This study of new political parties in the Third Wave democracies of Bolivia, Chile, Ecuador, and Venezuela conceptualizes the early life of a party as a developmental phase. The analysis uses latent trajectory modeling to identify five qualitatively distinctive performance profiles, which the author calls “explosive,” “contender,” “flash,” “flat,” and “flop” trajectories. This finding challenges the conventional approaches used in the study of new party performance, where scholars classify parties using subjective criteria, often into the successful/failed dichotomy. In unstable party systems, where we expect greater diversity in the performance profiles of new parties, latent trajectory modeling is preferred because it yields a result more consistent with extant theorizing on new parties. In stable systems, as in the case of Chile, the approaches can yield similar results. Nevertheless, the case of Venezuela (1958–88) demonstrates that even in stable party systems, the modeling approach used here can identify important variation that alternatives might miss.

The consolidation of polling places in the Vestal Central School District in New York State during the district's 2006 budget referendum provides a naturalistic setting to study the effects of polling consolidation on voter turnout on an electorate quite distinct from previous work by Brady and McNulty (2004, The costs of voting: Evidence from a natural experiment. Presented at the Annual Meeting of the Society for Political Methodology, Palo Alto, CA). In particular, voters in local elections are highly motivated and therefore might be thought to be less affected by poll consolidation. Nevertheless, through a matching analysis we find that polling consolidation decreases voter turnout substantially, by about seven percentage points, even among this electorate, suggesting that even habitual voters can be dissuaded from going to the polls. This finding has implications for how election administrators ought to handle cost-cutting measures like consolidation.

We evaluate two diagnostic tools used to determine if counterfactual analysis requires extrapolation. Counterfactuals based on extrapolation are model dependent and might not support empirically valid inferences. The diagnostics help researchers identify those counterfactual “what if” questions that are empirically plausible. We show, through simple Monte Carlo experiments, that these diagnostics will often detect extrapolation, suggesting that there is a risk of biased counterfactual inference when there is no such risk of extrapolation bias in the data. This is because the diagnostics are affected by what we call the n/k problem: as the number of data points relative to the number of explanatory variables decreases, the diagnostics are more likely to detect the risk of extrapolation bias even when such risk does not exist. We conclude that the diagnostics provide too severe a test for many data sets used in political science.

Three-decade-old research suggests that although regression coefficients obtained with ordinary least squares (OLS) are optimal for fitting a model to a sample, unless the N over which the model was estimated is large, they are generally not very much superior and frequently inferior to equal weights or unit weights for making predictions in a validating sample. Yet, that research has yet to make an impact on presidential elections forecasting, where models are estimated with fewer than 25 elections, and often no more than 15. In this research note, we apply equal weights to generate out-of-sample and one-step-ahead predictions in two sets of related presidential elections models, Fair's presidential equation and the fiscal model. We find that most of the time, using equal weights coefficients does improve the forecasting performance of both.

In response to the data-based measures of model dependence proposed in King and Zeng (2006), Sambanis and Michaelides (2008) propose alternative measures that rely upon assumptions untestable in observational data. If these assumptions are correct, then their measures are appropriate and ours, based solely on the empirical data, may be too conservative. If instead, and as is usually the case, the researcher is not certain of the precise functional form of the data generating process, the distribution from which the data are drawn, and the applicability of these modeling assumptions to new counterfactuals, then the data-based measures proposed in King and Zeng (2006) are much preferred. After all, the point of model dependence checks is to verify empirically, rather than to stipulate by assumption, the effects of modeling assumptions on counterfactual inferences.