Steady political polarization since the late 1970s ranks among the most consequential transformations of American politics—one with far-reaching consequences for governance, congressional performance, the legitimacy of the Supreme Court, and citizen perceptions of the stakes of party conflict and elections. Our understanding of this polarization critically depends on measuring it. Its measurement in turn began with the invention of the NOMINATE algorithm and the widespread adoption of its estimates of the ideal points of members of Congress. Although the NOMINATE project has not been immune from technical and conceptual critique, its impact on how we think about contemporary politics and its discontents has been extraordinary and has helped to stimulate the creation of several similar scores. In order to deepen appreciation of this broadly important intellectual phenomenon, we offer an intuitively accessible treatment of the mathematics and conceptual assumptions of NOMINATE. We also stress that NOMINATE scores are a major resource for understanding other eras in American political development (APD) besides the current great polarization. To illustrate this point, we introduce readers to Voteview, which provides two-dimensional snapshots of congressional roll calls, among other data that it generates. We conclude by sketching how APD scholarship might contribute to the contemporary polarization discussion. Placing polarization and depolarization in historical perspective may powerfully illuminate whether, how, and why our current polarization might recede.

According to the quantitative indicators scholars use to measure political polarization, the Gilded Age stands out for some of the most party-polarized Congresses of all time. By contrast, historians of the era depict the two major parties as presenting few programmatic alternatives to one another. I argue that a large share of the party-line votes in the Congress of this period are poorly suited to the standard conceptualization as “polarization,” meaning wide divergence on an ideological continuum structuring alternative views on national policy. Specifically, the era's continuous battles over the distribution of particularized benefits, patronage, and control of political office make little sense conceived as stemming from individual members' preferences on an underlying ideological dimension. They are better understood as fights between two long coalitions competing for power and distributive gains. In short, the Gilded Age illustrates that political parties are fully capable of waging ferocious warfare over spoils and office, even despite a relative lack of sharp party differences over national policy.

Poole and Rosenthal's NOMINATE scores have been a boon to the study of Congress, but they are not without limitations. We focus on two limitations that are especially important in historical applications. First, the dimensions uncovered by NOMINATE do not necessarily have a consistent ideological meaning over time. Our case study of the 1920s highlights the challenge of interpreting NOMINATE scores in periods when party lines do not map well onto the main contours of ideological debate in political life. Second, the commonly used DW-NOMINATE variant of these scores makes assumptions that are not well suited to dealing with rapid or non-monotonic ideological change. A case study of Southern Democrats in the New Deal era suggests that a more flexible dynamic item-response model provides a better fit for this important period. These applications illustrate the feasibility and value of tailoring one's model and data to one's research goals rather than relying on off-the-shelf NOMINATE scores.

This article aims to persuade historically oriented political scientists that ideal point techniques such as DW-NOMINATE can illuminate much about politics and lawmaking and be very useful to better understanding some of the key questions put forward by American political development (APD) scholars. We believe that there are many lines of inquiry of interest to APD scholars where ideal point measure could be useful, but which have been effectively foreclosed because of the assumptions undergirding DW-NOMINATE. In particular, we focus on three issues as particularly important: (1) the assumption of linear change; (2) the collapsing of distinct policy issue areas into a single “ideology” score; and (3) an agnosticism toward policy development, institutional context, and historical periodization. We go over these issues in detail and propose that many of these concerns can be addressed by taking seriously the proposition that policy substance, historical and political context, and the temporal dimension of political processes be integrated into the core of our measures and analyses. We also discuss a set of techniques for addressing these issues in order to answer specific questions of broad interest to both APD scholars and other Americanists.

Several of the articles in this volume criticize the use of DW-NOMINATE in historical work in American politics and suggest alternative approaches to the use of roll call voting data. While many of criticisms are certainly valid, their practical implications are often overstated. Moreover, the suggested alternatives are either impractical for most historically oriented scholars and or do not adequately address the underlying problems. Almost all of the criticisms can be addressed by correct application of DW-NOMINATE results or those of other closely related measures.

The NOMINATE algorithm has become the most important analytical tool used in the study of the United States Congress. As such, congressional scholars have developed a great many social conventions, practices, and assumptions that enable interpretation of the statistical artifacts the algorithm produces. However, as many of these scholars recognize, serious problems emerge whenever we try to translate these statistical artifacts into language and thus attempt to assign them meaning in historical analysis. These problems are irresolvable because they reside in the very construction of the algorithm itself.