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## Attaching package: 'ggplot2'
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## The following objects are masked from 'package:psych':
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## %+%, alpha
Analysis 1: How accept vs. reject is affected by moral identity (MI) internalization:
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## Call:
## glm(formula = accept ~ moral_internalization, family = binomial,
## data = moderator_temp)
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## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5150 -1.4807 0.8979 0.8999 0.9222
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.57520 0.51254 1.122 0.262
## moral_internalization 0.02721 0.11492 0.237 0.813
##
## (Dispersion parameter for binomial family taken to be 1)
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## Null deviance: 1491.1 on 1171 degrees of freedom
## Residual deviance: 1491.0 on 1170 degrees of freedom
## AIC: 1495
##
## Number of Fisher Scoring iterations: 4
Analysis 1.1: How accept vs. reject is affected by the interaction between MI internalization and fair, unfair offers. Here 1 in ylab “Accept” means that participants accepted the offer, 0 means that participants rejected the offer. In “Sharing”, average means that participants received an average offer (3 out of 10), fair means they received 5 out of 10, selfish means they received 1 out of 10.
##
## Call:
## glm(formula = accept ~ moral_internalization * (fair + selfish),
## family = binomial, data = moderator_temp)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.2383 -0.9356 0.1227 0.9154 1.4597
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.598620 0.877940 0.682 0.495
## moral_internalization 0.011881 0.196802 0.060 0.952
## fair 1.702367 4.615849 0.369 0.712
## selfish -1.284953 1.235177 -1.040 0.298
## moral_internalization:fair 0.575552 1.071120 0.537 0.591
## moral_internalization:selfish 0.007859 0.276842 0.028 0.977
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1491.1 on 1171 degrees of freedom
## Residual deviance: 1047.3 on 1166 degrees of freedom
## AIC: 1059.3
##
## Number of Fisher Scoring iterations: 7
Analysis 2: How accept vs. reject is affected MI symbolization:
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## Call:
## glm(formula = accept ~ moral_symbolization, family = binomial,
## data = moderator_temp)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.5771 -1.4596 0.8826 0.9101 0.9618
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.96554 0.21662 4.457 8.3e-06 ***
## moral_symbolization -0.06211 0.04757 -1.306 0.192
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1491.1 on 1171 degrees of freedom
## Residual deviance: 1489.3 on 1170 degrees of freedom
## AIC: 1493.3
##
## Number of Fisher Scoring iterations: 4
Analysis 2.1: How accept vs. reject is affected by the interaction between MI symbolization and fair, unfair offers:
##
## Call:
## glm(formula = accept ~ moral_symbolization * (fair + selfish),
## family = binomial, data = moderator_temp)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.1789 -0.9024 0.1197 0.9168 1.6498
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.75743 0.36728 2.062 0.0392 *
## moral_symbolization -0.02452 0.08104 -0.303 0.7622
## fair 3.33231 1.79808 1.853 0.0638 .
## selfish -0.62381 0.51109 -1.221 0.2223
## moral_symbolization:fair 0.20847 0.42298 0.493 0.6221
## moral_symbolization:selfish -0.14666 0.11384 -1.288 0.1976
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1491.1 on 1171 degrees of freedom
## Residual deviance: 1042.7 on 1166 degrees of freedom
## AIC: 1054.7
##
## Number of Fisher Scoring iterations: 7