How do People Sequentially Screen Multi-Attribute Preferential Options? A Hierarchical Latent Mixture Modelling Approach

When presented with a large array of possible alternatives consumers must quickly screen out undesirable options before more carefully deliberating over a smaller set. We consider the situation where these screening decisions are made sequentially and independently for each alternative. Understanding how attribute information is processed during these multi-attribute screening decisions provides useful insights into consumer decision making. Previous approaches to this problem have used equipment such as eye trackers, or intervened in the decision making process by obscuring information and requiring the participant reveal it. Here, we classify a broad set of decision strategies into higher-order classes based on whether those strategies assume processing of each attribute is complete or selective, and whether “good” attributes can compensate “bad” attributes. We then introduce a hierarchical latent mixture modelling approach that uses response times and choices to infer the higher-order decision class that best explains each individual’s screening decisions. We test the model against empirical data where the strategy decision makers ought to use was directly manipulated, demonstrating the model identifies the expected attribute processing strategy for all participants. In simulation, we demonstrate good recovery of the exhaustive set of decision classes we investigated, and extended this to show the model appropriately identifies different decision classes when different classes are present across a sample of participants. Our modelling approach thus provides a two-stage, principled solution to the challenge of identifying individual differences in preferential decision making: grouping a large set of candidate decision strategies into a smaller set of higher-order classes, and then discriminating between those higher-order classes in data with quantitative cognitive models of choices and response times. This allows the researcher to relax assumptions that a sample of participants adheres to the same decision strategy, and also capitalising on the benefits of hierarchical modelling to jointly estimate population parameters and random effects.