# Moral Dilemma Judgment Hennig and Huetter (2020) proposed a multinomial model of moral dilemma judgment and evaluated the model in a series of experiments. Participants were presented with hypothetical scenarios that required a decision whether or not to break a moral norm. Berentelg (2020) conducted a replication study that was designed to be similar to Experiment 2b in Hennig and Huetter (2020). ```r data(moraldilemma) ``` ## Format `MDHennig2020` A data frame consisting of seven variables: - **`selfrel`**: factor. Instructions about self-relevant consequences of the decision where either (`absent`) or (`present`). - **`congrcy`**: factor. Endorsement of consequences and norm endorsement lead to different decisions (`incongruent`) or to the same decision (`congruent`). - **`default`**: factor. The norm may be adhered to by continuing (`inaction` default state) or by changing (`action` default state) an ongoing behavior. - **`breaknorm`**: factor. Decision to break the norm. - **`exp1`, `exp2b`**: the aggregate response frequencies for Experiment 1 and 2b, respectively. - **`treeid`**: an identifier for the single trees of the joint multinomial model. `MDreplication` A data frame containing 751 observations of five variables: - **`selfrel`**: factor. See above. - **`gender`**: factor. Participant gender. - **`age`**: participant age. - **`rt`**: median response time (in seconds) across scenarios. - **`y`**: a matrix of response frequencies per participant. Each column represents a combination of the factors `congrcy`, `default`, and `breaknorm`. ## Source Hennig, M., & Huetter, M. (2020). Revisiting the divide between deontology and utilitarianism in moral dilemma judgment: A multinomial modeling approach. **Journal of Personality and Social Psychology** 118 (1), 22--56. tools:::Rd_expr_doi("10.1037/pspa0000173") Berentelg, M. (2020). **Multinomial modeling of moral dilemma judgment: A replication study**. Bachelor thesis. University of Tuebingen, Germany. [https://osf.io/mb32t/](https://osf.io/mb32t/) ## See Also `mpt`. ## Examples ```r data(moraldilemma) ## Exp. 1: proCNI and process dissociation (PD) model s <- mptspec("proCNI") exp1 <- subset(MDHennig2020, selfrel == "absent") mpt(update(s, .restr = list(J=I)), data = exp1, freqvar = "exp1") mpt(update(s, .restr = list(I=0, J=1)), data = exp1, freqvar = "exp1") ## Exp. 2b: self-relevant consequences and norm endorsement s <- mptspec("proCNI", .replicates = 2, .restr = list(J1=I1, J2=I2)) m1 <- mpt(s, data = MDHennig2020, freqvar = "exp2b") m2 <- mpt(update(m1$spec, .restr = list(N1=N2)), data = m1$y) anova(m2, m1) ## Replication of Exp. 2b md.agg <- aggregate(y ~ selfrel, MDreplication, sum) y <- as.vector(t(md.agg[, -1])) m3 <- mpt(s, data = y) m4 <- mpt(update(s, .restr = list(N1=N2)), data = y) anova(m4, m3) coefs <- c(diff(coef(m3)[c("N2", "N1")]), diff(coef(m1)[c("N2", "N1")])) names(coefs) <- c("Replication", "Hennig & Huetter\n(2020, Exp. 2b)") ci <- coefs + rbind( qnorm(c(.025, .975))*sqrt(sum(diag(vcov(m3))[c("N2", "N1")])), qnorm(c(.025, .975))*sqrt(sum(diag(vcov(m1))[c("N2", "N1")])) ) dotchart(coefs, pch = 16, xlim = c(-.2, 1), xlab = expression(N[absent] - N[present]~"(proCNI model, 95% CI)"), main = paste("Self-relevant consequences and norm endorsement", "in moral dilemma judgment", sep = "\n")) abline(v = 0, col = "gray") arrows(ci[, 1], 1:2, ci[, 2], 1:2, .05, 90, 3) ```

moraldilemma dataset