library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.3 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.5.0 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(knitr)
library(fs)
library(here)
## here() starts at /Users/sjg2222/Library/CloudStorage/GoogleDrive-sjg2222@columbia.edu/My Drive/YEAR 1/PROJECTS/SHAI/Dominance and Job Preference/Code
library(stringr)
library(digest)
set.seed(34349785)
options(scipen = 999) # turn off scientific notation
##### Load Data ######
d_study <- read.csv("~/Google drive/My Drive/YEAR 1/PROJECTS/SHAI/Dominance and Job Preference/Data/study2A_pilot.csv")
# Remove first two rows
d_study = d_study[-1,]
d_study = d_study[-1,]
##### Exclusions ######
d_study = d_study %>% mutate(workerID = row_number())
# Remove previews and non-finished
d_study <- d_study %>%
filter(!((Finished == "Finished") |
Finished == "{\"ImportId\":\"finished\"}")) %>%
filter(attn == 24) # %>%
# filter(employment == 1 | employment == 2)
d_study = d_study %>%
select(-contains("DO"))
zsb = d_study %>%
select(contains("zsb")) %>%
names()
d_study$zeroSumBeliefs_score = d_study %>%
select(all_of(zsb)) %>%
mutate_all(as.numeric) %>%
rowMeans(na.rm = T)
socialGoodNames = d_study %>%
select(contains("socialimpact")) %>%
names()
d_study$socialimpact_score = d_study %>%
select(all_of(socialGoodNames)) %>%
mutate_all(as.numeric) %>%
rowMeans(na.rm = T)
Primary models
Pairwise comparisons: ZSB
# Green Machine vs. Machine
model1 <- d_study %>%
filter(name != "War Machine Inc.") %>%
mutate(name = relevel(as.factor(name), ref = "Machine Inc.")) %>%
lm(zeroSumBeliefs_score ~ name, .)
summary(model1)
##
## Call:
## lm(formula = zeroSumBeliefs_score ~ name, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4350 -1.4087 -0.1587 1.0848 3.8413
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.1587 0.2175 14.52 <0.0000000000000002 ***
## nameGreen Machine Inc. 0.2763 0.3106 0.89 0.376
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.568 on 100 degrees of freedom
## Multiple R-squared: 0.007852, Adjusted R-squared: -0.00207
## F-statistic: 0.7914 on 1 and 100 DF, p-value: 0.3758
# War Machine vs. Machine
model2 <- d_study %>%
filter(name != "Green Machine Inc.") %>%
mutate(name = relevel(as.factor(name), ref = "Machine Inc.")) %>%
lm(zeroSumBeliefs_score ~ name, .)
summary(model2)
##
## Call:
## lm(formula = zeroSumBeliefs_score ~ name, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6979 -1.4087 -0.1587 1.3021 3.8413
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.1587 0.2218 14.240 <0.0000000000000002 ***
## nameWar Machine Inc. 0.5393 0.3202 1.684 0.0953 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.6 on 98 degrees of freedom
## Multiple R-squared: 0.02813, Adjusted R-squared: 0.01822
## F-statistic: 2.837 on 1 and 98 DF, p-value: 0.09531
# Green Machine vs. War Machine
model3 <- d_study %>%
filter(name != "Machine Inc.") %>%
mutate(name = relevel(as.factor(name), ref = "War Machine Inc.")) %>%
lm(zeroSumBeliefs_score ~ name, .)
summary(model3)
##
## Call:
## lm(formula = zeroSumBeliefs_score ~ name, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6979 -1.4350 -0.0729 1.2428 3.5650
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.6979 0.2321 15.933 <0.0000000000000002 ***
## nameGreen Machine Inc. -0.2629 0.3249 -0.809 0.42
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.608 on 96 degrees of freedom
## Multiple R-squared: 0.006774, Adjusted R-squared: -0.003572
## F-statistic: 0.6547 on 1 and 96 DF, p-value: 0.4204
Pairwise comparisons: Social good
# Green Machine vs. Machine
model1 <- d_study %>%
filter(name != "War Machine Inc.") %>%
mutate(name = relevel(as.factor(name), ref = "Machine Inc.")) %>%
lm(socialimpact_score ~ name, .)
summary(model1)
##
## Call:
## lm(formula = socialimpact_score ~ name, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9295 -0.6190 0.0705 0.7067 2.0705
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.9295 0.1617 30.482 <0.0000000000000002 ***
## nameGreen Machine Inc. 0.3638 0.2310 1.575 0.118
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.166 on 100 degrees of freedom
## Multiple R-squared: 0.02421, Adjusted R-squared: 0.01445
## F-statistic: 2.481 on 1 and 100 DF, p-value: 0.1184
# War Machine vs. Machine
model2 <- d_study %>%
filter(name != "Green Machine Inc.") %>%
mutate(name = relevel(as.factor(name), ref = "Machine Inc.")) %>%
lm(socialimpact_score ~ name, .)
summary(model2)
##
## Call:
## lm(formula = socialimpact_score ~ name, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.9295 -0.9295 0.0735 1.0705 3.4097
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.9295 0.2028 24.302 < 0.0000000000000002 ***
## nameWar Machine Inc. -1.3392 0.2928 -4.574 0.000014 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.463 on 98 degrees of freedom
## Multiple R-squared: 0.1759, Adjusted R-squared: 0.1675
## F-statistic: 20.92 on 1 and 98 DF, p-value: 0.00001401
# Green Machine vs. War Machine
model3 <- d_study %>%
filter(name != "Machine Inc.") %>%
mutate(name = relevel(as.factor(name), ref = "War Machine Inc.")) %>%
lm(socialimpact_score ~ name, .)
summary(model3)
##
## Call:
## lm(formula = socialimpact_score ~ name, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2933 -0.9509 0.2249 0.7340 3.4097
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.5903 0.2004 17.91 < 0.0000000000000002 ***
## nameGreen Machine Inc. 1.7031 0.2806 6.07 0.0000000255 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 96 degrees of freedom
## Multiple R-squared: 0.2773, Adjusted R-squared: 0.2698
## F-statistic: 36.84 on 1 and 96 DF, p-value: 0.00000002548
Plot
library(ggpubr) # Package for visualizing data
library(see) # Package for visualizing data
my_comparisons <- list(c("Machine Inc.", "Green Machine Inc."), c("War Machine Inc.", "Machine Inc."), c("War Machine Inc.", "Green Machine Inc."))
ggplot(data = d_study,
mapping = aes(x = name,
y = zeroSumBeliefs_score,
color = name)) +
# means with confidence intervals
geom_violinhalf(position = position_nudge(0.1),
#fill = "gray23",
alpha = 0.4) +
geom_point(alpha = 0.3,
size = 2,
position = position_jitter(0.1)) +
stat_summary(fun.data = "mean_cl_boot",
size = 1,
geom = "linerange",
color = "grey50",
position = position_nudge(x = 0.2)) +
stat_summary(fun = "mean",
size = 0.3,
position = position_nudge(x = 0.2))+
# individual data points (jittered horizontally)
theme_bw()+
theme(legend.position="none")+
ylab("Zero Sum Belief")+
stat_compare_means(comparisons = my_comparisons, label = "p.signif", method = "t.test")+
scale_color_manual(values = c("War Machine Inc." = "darkblue", "Green Machine Inc." = "darkgreen", "Machine Inc." = "grey"))
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Removed 3 rows containing missing values or values outside the scale range
## (`geom_segment()`).
ggplot(data = d_study,
mapping = aes(x = name,
y = socialimpact_score,
color = name)) +
# means with confidence intervals
geom_violinhalf(position = position_nudge(0.1),
#fill = "gray23",
alpha = 0.4) +
geom_point(alpha = 0.3,
size = 2,
position = position_jitter(0.1)) +
stat_summary(fun.data = "mean_cl_boot",
size = 1,
geom = "linerange",
color = "grey50",
position = position_nudge(x = 0.2)) +
stat_summary(fun = "mean",
size = 0.3,
position = position_nudge(x = 0.2))+
# individual data points (jittered horizontally)
theme_bw()+
theme(legend.position="none")+
ylab("Social Impact")+
stat_compare_means(comparisons = my_comparisons, label = "p.signif", method = "t.test")+
scale_color_manual(values = c("War Machine Inc." = "darkblue", "Green Machine Inc." = "darkgreen", "Machine Inc." = "grey"))
## Warning: Removed 3 rows containing missing values or values outside the scale range
## (`geom_segment()`).
