The article discusses different types of probability models like Logit and Probit, and how they can be used to interpret data in various ways. It explains how these models can be applied to different situations, such as binary, sequential, ordinal, multinomial, and conditional scenarios. The researchers found that these models can help analyze and understand data in a more structured and meaningful way, leading to valuable insights and conclusions.