CAN Algorithm: An Individual Level Approach to identify Consequences and Norms Sensitivities and Overall Action/inaction Preferences in Moral Decision-making

摘要: Gawronski et al. (2017) developed a CNI model to measure an agent’s norms sensitivity, consequences sensitivity, and generalized inaction/action preferences when making moral decisions. However, the CNI model presupposed that an agent considers consequences—norms—generalized inaction/action preferences sequentially, which is untenable based on recent evidence. Moreover, the CNI model generates parameters at the group level based on binary categoric data. Hence, the C/N/I parameters cannot be used for correlation analyses or other conventional research designs. To solve these limitations, we developed the CAN algorithm to compute norms and consequences sensitivities and overall action/inaction preferences algebraically in a parallel manner. We re-analyzed the raw data of Gawronski et al.(2017) to test the methodological predictions. Our results demonstrate that: (1) the C parameter is approximately equal between the CNI model and CAN algorithm; (2) the N parameter under the CNI model approximately equals N/(1 – C) under the CAN algorithm; (3) the I parameter and A parameter are reversed around 0.5 – the larger the I parameter, the more the generalized inaction versus action preference and the larger the A parameter, the more overall action versus inaction 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.