The researchers developed a new method called the mixture of latent trait analyzers model to group categorical data into clusters. This model combines discrete groups and continuous traits to better capture patterns in the data. By applying this model to real-world data on long-term care and voting behavior, they found that it produced more accurate and meaningful results compared to other clustering methods.