In most situations of bilateral cooperation we can observe only whether or not potential partners actually cooperate. Yet we often want to know what factors lead the actors to enter into and continue cooperation. The model we develop—a dynamic version of bivariate probit with partial observability—permits one to estimate the probabilities that either of two parties would want to cooperate and to identify the factors that affect these probabilities. As an illustration, we focus on agreements between national governments and the International Monetary Fund. The model should have a wide applicability.

Recently in this journal, Gary Cox introduced a special issue by noting that “The spatial model is the workhorse theory of modern legislative studies” (Cox 2001, p. 189). In this special issue, the focus is on a different type of spatial model than that typically used in legislative studies. Specifically, we concentrate on models in which the data are dependent on one another as a result of geographic influences, broadly defined.

Party identification has been studied extensively using both individual- and aggregate-level data. This paper attempts to formulate a statistical model that can account for the range of empirical generalizations that have emerged from aggregate time series and panel surveys. Using Monte Carlo simulation, we show that only certain types of data generation processes can account for these empirical regularities. Deciding which of the remaining types best explains the data means investigating the ways in which individual-level partisanship behaves over time. Partisanship at the aggregate-level tends to be highly autocorrelated, reequilibrating slowly in the wake of each perturbation. Working downward from the analysis of aggregate data, previous researchers argued that aggregate partisanship is fractionally integrated and contended that dynamics at the individual level are therefore heterogeneous. Using data from three panel surveys, we present the first direct assessment of individual-level dynamics. We also investigate the hypothesis that these dynamics vary among individuals, a claim that motivates much recent work on fractionally integrated time series. The model that best explains the observed characteristics of party identification is one in which individuals respond in similar ways to external shocks, reequilibrate rapidly thereafter, and seldom change their equilibrium level of partisan attachment.

For many observers, experiments symbolize science. Young and old alike are drawn to science by visions of women and men (often in white coats) experimenting on new ideas. In sciences such as chemistry and psychology, the image is real. Although both disciplines contain theorists and nonexperimental researchers, most of their professional norms and great accomplishments are indescribable without reference to experimental methods. In such disciplines, experiments are the leading vehicle of knowledge creation.

For more than half a century, social scientists have probed the aggregate correlates of the vote for the Nazi party (NSDAP) in Weimar Germany. Since individual-level data are not available for this time period, aggregate census data for small geographic units have been heavily used to infer the support of the Nazi party by various compositional groups. Many of these studies hint at a complex geographic patterning. Recent developments in geographic methodologies, based on Geographic Information Science (GIS) and spatial statistics, allow a deeper probing of these regional and local contextual elements. In this paper, a suite of geographic methods—global and local measures of spatial autocorrelation, variography, distance-based correlation, directional spatial correlograms, vector mapping, and barrier definition (wombling)—are used in an exploratory spatial data analysis of the NSDAP vote. The support for the NSDAP by Protestant voters (estimated using King's ecological inference procedure) is the key correlate examined. The results from the various methods are consistent in showing a voting surface of great complexity, with many local clusters that differ from the regional trend. The Weimar German electoral map does not show much evidence of a nationalized electorate, but is better characterized as a mosaic of support for “milieu parties,” mixed across class and other social lines, and defined by a strong attachment to local traditions, beliefs, and practices.

This paper discusses a nonstationary, heterogeneous Markov model designed to estimate entry and exit transition probabilities at the micro level from a time series of independent cross-sectional samples with a binary outcome variable. The model has its origins in the work of Moffitt and shares features with standard statistical methods for ecological inference. We outline the methodological framework proposed by Moffitt and present several extensions of the model to increase its potential application in a wider array of research contexts. We also discuss the relationship with previous lines of related research in political science. The example illustration uses survey data on American presidential vote intentions from a five-wave panel study conducted by Patterson in 1976. We treat the panel data as independent cross sections and compare the estimates of the Markov model with both dynamic panel parameter estimates and the actual observations in the panel. The results suggest that the proposed model provides a useful framework for the analysis of transitions in repeated cross sections. Open problems requiring further study are discussed.

Panel data are a very valuable resource for finding empirical solutions to political science puzzles. Yet numerous published studies in political science that use panel data to estimate models with dynamics have failed to take into account important estimation issues, which calls into question the inferences we can make from these analyses. The failure to account explicitly for unobserved individual effects in dynamic panel data induces bias and inconsistency in cross-sectional estimators. The purpose of this paper is to review dynamic panel data estimators that eliminate these problems. I first show how the problems with cross-sectional estimators arise in dynamic models for panel data. I then show how to correct for these problems using generalized method of moments estimators. Finally, I demonstrate the usefulness of these methods with replications of analyses in the debate over the dynamics of party identification.

Experiments offer a useful methodological tool to examine issues of importance to political scientists. The historical and cultural differences between experiments in behavioral economics and social psychology are discussed. Issues of central concern to experimentalists are covered, including impact versus control, mundane versus experimental realism, internal versus external validity, deception, and laboratory versus field experiments. Advantages and disadvantages of experimentation are summarized.

We advocate for an experimental approach to the study of personality and politics. In particular, we propose an “interactionist” model of political behavior in which the cognitive and behavioral effects of dispositional variables are qualified by experimentally induced contexts. Our operating assumption is that the political effects of personality do not occur in a contextual vacuum, but instead are magnified by the presence of key precipitating or “activating” features of the political environment. We illustrate the approach with four experimental studies of authoritarianism. Results indicate that the effects of authoritarianism depend critically on the presence of situationally induced threat. More generally, we argue that interactions between personality variables and experimental treatments can lead to valuable insights about when and why personality makes a meaningful contribution to public opinion and political behavior. Finally, we close with a critique of the traditional skepticism toward experimentation in political science, and suggest that external validity is an overrated virtue when the research goal is the development of theory rather than the description of “real-world” phenomena.

This paper develops an estimator for models of election returns in multiparty elections. It shares the same functional formas the Katz—King estimator but is computationally simpler, can be used with any number of parties, and is based on more conventional distributional assumptions. Small sample properties of the estimator are derived, which makes it particularly useful in many of the applications where there are a relatively small number of voting districts. The distributional assumptions are contained in two elements. The first treats the observed votes as the outcomes resulting from sampling the voters in each district. The second stochastic element arises from the usual treatment of the stochastic term in a regression model, namely, the inability of the included variables and the linear form to match the underlying process perfectly. The model is then used to analyze the 1993 Polish parliamentary elections. The results from this analysis are used to develop Monte Carlo experiments comparing several different yet feasible estimators. The conclusion is that a number of accessible estimators, including the standard seemingly unrelated regression model and the Beck-Katz model with panel-corrected standard errors, are all good choices.

At the heart of attitudinal and strategic explanations of judicial behavior is the assumption that justices have policy preferences. In this paper we employ Markov chain Monte Carlo methods to fit a Bayesian measurement model of ideal points for all justices serving on the U.S. Supreme Court from 1953 through 1999. We are particularly interested in determining to what extent ideal points of justices change throughout their tenure on the Court. This is important because judicial politics scholars oftentimes invoke preference measures that are time invariant. To investigate preference change, we posit a dynamic item response model that allows ideal points to change systematically over time. Additionally, we introduce Bayesian methods for fitting multivariate dynamic linear models to political scientists. Our results suggest that many justices do not have temporally constant ideal points. Moreover, our ideal point estimates outperform existing measures and explain judicial behavior quite well across civil rights, civil liberties, economics, and federalism cases.

This article demonstrates how spatially dependent data with a categorical response variable can be addressed in a statistical model. We introduce the idea of an autologistic model where the response for one observation is dependent on the value of the response among adjacent observations. The autologistic model has likelihood function that is mathematically intractable, since the observations are conditionally dependent upon one another. We review alternative techniques for estimating this model, with special emphasis on recent advances using Markov chain Monte Carlo (MCMC) techniques. We evaluate a highly simplified autologistic model of conflict where the likelihood of war involvement for each nation is conditional on the war involvement of proximate states. We estimate this autologistic model for a single year (1988) via maximum pseudolikelihood and MCMC maximum likelihood methods. Our results indicate that the autologistic model fits the data much better than an unconditional model and that the MCMC estimates generally dominate the pseudolikelihood estimates. The autologistic model generates predicted probabilities greater than 0.5 and has relatively good predictive abilities in an out-of-sample forecast for the subsequent decade (1989 to 1998), correctly identifying not only ongoing conflicts, but also new ones.

A continuing tradition in contextual analysis locates individual citizens within spatially defined, aggregate settings in an effort to provide a more complete account of individual behavior. Given the increasing individual mobility within society, it is less than clear that geography continues to define the boundaries on meaningful aggregate contexts—people have become less tied to their geographic contexts, and technology makes it possible for citizens to maintain relationships independently of space, distance, and location. In this paper we pursue an analysis and set of analytic techniques that are designed to connect individual voters, their communication networks, and the geography that surrounds them. The analytic techniques utilize a unique data set that captures spatial dispersion in an individual's social and political network, and from these analyses we can draw two conclusions. First, spatial dispersion in a network does have an effect on interaction within the network; the world is not full of voters who operate independently of their geographic contexts. Second, spatial dispersion provides opportunities to connect citizens living in different geographic contexts, thereby creating bridges for communication across different contexts. These findings suggest that scholars might profitably incorporate geography as an important component of the complex relationships among and between individual citizens in explaining the role of the individual in modern democratic politics.

Katz and King have previously proposed a statistical model for multiparty election data. They argue that ordinary least-squares (OLS) regression is inappropriate when the dependent variable measures the share of the vote going to each party, and they recommend a superior technique. Regrettably, the Katz-King model requires a high level of statistical expertise and is computationally demanding for more than three political parties. We offer a sophisticated yet convenient alternative that involves seemingly unrelated regression (SUR). SUR is nearly as easy to use as OLS yet performs as well as the Katz-King model in predicting the distribution of votes and the composition of parliament. Moreover, it scales easily to an arbitrarily large number of parties. The model has been incorporated into Clarify, a statistical suite that is available free on the Internet.

Some who have written about the logic of experimentation argue that random assignment of subjects to treatment conditions is an essential attribute of an experiment. Others disagree. Rather than treating this as a matter of dueling definitions, we consider experiments without randomization from a theoretical perspective. Our central contention here is that, for some research questions, theory dictates systematic (not random) assignment of respondents to experimental conditions. Two such areas of inquiry are research on political tolerance and on institutional legitimacy. This article gives cursory attention to the former body of work and detailed attention to the latter, based on an experiment conducted in a survey in 2001 on the consequences of the American presidential election for institutional legitimacy. Because in both instances theory requires nonrandom assignment, the problem becomes one of identifying the costs of nonrandomization (threats to internal validity) and specifying analytical techniques that might ameliorate those costs. Consequently, we present results from a statistical approach that addresses the problem of nonrandomization. The most important claim of this article is that theory ought to specify research design, including experimental designs, and that dogmatic attachment to one definition of experiment will not serve the discipline of political science.

Statistical models are often extended to explore the aggregate impact of policy reforms. After discussing these techniques and the incorporation of prediction uncertainty, this article examines the effects of registration reform in an analytic framework that explicitly considers the two stages that defined electoral participation throughout the 20th century in the United States—registration and then voting. Using selection bias techniques, the effects of counterfactual registration reform conditions are explored on the aggregate level of participation and the nature of representation in the electoral process. These offer a richer baseline of the impact of policy changes than previous work by directly exploring the expected level of dropoff in going to the polls by new registrants. Results indicate that the dropoff between registration and voting would be expected to increase as more individuals become registered. In addition, while turnout due to reforms among projected “new registrants” shows potentially larger biases than those among existing registrants, because of the different bases of registration the changes would still lead to a modest reduction in the disparity between actual group sizes and their role in elections.

Previous researchers have argued that necessary and/or sufficient causes should be tested through research designs that consider only cases with limited combinations of scores on the independent and the dependent variables. I explore the utility for causal inference of the design proposed by these authors, as compared to an “All Cases Design.” I find that, if researchers define the population carefully and appropriately, each case in the population contributes to causal inference and is therefore useful. Previous authors reject this claim on the basis of a view that holds constant the marginal distribution of either the dependent or the independent variable across the working and the alternate hypotheses. I argue that this restriction is not generally appropriate, and hence, an analysis that samples from the entire population is logically defensible. I also argue that this design is more statistically efficient. A reanalysis of two well-known studies demonstrates that sampling from all cases in the relevant population produces greater confidence in the hypothesis than sampling only from cases that experience the outcome.

The ultimatum game is a standard instrument for laboratory experimentalists. It has been replicated in a large number of environments and points to special considerations for fairness. Although it has been popular in the experimental community, researchers have not harnessed all the statistical power they should to evaluate the dynamics at work in this type of a bargaining game. This research uses two planned treatments, the first involving a signaling condition concerning a subject's “type”, and the second a price effect built into the structure of the game. We find that there are no significant main effects as a result of the signaling condition of a subject's type, but that there are strong effects as a result of the different payoff parameters. Using a variety of multivariate models we find important, nonobvious interactions with the gender of the subjects. The lesson that we take away from this research is that experimentalists can learn more from data collected in the tightly controlled laboratory environment by using statistical techniques that complement their research designs.

Katz and King have previously developed a model for predicting or explaining aggregate electoral results in multiparty democracies. Their model is, in principle, analogous to what least-squares regression provides American political researchers in that two-party system. Katz and King applied their model to three-party elections in England and revealed a variety of new features of incumbency advantage and sources of party support. Although the mathematics of their statistical model covers any number of political parties, it is computationally demanding, and hence slow and numerically imprecise, with more than three parties. In this paper we produce an approximate method that works in practice with many parties without making too many theoretical compromises. Our approach is to treat the problem as one of missing data. This allows us to use a modification of the fast EMis algorithm of King, Honaker, Joseph, and Scheve and to provide easy-to-use software, while retaining the attractive features of the Katz and King model, such as the t distribution and explicit models for uncontested seats.

This paper examines the role of spatial effects in ecological inference. Both formally and through simulation experiments, we consider the problems associated with ecological inference and cross-level inference methods in the presence of increasing degrees of spatial autocorrelation. Past assessments of spatial autocorrelation in aggregate data analysis focused on unidimensional, one-directional processes that are not representative of the full complexities caused by spatial autocorrelation. Our analysis is more complete and representative of true forms of spatial autocorrelation and pays particular attention to the specification of spatial autocorrelation in models with random coefficient variation. Our assessment focuses on the effects of this specification on the bias and precision of parameter estimates.