Abstract

Recently, a multinomial process tree model was developed to measure an agent’s consequence sensitivity, norm sensitivity, and generalized inaction/action preferences when making moral decisions (CNI model). However, the CNI model presupposed that an agent considersconsequences—norms—generalizedinaction/actionpreferences sequentially, which is untenable based on recent evidence. Besides, the CNI model generates parameters at the group level based on binary categorical data. Hence, theC/N/Iparameters cannot be used for correlation analyses or other conventional research designs. To solve these limitations, we developed the CAN algorithm to computenormandconsequencesensitivities and overallaction/inactionpreferences algebraically in a parallel manner. We re-analyzed the raw data of the original CNI model to test the methodological predictions. Our results demonstrate that: (1) theCparameter is approximately equal between the CNI model and CAN algorithm; (2) theNparameter under the CNI model approximately equalsN/(1 −C) under the CAN algorithm; (3) theIparameter andAparameter are reversed around 0.5 – the larger theIparameter, the more the generalized inactionversusaction preference and the larger theAparameter, the more overall actionversusinaction preference; (4) tests of differences in parameters between groups with the CNI model and CAN algorithm led to almost the same statistical conclusion; (5) parameters from the CAN algorithm can be used for correlational analyses and multiple comparisons, and this is an advantage over the parameters from the CNI model. The theoretical and methodological implications of our study were also discussed.