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.

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.

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.

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.

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.