We address the problem that occurs when inferences about counterfactuals—predictions, “what-if” questions, and causal effects—are attempted far from the available data. The danger of these extreme counterfactuals is that substantive conclusions drawn from statistical models that fit the data well turn out to be based largely on speculation hidden in convenient modeling assumptions that few would be willing to defend. Yet existing statistical strategies provide few reliable means of identifying extreme counterfactuals. We offer a proof that inferences farther from the data allow more model dependence and then develop easy-to-apply methods to evaluate how model dependent our answers would be to specified counterfactuals. These methods require neither sensitivity testing over specified classes of models nor evaluating any specific modeling assumptions. If an analysis fails the simple tests we offer, then we know that substantive results are sensitive to at least some modeling choices that are not based on empirical evidence. Free software that accompanies this article implements all the methods developed.

Bayesian approaches to the study of politics are increasingly popular. But Bayesian approaches to modeling multiple time series have not been critically evaluated. This is in spite of the potential value of these models in international relations, political economy, and other fields of our discipline. We review recent developments in Bayesian multi-equation time series modeling in theory testing, forecasting, and policy analysis. Methods for constructing Bayesian measures of uncertainty of impulse responses (Bayesian shape error bands) are explained. A reference prior for these models that has proven useful in short- and medium-term forecasting in macroeconomics is described. Once modified to incorporate our experience analyzing political data and our theories, this prior can enhance our ability to forecast over the short and medium terms complex political dynamics like those exhibited by certain international conflicts. In addition, we explain how contingent Bayesian forecasts can be constructed, contingent Bayesian forecasts that embody policy counterfactuals. The value of these new Bayesian methods is illustrated in a reanalysis of the Israeli-Palestinian conflict of the 1980s.

Theories of voting tell us that party vote dynamics have two core components, long-run equilibria and more or less rapid returns to equilibria after short-run deviations. Statistical models used to represent these theories, however, tend to emphasize one component or the other. The unbalanced emphasis leaves the theoretical ideas underspecified and produces biased estimates of both long- and short-run components of vote dynamics. We specify the two components in the familiar form of an error correction model and demonstrate its advantages in terms of its theoretical consistency, simplicity, and precise prediction of whether something is wrong. We illustrate its usefulness by applying it to two sets of analyses reported in the literature and showing that it usually changes the conclusions reported in regard to both the equilibrium levels of party competition and the strength of restoring forces.

This special issue of Political Analysis engages in a dialogue between qualitative and quantitative methods. It proposes that each has something to say to the other and more generally has a contribution to make to empirical social science.

The quantitative and qualitative research traditions can be thought of as distinct cultures marked by different values, beliefs, and norms. In this essay, we adopt this metaphor toward the end of contrasting these research traditions across 10 areas: (1) approaches to explanation, (2) conceptions of causation, (3) multivariate explanations, (4) equifinality, (5) scope and causal generalization, (6) case selection, (7) weighting observations, (8) substantively important cases, (9) lack of fit, and (10) concepts and measurement. We suggest that an appreciation of the alternative assumptions and goals of the traditions can help scholars avoid misunderstandings and contribute to more productive “cross-cultural” communication in political science.

The use of the proximity model to represent the relationship between citizens' policy attitudes and the positions of candidates on the issues of the day has considerable appeal because it offers a bridge between theoretical models of political behavior and empirical work. However, there is little consensus among applied researchers about the appropriate representation of voter behavior with respect to the measurement of issue distance, candidate location, or whether to allow heterogeneity in the weight that each individual places on particular issues. Each of these choices suggests a different, and reasonably complicated, nonlinear relationship between voter utility and candidate and voter issue positions which may have a meaningful influence on the substantive conclusions drawn by the researcher. Yet, little attention has been given to the best way to represent the proximity model in applied work. The purpose of this paper is to identify which forms of the proximity model work best, with particular consideration given to the identification of functional forms that are invariant to the choice of scale for the independent variables.

This article provides a model for analyzing approval voting elections. Within a standard probabilistic spatial voting setting, we show that principal component analysis makes it possible to derive candidates' relative locations from the approval votes. We apply this technique to original experimental data from the French 2002 presidential election.

In the social sciences, randomized experimentation is the optimal research design for establishing causation. However, for a number of practical reasons, researchers are sometimes unable to conduct experiments and must rely on observational data. In an effort to develop estimators that can approximate experimental results using observational data, scholars have given increasing attention to matching. In this article, we test the performance of matching by gauging the success with which matching approximates experimental results. The voter mobilization experiment presented here comprises a large number of observations (60,000 randomly assigned to the treatment group and nearly two million assigned to the control group) and a rich set of covariates. This study is analyzed in two ways. The first method, instrumental variables estimation, takes advantage of random assignment in order to produce consistent estimates. The second method, matching estimation, ignores random assignment and analyzes the data as though they were nonexperimental. Matching is found to produce biased results in this application because even a rich set of covariates is insufficient to control for preexisting differences between the treatment and control group. Matching, in fact, produces estimates that are no more accurate than those generated by ordinary least squares regression. The experimental findings show that brief paid get-out-the-vote phone calls do not increase turnout, while matching and regression show a large and significant effect.

This article discusses the application of qualitative methods in analyzing causal complexity. In particular, the essay reviews how process tracing and systematic case comparisons can address path-dependent explanations. The article unpacks the concept of path dependence and its component elements of causal possibility, contingency, closure of alternatives, and constraints to the current path. The article then reviews four strengths that case studies bring to the study of path dependence: offering a detailed and holistic analysis of sequences in historical cases, being suitable for the study of rare events, facilitating the search for omitted variables that might lie behind contingent events, and allowing for the study of interaction effects within one or a few cases.

Multiplicative interaction models are common in the quantitative political science literature. This is so for good reason. Institutional arguments frequently imply that the relationship between political inputs and outcomes varies depending on the institutional context. Models of strategic interaction typically produce conditional hypotheses as well. Although conditional hypotheses are ubiquitous in political science and multiplicative interaction models have been found to capture their intuition quite well, a survey of the top three political science journals from 1998 to 2002 suggests that the execution of these models is often flawed and inferential errors are common. We believe that considerable progress in our understanding of the political world can occur if scholars follow the simple checklist of dos and don'ts for using multiplicative interaction models presented in this article. Only 10% of the articles in our survey followed the checklist.

A lagged dependent variable in an OLS regression is often used as a means of capturing dynamic effects in political processes and as a method for ridding the model of autocorrelation. But recent work contends that the lagged dependent variable specification is too problematic for use in most situations. More specifically, if residual autocorrelation is present, the lagged dependent variable causes the coefficients for explanatory variables to be biased downward. We use a Monte Carlo analysis to assess empirically how much bias is present when a lagged dependent variable is used under a wide variety of circumstances. In our analysis, we compare the performance of the lagged dependent variable model to several other time series models. We show that while the lagged dependent variable is inappropriate in some circumstances, it remains an appropriate model for the dynamic theories often tested by applied analysts. From the analysis, we develop several practical suggestions on when and how to use lagged dependent variables on the right-hand side of a model.

A variety of factors have been shown to influence position timing and the content of positions taken by legislators on important issues. In addition to these observed factors, I argue that unobserved factors such as behind-the-scenes lobbying and party loyalty may also influence position timing and position content. Although hypotheses about observed factors can be tested using traditional methods, hypotheses about unobserved factors cannot. To test for systematic effects of unobserved factors on position timing and content, I develop a seemingly unrelated discrete-choice duration estimator and apply it to data from the vote for the North American Free Trade Agreement. The results indicate that even after controlling for observed factors, there is still evidence that unobserved factors such as Presidential lobbying and/or party loyalty influence both choices.

We provide an introduction to the regression discontinuity design (RDD) and use the technique to evaluate models of sequential Senate elections predicting that the winning party for one Senate seat will receive fewer votes in the next election for the other seat. Using data on U.S. Senate elections from 1946 to 2004, we find strong evidence that the outcomes of the elections for the two Senate seats are independent.

Political scientists overwhelmingly seek evidence of causation by measuring changes in the mean of the distribution of the dependent variable. This article points out that some causal relationships produce changes in the variance, not the mean, of that distribution. It makes the case for variance-altering causation by demonstrating its empirical relevance to political scientists. The article also lays out an array of causal mechanisms—under the general headings of aggregation, contagion, and constraint—in order to demonstrate the logical coherence of variance-altering causation and the many ways in which it can arise. The discussion highlights the often-stringent empirical and logical requirements that must be met if the researcher hopes to make concrete predictions about changes in variance.

Studies of voter turnout across states find that those with more facilitative registration laws have higher turnout rates. Eliminating registration barriers altogether is estimated to raise voter participation rates by up to 10%. This article presents panel estimates of the effects of introducing registration that exploits changes in registration laws and turnout within states. New York and Ohio imposed registration requirements on all of their counties in 1965 and 1977, respectively. We find that the introduction of registration to counties that did not previously require registration decreased participation over the long term by three to five percentage points. Though significant, this is lower than estimates of the effects of registration from cross-sectional studies and suggests that expectations about the effects of registration reforms on turnout may be overstated.

Examining the influence of campaign tone on individual turnout in the 1990 U.S. Senate elections, this note revisits Kahn and Kenney's conclusion that the political profile of citizens (as based on partisanship, level of political interest, and level of political expertise) conditions their responsiveness. Implementing an appropriate modeling strategy for making group comparisons, our analyses do not provide statistical support for the conditional effects that they highlight. More generally, our results do reinforce Kahn and Kenney's finding that negativity in tone of news coverage mobilizes citizens, but they do not reveal significant turnout influence for television advertising tone.

Using large-scale network analysis I map the cosponsorship networks of all 280,000 pieces of legislation proposed in the U.S. House and Senate from 1973 to 2004. In these networks, a directional link can be drawn from each cosponsor of a piece of legislation to its sponsor. I use a number of statistics to describe these networks such as the quantity of legislation sponsored and cosponsored by each legislator, the number of legislators cosponsoring each piece of legislation, the total number of legislators who have cosponsored bills written by a given legislator, and network measures of closeness, betweenness, and eigenvector centrality. I then introduce a new measure I call “connectedness” which uses information about the frequency of cosponsorship and the number of cosponsors on each bill to make inferences about the social distance between legislators. Connectedness predicts which members will pass more amendments on the floor, a measure that is commonly used as a proxy for legislative influence. It also predicts roll call vote choice even after controlling for ideology and partisanship.

Testing the fit of competing equilibrium solutions to extensive form games crucially depends on assumptions about the distribution of player types. To illustrate the importance of these assumptions for differentiating standard statistical models of strategic choice, I draw on a game previously analyzed by Lewis and Schultz (2003). The differences that they highlight between a pair of perfect Bayesian equilibrium and quantal response equilibrium models are not produced by signaling and updating dynamics as claimed, but are instead produced by different assumptions about the distribution of player types. The method of analysis developed and the issues raised are applicable to a broad range of structural models of conflict and bargaining.

Because of its inherently asymmetric nature, set-theoretic analysis offers many interesting contrasts with analysis based on correlations. Until recently, however, social scientists have been slow to embrace set-theoretic approaches. The perception was that this type of analysis is restricted to primitive, binary variables and that it has little or no tolerance for error. With the advent of “fuzzy” sets and the recognition that even rough set-theoretic relations are relevant to theory, these old barriers have crumbled. This paper advances the set-theoretic approach by presenting simple descriptive measures that can be used to evaluate set-theoretic relationships, especially relations between fuzzy sets. The first measure, “consistency,” assesses the degree to which a subset relation has been approximated, whereas the second measure, “coverage,” assesses the empirical relevance of a consistent subset. This paper demonstrates further that set-theoretic coverage can be partitioned in a manner somewhat analogous to the partitioning of explained variation in multiple regression analysis.

Robert Jackson and Jason Sides (2005) conclude in their article, Revisiting the Influence of Campaign Tone on Turnout in Senate Elections, “We are hard-pressed to conclude that respondents' political profiles condition the influence of campaign tone on their turnout behavior.… Kahn and Kenney's conclusions about differential citizen responsiveness to campaign negativity should not become part of accepted wisdom in this area of scholarship.” We disagree and our reasoning rests on three points: the measurement and operationalization of a key variable: mudslinging; the selection of an appropriate estimation strategy; and the employment of theoretical expectations to make sense of the central findings.