Dear Applied Statistics Workshop Community, Our next meeting of the spring semester will be on January 31 (12:00 EST). Sooahn Shin presents "Measuring Issue Specific Ideal Points from Roll Call Votes." <When> January 31, 12:00 to 1:30 PM, EST Lunch will be available for pick-up inside CGIS K354. <Where> In-person: CGIS K354 Zoom: https://harvard.zoom.us/j/93217566507?pwd=elBwYjRJcWhlVE5teE1VNDZoUXdjQT09 <Abstract> Ideal points are widely used to measure the ideology and policy preferences of political actors, from voters and politicians to sovereign states. Yet, the lingering challenge is to measure ideal points specific to a single issue area. Scholars who wish to measure preferences in a specific area of interest often resort to subsetting the voting data, resulting in the loss of valuable information and rendering ambiguous comparisons across different issue areas. To address this, I introduce IssueIRT — a hierarchical Item Response Theory (IRT) model that estimates an issue-specific axis representing a continuum extending from left to right positions on the issue using roll-call votes and their issue labels. This approach first estimates multidimensional ideal points using all available voting data, which are then projected onto issue-specific axes to generate single-dimensional, issue-specific ideal points. Furthermore, I develop a measure of issue similarity to compare the alignment of different issue areas on a unified left-to-right spectrum. I demonstrate that IssueIRT effectively captures issue-specific voting behaviors through simulations and a validation study that measures sectionalism in the US House of Representatives during the 1890s gold standard era. Finally, I show that polarization in Congress has markedly increased across 32 separate issues from 1979 to 2023. IssueIRT is implemented in issueirt, an open-source R package. <2023-2024 Schedule> GOV 3009 Website: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009 Calendar: https://calendar.google.com/calendar/u/0?cid=Y18zdjkzcGF2OWZqa2tsZHJidTlzbm… Best, Jialu -- Jialu Li Department of Government Harvard University jialu_li(a)g.harvard.edu