_sdo,
  "Trust"                  = model_cit_trust
)

modelsummary(models,
             stars = TRUE,
             coef_map = c("ZS_attribution._1" = "ZS Attribution",
                          "chinoy_citizenship" = "Chinoy Citizenship ZSB",
                          "ZS_attribution._1:chinoy_citizenship" = "ZS Attribution × Chinoy Citizenship"),
             gof_omit = "IC|Log|Adj|F|RMSE",
             output = "markdown")

	Social Inclusion	Professional Inclusion	Cooperation	Competitiveness	SDO	Trust
+ p < 0.1, * p < 0.05, p < 0.01, * p < 0.001
ZS Attribution	0.379	-0.033	-0.099	-0.016	0.057	-0.095
	(0.258)	(0.200)	(0.226)	(0.189)	(0.240)	(0.241)
Chinoy Citizenship ZSB	-0.001	-0.036	-0.281*	0.304**	0.323*	-0.270*
	(0.129)	(0.100)	(0.113)	(0.094)	(0.120)	(0.121)
ZS Attribution × Chinoy Citizenship	-0.073	0.013	-0.008	0.025	0.029	0.026
	(0.066)	(0.051)	(0.058)	(0.048)	(0.062)	(0.062)
Num.Obs.	49	49	49	49	49	49
R2	0.049	0.006	0.139	0.192	0.161	0.114

Interactions with Chinoy (trade)

Social inclusion

model_tra_soc_incl <- lm(social_incl_avg ~ ZS_attribution._1 * chinoy_trade, data = chinoy)

summary(model_tra_soc_incl)

## 
## Call:
## lm(formula = social_incl_avg ~ ZS_attribution._1 * chinoy_trade, 
##     data = chinoy)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4474 -0.7381  0.5119  1.0922  1.8399 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    5.166620   0.609984   8.470 7.21e-11 ***
## ZS_attribution._1              0.007364   0.312362   0.024    0.981    
## chinoy_trade                   0.070192   0.154213   0.455    0.651    
## ZS_attribution._1:chinoy_trade 0.034507   0.075998   0.454    0.652    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.656 on 45 degrees of freedom
## Multiple R-squared:  0.02864,    Adjusted R-squared:  -0.03612 
## F-statistic: 0.4422 on 3 and 45 DF,  p-value: 0.724

Professional inclusion

model_tra_pro_incl <- lm(pro_incl_avg ~ ZS_attribution._1 * chinoy_trade, data = chinoy)

summary(model_tra_pro_incl)

## 
## Call:
## lm(formula = pro_incl_avg ~ ZS_attribution._1 * chinoy_trade, 
##     data = chinoy)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2837 -0.8795  0.0280  1.1054  1.5114 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                     5.00733    0.46247  10.827 4.07e-14 ***
## ZS_attribution._1              -0.23225    0.23682  -0.981    0.332    
## chinoy_trade                    0.09213    0.11692   0.788    0.435    
## ZS_attribution._1:chinoy_trade  0.06760    0.05762   1.173    0.247    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.255 on 45 degrees of freedom
## Multiple R-squared:  0.03238,    Adjusted R-squared:  -0.03213 
## F-statistic: 0.502 on 3 and 45 DF,  p-value: 0.6828

Cooperation

model_tra_coop <- lm(coop_avg ~ ZS_attribution._1 * chinoy_trade, data = chinoy)

summary(model_tra_coop)

## 
## Call:
## lm(formula = coop_avg ~ ZS_attribution._1 * chinoy_trade, data = chinoy)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1382 -0.6374  0.4326  0.9309  2.0073 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                     6.28239    0.53092  11.833 2.07e-15 ***
## ZS_attribution._1              -0.41334    0.27187  -1.520    0.135    
## chinoy_trade                   -0.21330    0.13422  -1.589    0.119    
## ZS_attribution._1:chinoy_trade  0.06696    0.06615   1.012    0.317    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.441 on 45 degrees of freedom
## Multiple R-squared:  0.1323, Adjusted R-squared:  0.0744 
## F-statistic: 2.286 on 3 and 45 DF,  p-value: 0.09154

Comp

model_tra_comp <- lm(comp_avg ~ ZS_attribution._1 * chinoy_trade, data = chinoy)

summary(model_tra_comp)

## 
## Call:
## lm(formula = comp_avg ~ ZS_attribution._1 * chinoy_trade, data = chinoy)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.5019 -0.8828 -0.3140  0.6541  4.1702 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                     1.454351   0.470916   3.088  0.00344 **
## ZS_attribution._1              -0.008099   0.241148  -0.034  0.97336   
## chinoy_trade                    0.224863   0.119054   1.889  0.06538 . 
## ZS_attribution._1:chinoy_trade  0.026487   0.058672   0.451  0.65384   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.278 on 45 degrees of freedom
## Multiple R-squared:  0.08083,    Adjusted R-squared:  0.01955 
## F-statistic: 1.319 on 3 and 45 DF,  p-value: 0.2799

SDO

model_tra_sdo <- lm(sdo_avg ~ ZS_attribution._1 * chinoy_trade, data = chinoy)

summary(model_tra_sdo)

## 
## Call:
## lm(formula = sdo_avg ~ ZS_attribution._1 * chinoy_trade, data = chinoy)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.2898 -1.2111 -0.0829  0.6803  4.1086 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                     2.08721    0.58236   3.584 0.000828 ***
## ZS_attribution._1               0.50879    0.29822   1.706 0.094880 .  
## chinoy_trade                    0.12393    0.14723   0.842 0.404380    
## ZS_attribution._1:chinoy_trade -0.08864    0.07256  -1.222 0.228201    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.581 on 45 degrees of freedom
## Multiple R-squared:  0.09952,    Adjusted R-squared:  0.03948 
## F-statistic: 1.658 on 3 and 45 DF,  p-value: 0.1896

Trust

model_tra_trust <- lm(trust_avg ~ ZS_attribution._1 * chinoy_trade, data = chinoy)

summary(model_tra_trust)

## 
## Call:
## lm(formula = trust_avg ~ ZS_attribution._1 * chinoy_trade, data = chinoy)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.0189 -0.7181  0.3384  1.0876  2.4451 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                     5.230092   0.582531   8.978 1.36e-11 ***
## ZS_attribution._1              -0.039605   0.298304  -0.133    0.895    
## chinoy_trade                   -0.211233   0.147272  -1.434    0.158    
## ZS_attribution._1:chinoy_trade  0.002337   0.072578   0.032    0.974    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.581 on 45 degrees of freedom
## Multiple R-squared:  0.05354,    Adjusted R-squared:  -0.009559 
## F-statistic: 0.8485 on 3 and 45 DF,  p-value: 0.4747

models <- list(
  "Social Inclusion"       = model_tra_soc_incl,
  "Professional Inclusion" = model_tra_pro_incl,
  "Cooperation"            = model_tra_coop,
  "Competitiveness"        = model_tra_comp,
  "SDO"                    = model_tra_sdo,
  "Trust"                  = model_tra_trust
)

modelsummary(models,
             stars = TRUE,
             coef_map = c("ZS_attribution._1" = "ZS Attribution",
                          "chinoy_trade" = "Chinoy Trade ZSB",
                          "ZS_attribution._1:chinoy_trade" = "ZS Attribution × Chinoy Trade"),
             gof_omit = "IC|Log|Adj|F|RMSE",
             output = "markdown")

	Social Inclusion	Professional Inclusion	Cooperation	Competitiveness	SDO	Trust
+ p < 0.1, * p < 0.05, p < 0.01, * p < 0.001
ZS Attribution	0.007	-0.232	-0.413	-0.008	0.509+	-0.040
	(0.312)	(0.237)	(0.272)	(0.241)	(0.298)	(0.298)
Chinoy Trade ZSB	0.070	0.092	-0.213	0.225+	0.124	-0.211
	(0.154)	(0.117)	(0.134)	(0.119)	(0.147)	(0.147)
ZS Attribution × Chinoy Trade	0.035	0.068	0.067	0.026	-0.089	0.002
	(0.076)	(0.058)	(0.066)	(0.059)	(0.073)	(0.073)
Num.Obs.	49	49	49	49	49	49
R2	0.029	0.032	0.132	0.081	0.100	0.054

Interactions with Chinoy (income)

Social inclusion

model_inc_soc_incl <- lm(social_incl_avg ~ ZS_attribution._1 * chinoy_income, data = chinoy)

summary(model_inc_soc_incl)

## 
## Call:
## lm(formula = social_incl_avg ~ ZS_attribution._1 * chinoy_income, 
##     data = chinoy)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4214 -0.7729  0.4558  1.0988  1.8488 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                     5.372501   0.656320   8.186 1.85e-10 ***
## ZS_attribution._1               0.118514   0.348816   0.340    0.736    
## chinoy_income                   0.008144   0.136675   0.060    0.953    
## ZS_attribution._1:chinoy_income 0.002501   0.069072   0.036    0.971    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.661 on 45 degrees of freedom
## Multiple R-squared:  0.02243,    Adjusted R-squared:  -0.04274 
## F-statistic: 0.3442 on 3 and 45 DF,  p-value: 0.7935

Professional inclusion

model_inc_pro_incl <- lm(pro_incl_avg ~ ZS_attribution._1 * chinoy_income, data = chinoy)

summary(model_inc_pro_incl)

## 
## Call:
## lm(formula = pro_incl_avg ~ ZS_attribution._1 * chinoy_income, 
##     data = chinoy)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3567 -0.7233  0.1347  1.0750  1.6642 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      5.26157    0.50265  10.468 1.22e-13 ***
## ZS_attribution._1               -0.10908    0.26715  -0.408    0.685    
## chinoy_income                    0.01585    0.10468   0.151    0.880    
## ZS_attribution._1:chinoy_income  0.02685    0.05290   0.508    0.614    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.272 on 45 degrees of freedom
## Multiple R-squared:  0.006329,   Adjusted R-squared:  -0.05992 
## F-statistic: 0.09554 on 3 and 45 DF,  p-value: 0.9621

Cooperation

model_inc_coop <- lm(coop_avg ~ ZS_attribution._1 * chinoy_income, data = chinoy)

summary(model_inc_coop)

## 
## Call:
## lm(formula = coop_avg ~ ZS_attribution._1 * chinoy_income, data = chinoy)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2411 -0.7466  0.2948  0.8901  2.9200 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      4.65139    0.55534   8.376 9.85e-11 ***
## ZS_attribution._1               -0.73804    0.29515  -2.501   0.0161 *  
## chinoy_income                    0.20532    0.11565   1.775   0.0826 .  
## ZS_attribution._1:chinoy_income  0.14579    0.05844   2.495   0.0164 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.405 on 45 degrees of freedom
## Multiple R-squared:  0.1747, Adjusted R-squared:  0.1197 
## F-statistic: 3.175 on 3 and 45 DF,  p-value: 0.03307

lm(coop_avg ~ ZS_attribution._1 * chinoy_income, data = chinoy) %>% 
  plot_model(type="pred", terms=c("ZS_attribution._1", "chinoy_income"))

Comp

model_inc_comp <- lm(comp_avg ~ ZS_attribution._1 * chinoy_income, data = chinoy)

summary(model_inc_comp)

## 
## Call:
## lm(formula = comp_avg ~ ZS_attribution._1 * chinoy_income, data = chinoy)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.7629 -1.0437 -0.3868  0.6132  4.1352 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                      1.38759    0.50517   2.747  0.00862 **
## ZS_attribution._1                0.16589    0.26848   0.618  0.53977   
## chinoy_income                    0.19647    0.10520   1.868  0.06834 . 
## ZS_attribution._1:chinoy_income -0.01268    0.05316  -0.239  0.81253   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.279 on 45 degrees of freedom
## Multiple R-squared:  0.08049,    Adjusted R-squared:  0.01919 
## F-statistic: 1.313 on 3 and 45 DF,  p-value: 0.2819

SDO

model_inc_sdo <- lm(sdo_avg ~ ZS_attribution._1 * chinoy_income, data = chinoy)

summary(model_inc_sdo)

## 
## Call:
## lm(formula = sdo_avg ~ ZS_attribution._1 * chinoy_income, data = chinoy)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.0468 -1.0884 -0.2945  0.6850  4.2914 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      3.20657    0.61539   5.211 4.55e-06 ***
## ZS_attribution._1                0.70999    0.32707   2.171   0.0353 *  
## chinoy_income                   -0.15973    0.12815  -1.246   0.2191    
## ZS_attribution._1:chinoy_income -0.13096    0.06476  -2.022   0.0491 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.557 on 45 degrees of freedom
## Multiple R-squared:  0.1259, Adjusted R-squared:  0.0676 
## F-statistic:  2.16 on 3 and 45 DF,  p-value: 0.1059

Trust

model_inc_trust <- lm(trust_avg ~ ZS_attribution._1 * chinoy_income, data = chinoy)

summary(model_inc_trust)

## 
## Call:
## lm(formula = trust_avg ~ ZS_attribution._1 * chinoy_income, data = chinoy)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.5922 -0.8658  0.3000  0.9579  2.4493 
## 
## Coefficients:
##                                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      5.54501    0.61389   9.033 1.14e-11 ***
## ZS_attribution._1               -0.25993    0.32627  -0.797   0.4298    
## chinoy_income                   -0.23821    0.12784  -1.863   0.0689 .  
## ZS_attribution._1:chinoy_income  0.04317    0.06461   0.668   0.5074    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.554 on 45 degrees of freedom
## Multiple R-squared:  0.08626,    Adjusted R-squared:  0.02534 
## F-statistic: 1.416 on 3 and 45 DF,  p-value: 0.2505

models <- list(
  "Social Inclusion"       = model_inc_soc_incl,
  "Professional Inclusion" = model_inc_pro_incl,
  "Cooperation"            = model_inc_coop,
  "Competitiveness"        = model_inc_comp,
  "SDO"                    = model_inc_sdo,
  "Trust"                  = model_inc_trust
)

modelsummary(models,
             stars = TRUE,
             coef_map = c("ZS_attribution._1" = "ZS Attribution",
                          "chinoy_income" = "Chinoy Income ZSB",
                          "ZS_attribution._1:chinoy_income" = "ZS Attribution × Chinoy Income"),
             gof_omit = "IC|Log|Adj|F|RMSE",
             output = "markdown")

	Social Inclusion	Professional Inclusion	Cooperation	Competitiveness	SDO	Trust
+ p < 0.1, * p < 0.05, p < 0.01, * p < 0.001
ZS Attribution	0.119	-0.109	-0.738*	0.166	0.710*	-0.260
	(0.349)	(0.267)	(0.295)	(0.268)	(0.327)	(0.326)
Chinoy Income ZSB	0.008	0.016	0.205+	0.196+	-0.160	-0.238+
	(0.137)	(0.105)	(0.116)	(0.105)	(0.128)	(0.128)
ZS Attribution × Chinoy Income	0.003	0.027	0.146*	-0.013	-0.131*	0.043
	(0.069)	(0.053)	(0.058)	(0.053)	(0.065)	(0.065)
Num.Obs.	49	49	49	49	49	49
R2	0.022	0.006	0.175	0.080	0.126	0.086

ZSB Attributon Pilot

Samantha Grayson

2025-07-27

Load packages

Load data

Exclusions

Data cleaning

Gather and clean numeric data

Create average scores

Combine with relevant order and condition info

Break down by condition

Variation in Attribution?

Variation in Inclusion?

What do these attributions mean for…

Inclusion

Cooperation under scarcity

Competitiveness

SDO

Trust

Interactions with ZSM

Social inclusion

Professional inclusion

Cooperation

Competition

SDO

Trust

Interactions with Chinoy (ethnicity)

Social inclusion

Professional inclusion

Cooperation

Comp

SDO

Trust

Interactions with Chinoy (citizenship)

Social inclusion

Professional inclusion

Cooperation

Comp

SDO

Trust

Interactions with Chinoy (trade)

Social inclusion

Professional inclusion

Cooperation

Comp

SDO

Trust

Interactions with Chinoy (income)

Social inclusion

Professional inclusion

Cooperation

Comp

SDO

Trust