This paper shows a general nonparametric unfolding technique for maximizing the correct classification of binary choice or two-category data. The motivation for and the primary focus of the unfolding technique are parliamentary roll call voting data. However, the procedures that implement the unfolding also can be applied to the problem of unfolding rank order data as well as analyzing a data set that would normally be the subject of a probit, logit, or linear probability analysis. One aspect of the scaling method greatly improves Manski's “maximum score estimator” technique for estimating limited dependent variable models. To unfold binary choice data two subproblems must be solved. First, given a set of chooser or legislator points, a cutting plane must be found such that it divides the legislators/choosers into two sets that reproduce the actual choices as closely as possible. Second, given a set of cutting planes for the binary choices, a point for each chooser or legislator must be found which reproduces the actual choices as closely as possible. Solutions for these two problems are shown in this paper. Monte Carlo tests of the procedure show it to be highly accurate in the presence of voting error and missing data.

There has been much discussion about how members of Congress desire money early in the campaign season. However, theoretical models of how contributions are allocated during the electoral cycle have been lacking. Our analysis attempts to remedy this gap by providing and testing a model which specifies how the process of bargaining between members of Congress and organized interests produces the pattern of donations observed over the course of the electoral cycle. Most notably, our results suggest that strategic incumbents can receive money early in the campaign if they desire but that they are generally unwilling to pay the price of lower aggregate fundraising and greater provision of access. These findings buttress earlier empirical findings that question the instrumental value of early money. In addition, our results highlight that contribution choices are fundamentally influenced by short-term factors, especially electoral conditions, that do not lend themselves to the routinized behavior necessary for contributors to invest in incumbents for long-run payoffs.

A statistic—the similarity coefficient—is developed for assessing the property that a set of scale items measures one and only one construct. This statistic is rooted in an explicit measurement model and is flexible enough to be used in exploratory scale analyses, even in small samples. Methods for analyzing similarity coefficients are described and illustrated in analyses of Stimson's (1991) policy mood data and Markus' (1990) popular individualism items. The Appendix discusses the statistical properties of similarity coefficients.

In recent years, political scientists have tested for the existence of rational expectations (RE) using survey-based aggregate data on subjective economic perceptions. These tests suffer from several conceptual shortcomings of a nontrivial nature. In this study, the meaning of RE is clarified, and also a test for strong rational expectations (SRE) where citizens possess heterogeneous information levels is set forth. These empirical tests provide insights into what kinds of information citizens use in forming expectations from that which they do not utilize but could employ to arrive at more accurate forecasts. Using inflation expectations data for the period January 1978—December 1993, the empirical findings indicate that citizens can benefit from greater reliance on objective economic and political conditions when formulating their inflation expectations. The broader implications of this work pertain not only to the execution of RE tests in political science, but also to distinguishing which types of information people do and do not (but could) incorporate in their decision-making calculus.