setwd("~/Google Drive/work/old projects/Good man effect/analysis/data")
load("cleaned_data_final.csv")
## rule out people who don't believe the setting
check = c("computer", "fake","real", "bot")
d <- subset(temp, grepl(paste(check, collapse= "|"), temp$things_unusual))
data = temp[!temp$MTurk_ID %in% d$MTurk_ID, ]
rm(d)
moderator_temp = data[, c(3,50:62)]
moderator_temp <- data.frame(moderator_temp[, c(1,8:14)], stack(moderator_temp[2:7]))## Warning in data.frame(moderator_temp[, c(1, 8:14)],
## stack(moderator_temp[2:7])): row names were found from a short variable and
## have been discardednames(moderator_temp)[10] = "condition"
names(moderator_temp)[9] = "accept"
## set condition variables
## good vs. bad
moderator_temp$good[moderator_temp$condition=="accept_good_average"|moderator_temp$condition=="accept_good_fair"|moderator_temp$condition=="accept_good_selfish"] =1
moderator_temp$good[moderator_temp$condition=="accept_bad_average"|moderator_temp$condition=="accept_bad_fair"|moderator_temp$condition=="accept_bad_selfish"] =0
moderator_temp$average[moderator_temp$condition=="accept_good_average"|moderator_temp$condition=="accept_bad_average"] =1
moderator_temp$average[moderator_temp$condition !="accept_good_average" & moderator_temp$condition !="accept_bad_average"] =0
moderator_temp$fair[moderator_temp$condition=="accept_good_fair"|moderator_temp$condition=="accept_bad_fair"] =1
moderator_temp$fair[moderator_temp$condition !="accept_good_fair" & moderator_temp$condition !="accept_bad_fair"] =0
moderator_temp$selfish[moderator_temp$condition=="accept_good_selfish"|moderator_temp$condition=="accept_bad_selfish"] =1
moderator_temp$selfish[moderator_temp$condition !="accept_good_selfish" & moderator_temp$condition !="accept_bad_selfish"] =0You can also embed plots, for example:
##
## Call:
## glm(formula = accept ~ moral_internalization, family = binomial,
## data = bad_reputation)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4791 -1.4697 0.9087 0.9109 0.9242
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.73344 0.72283 1.015 0.310
## moral_internalization -0.01484 0.16191 -0.092 0.927
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 751.8 on 586 degrees of freedom
## Residual deviance: 751.8 on 585 degrees of freedom
## (22 observations deleted due to missingness)
## AIC: 755.8
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = accept ~ (fair + selfish), family = binomial, data = bad_reputation)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.0282 -0.9394 0.1432 0.9341 1.4357
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.6035 0.1487 4.060 4.91e-05 ***
## fair 3.9712 0.7261 5.469 4.52e-08 ***
## selfish -1.1929 0.2098 -5.687 1.29e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 758.10 on 592 degrees of freedom
## Residual deviance: 538.88 on 590 degrees of freedom
## (16 observations deleted due to missingness)
## AIC: 544.88
##
## Number of Fisher Scoring iterations: 7##
## Call:
## glm(formula = accept ~ moral_internalization * (fair + selfish),
## family = binomial, data = bad_reputation)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.0646 -0.9227 0.1355 0.9306 1.4955
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.98027 1.23137 0.796 0.426
## moral_internalization -0.08354 0.27561 -0.303 0.762
## fair 7.04375 5.10496 1.380 0.168
## selfish -1.79140 1.74386 -1.027 0.304
## moral_internalization:fair -0.67495 1.08047 -0.625 0.532
## moral_internalization:selfish 0.12387 0.39047 0.317 0.751
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 751.80 on 586 degrees of freedom
## Residual deviance: 529.09 on 581 degrees of freedom
## (22 observations deleted due to missingness)
## AIC: 541.09
##
## Number of Fisher Scoring iterations: 7##
## Call:
## glm(formula = accept ~ moral_internalization * (fair + average),
## family = binomial, data = bad_reputation)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.0646 -0.9227 0.1355 0.9306 1.4955
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.81113 1.23482 -0.657 0.5113
## moral_internalization 0.04032 0.27660 0.146 0.8841
## fair 8.83515 5.10580 1.730 0.0836 .
## average 1.79140 1.74386 1.027 0.3043
## moral_internalization:fair -0.79882 1.08072 -0.739 0.4598
## moral_internalization:average -0.12387 0.39047 -0.317 0.7511
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 751.80 on 586 degrees of freedom
## Residual deviance: 529.09 on 581 degrees of freedom
## (22 observations deleted due to missingness)
## AIC: 541.09
##
## Number of Fisher Scoring iterations: 7##
## Call:
## glm(formula = accept ~ moral_symbolization, family = binomial,
## data = bad_reputation)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5477 -1.4511 0.8960 0.9187 0.9615
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.88942 0.30402 2.926 0.00344 **
## moral_symbolization -0.05109 0.06688 -0.764 0.44495
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 751.80 on 586 degrees of freedom
## Residual deviance: 751.22 on 585 degrees of freedom
## (22 observations deleted due to missingness)
## AIC: 755.22
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = accept ~ moral_symbolization * (fair + selfish),
## family = binomial, data = bad_reputation)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.0505 -0.8950 0.1417 0.9307 1.5943
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.69327 0.51405 1.349 0.177
## moral_symbolization -0.01924 0.11359 -0.169 0.866
## fair 3.53781 2.39847 1.475 0.140
## selfish -0.70055 0.72010 -0.973 0.331
## moral_symbolization:fair 0.09851 0.54331 0.181 0.856
## moral_symbolization:selfish -0.12675 0.16042 -0.790 0.429
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 751.80 on 586 degrees of freedom
## Residual deviance: 527.92 on 581 degrees of freedom
## (22 observations deleted due to missingness)
## AIC: 539.92
##
## Number of Fisher Scoring iterations: 7##
## Call:
## glm(formula = accept ~ moral_symbolization * (fair + average),
## family = binomial, data = bad_reputation)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.0505 -0.8950 0.1417 0.9307 1.5943
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.007277 0.504276 -0.014 0.988
## moral_symbolization -0.145992 0.113280 -1.289 0.197
## fair 4.238359 2.396392 1.769 0.077 .
## average 0.700551 0.720095 0.973 0.331
## moral_symbolization:fair 0.225263 0.543249 0.415 0.678
## moral_symbolization:average 0.126755 0.160422 0.790 0.429
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 751.80 on 586 degrees of freedom
## Residual deviance: 527.92 on 581 degrees of freedom
## (22 observations deleted due to missingness)
## AIC: 539.92
##
## Number of Fisher Scoring iterations: 7Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.