[gov3009-l] 9/6 Applied Stats Change!

Hi everyone! Due to a scheduling issue, Stephen Raudenbush will be presenting next week. With less than 24 hours notice, *Mayya Komisarchik and Aaron Kaufman *have graciously offered to present their work in progress, entitled *How to Measure Legislative Compactness If You Only Know It When You See It*. Please find the abstract below. As usual, we will meet at noon in CGIS Knafel, Room 354, and lunch will be provided. See you there! -- Dana Higgins *Title: **How to Measure Legislative Compactness If You Only Know It When You See It* *Authors: *Aaron Kaufman, Gary King, and Mayya Komisarchik *Abstract: *The US Supreme Court, many state constitutions, and numerous judicial opinions require that legislative districts be “compact,” a concept assumed so simple that no definition is offered other than “you know it when you see it.” Academics, in contrast, have concluded that the concept is so complex that it has multiple theoretical dimensions requiring large numbers of conflicting empirical measures. We hypothesize that both are correct — that the concept is complex and multidimensional, but one particular unidimensional ordering represents a common understanding of compactness in the law and across people. We develop a survey design to elicit this understanding, without bias in favor of one’s own political views, and with high levels of intracoder and intercoder reliability (even though the standard paired comparisons approach fails). We then create a statistical model that predicts, with high accuracy and solely from the geometric features of the district, compactness evaluations by judges and other public officials from many jurisdictions (as well as by redistricting consultants and expert witnesses, law professors, law students, graduate students, undergraduates, ordinary citizens, and Mechanical Turk workers). As a companion to this paper, we offer data on compactness from our validated measure for 18,215 US state legislative and congressional districts, as well as software to compute this measure from any district shape. We also discuss what may be the wider applicability of our general methodological approach to measuring important concepts that you only know when you see.