Load data
setwd("~/Projects/Prosociality DG_Sai/EX3 cost benefit")
pilotResult <- read.csv("pilotResult_core.csv")
library(ggplot2)
MU~time*conditions
model1 <- lm(Dictator_FinalValue ~ log(Dictator_StartToEndTicks)*(boost3+penalty3), data = pilotResult[pilotResult$Dictator_FinalValue > 0,], na.action = na.omit)
summary(model1)
##
## Call:
## lm(formula = Dictator_FinalValue ~ log(Dictator_StartToEndTicks) *
## (boost3 + penalty3), data = pilotResult[pilotResult$Dictator_FinalValue >
## 0, ], na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -43.33 -9.79 0.02 7.10 72.65
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 56.50 6.79 8.32
## log(Dictator_StartToEndTicks) -5.57 3.20 -1.74
## boost3 -14.05 10.29 -1.37
## penalty3 -19.65 11.32 -1.74
## log(Dictator_StartToEndTicks):boost3 1.01 4.42 0.23
## log(Dictator_StartToEndTicks):penalty3 7.85 4.67 1.68
## Pr(>|t|)
## (Intercept) 1.4e-15 ***
## log(Dictator_StartToEndTicks) 0.082 .
## boost3 0.173
## penalty3 0.083 .
## log(Dictator_StartToEndTicks):boost3 0.820
## log(Dictator_StartToEndTicks):penalty3 0.094 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19.9 on 406 degrees of freedom
## Multiple R-squared: 0.104, Adjusted R-squared: 0.093
## F-statistic: 9.43 on 5 and 406 DF, p-value: 1.62e-08
pilotResult$rational[pilotResult$Dictator_FinalValue == 0 & !is.na(pilotResult$Dictator_FinalValue)] <- 1
pilotResult$rational[!pilotResult$Dictator_FinalValue == 0 & !is.na(pilotResult$Dictator_FinalValue)] <- 0
model1 <- lm(log(Dictator_StartToEndTicks) ~ rational*(boost3+penalty3), data = pilotResult, na.action = na.omit)
summary(model1)#rational men take less time
##
## Call:
## lm(formula = log(Dictator_StartToEndTicks) ~ rational * (boost3 +
## penalty3), data = pilotResult, na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3849 -0.3451 -0.0535 0.3393 1.7696
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.0610 0.0422 48.90 < 2e-16 ***
## rational -0.1516 0.0880 -1.72 0.085 .
## boost3 0.4128 0.0595 6.94 1.1e-11 ***
## penalty3 0.5388 0.0651 8.28 9.1e-16 ***
## rational:boost3 -0.3663 0.1267 -2.89 0.004 **
## rational:penalty3 -0.4930 0.1174 -4.20 3.1e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.518 on 567 degrees of freedom
## Multiple R-squared: 0.215, Adjusted R-squared: 0.208
## F-statistic: 31 on 5 and 567 DF, p-value: <2e-16
pilotResult$Choices[pilotResult$rational == 1 & !is.na(pilotResult$rational)] <- "rational"
pilotResult$Choices[pilotResult$rational == 0 & !is.na(pilotResult$rational)] <- "prosocial"
bar <- ggplot(pilotResult, aes(ConditionName, Dictator_StartToEndTicks, fill = Choices))
bar + stat_summary(fun.y = mean, geom = "bar") + stat_summary(fun.data = mean_cl_normal, geom = "errorbar", width = 0.2) + facet_wrap(~Choices)+ labs(x = "ConditionName", y = "Time used", fill = "Choices") +ggtitle("Decision time comparison between prosocial actor and rational actor")
Figure
Contemplative fairness/absolute fairness analysis
##
## Call:
## glm(formula = comtemplative_fairness ~ Dictator_StartToEndTicks *
## ConditionName, na.action = na.omit)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.8237 -0.3804 -0.0612 0.4040 0.9550
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.24327 0.06427 3.78
## Dictator_StartToEndTicks 0.01848 0.00454 4.07
## ConditionNamecontrol 0.42637 0.08694 4.90
## ConditionNamepenalty -0.24993 0.08644 -2.89
## Dictator_StartToEndTicks:ConditionNamecontrol -0.02913 0.00726 -4.01
## Dictator_StartToEndTicks:ConditionNamepenalty -0.00978 0.00592 -1.65
## Pr(>|t|)
## (Intercept) 0.00017 ***
## Dictator_StartToEndTicks 5.3e-05 ***
## ConditionNamecontrol 1.2e-06 ***
## ConditionNamepenalty 0.00398 **
## Dictator_StartToEndTicks:ConditionNamecontrol 6.8e-05 ***
## Dictator_StartToEndTicks:ConditionNamepenalty 0.09889 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1907)
##
## Null deviance: 136.21 on 572 degrees of freedom
## Residual deviance: 108.15 on 567 degrees of freedom
## AIC: 684.7
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = comtemplative_fairness ~ Dictator_StartToEndTicks *
## ConditionName, na.action = na.omit)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.853 -0.293 0.156 0.353 0.892
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.49190 0.07705 6.38
## Dictator_StartToEndTicks 0.00786 0.00504 1.56
## ConditionNamecontrol 0.45132 0.10388 4.34
## ConditionNamepenalty -0.42476 0.11543 -3.68
## Dictator_StartToEndTicks:ConditionNamecontrol -0.02948 0.00833 -3.54
## Dictator_StartToEndTicks:ConditionNamepenalty -0.00103 0.00695 -0.15
## Pr(>|t|)
## (Intercept) 4.7e-10 ***
## Dictator_StartToEndTicks 0.11921
## ConditionNamecontrol 1.8e-05 ***
## ConditionNamepenalty 0.00027 ***
## Dictator_StartToEndTicks:ConditionNamecontrol 0.00045 ***
## Dictator_StartToEndTicks:ConditionNamepenalty 0.88203
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1915)
##
## Null deviance: 102.299 on 411 degrees of freedom
## Residual deviance: 77.758 on 406 degrees of freedom
## AIC: 496.2
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = absolute_fairness ~ Dictator_StartToEndTicks *
## ConditionName, na.action = na.omit)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.6337 -0.1496 -0.0693 0.3751 0.9373
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.061227 0.056293 1.09
## Dictator_StartToEndTicks 0.000496 0.003975 0.12
## ConditionNamecontrol 0.608412 0.076141 7.99
## ConditionNamepenalty 0.059293 0.075705 0.78
## Dictator_StartToEndTicks:ConditionNamecontrol -0.011143 0.006357 -1.75
## Dictator_StartToEndTicks:ConditionNamepenalty 0.002008 0.005183 0.39
## Pr(>|t|)
## (Intercept) 0.28
## Dictator_StartToEndTicks 0.90
## ConditionNamecontrol 7.5e-15 ***
## ConditionNamepenalty 0.43
## Dictator_StartToEndTicks:ConditionNamecontrol 0.08 .
## Dictator_StartToEndTicks:ConditionNamepenalty 0.70
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1463)
##
## Null deviance: 112.611 on 572 degrees of freedom
## Residual deviance: 82.956 on 567 degrees of freedom
## AIC: 532.7
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = absolute_fairness ~ Dictator_StartToEndTicks *
## ConditionName, na.action = na.omit)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.853 -0.192 -0.083 0.193 0.941
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 0.10596 0.06698 1.58
## Dictator_StartToEndTicks -0.00150 0.00438 -0.34
## ConditionNamecontrol 0.83726 0.09030 9.27
## ConditionNamepenalty 0.24863 0.10035 2.48
## Dictator_StartToEndTicks:ConditionNamecontrol -0.02011 0.00725 -2.78
## Dictator_StartToEndTicks:ConditionNamepenalty -0.00472 0.00604 -0.78
## Pr(>|t|)
## (Intercept) 0.1144
## Dictator_StartToEndTicks 0.7312
## ConditionNamecontrol <2e-16 ***
## ConditionNamepenalty 0.0136 *
## Dictator_StartToEndTicks:ConditionNamecontrol 0.0058 **
## Dictator_StartToEndTicks:ConditionNamepenalty 0.4344
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.1447)
##
## Null deviance: 96.437 on 411 degrees of freedom
## Residual deviance: 58.762 on 406 degrees of freedom
## AIC: 380.8
##
## Number of Fisher Scoring iterations: 2
