Before revision: Ideal point estimation and dimensionality reduction have long been utilized to simplify and cluster complex, high-dimensional political data(e. g., roll-call votes and surveys) for use in analysis and visualization. These methods often work by finding the directions or principal components (PCs) on which either the data varies the most or respondents make the fewest decision errors. However, these PCs, which usually reflect the left-right political spectrum, are sometimes uninformative in explaining significant differences in the distribution of the data(e. g., how to categorize a set of highly-moderate voters).
After revision: Ideal point estimation and dimensionality reduction have long been utilized to simplify and cluster complex, high-dimensional data. However, the latent spaces derived from these methods are sometimes uninformative in explaining significant differences in the distribution of the data(e. g., how to categorize a set of highly-moderate voters).