Although published works rarely include causal estimates from more than a few model specifications, authors usually choose the presented estimates from numerous trial runs readers never see. Given the often large variation in estimates across choices of control variables, functional forms, and other modeling assumptions, how can researchers ensure that the few estimates presented are accurate or representative? How do readers know that publications are not merely demonstrations that it is possible to find a specification that fits the author's favorite hypothesis? And how do we evaluate or even define statistical properties like unbiasedness or mean squared error when no unique model or estimator even exists? Matching methods, which offer the promise of causal inference with fewer assumptions, constitute one possible way forward, but crucial results in this fast-growing methodological literature are often grossly misinterpreted. We explain how to avoid these misinterpretations and propose a unified approach that makes it possible for researchers to preprocess data with matching (such as with the easy-to-use software we offer) and then to apply the best parametric techniques they would have used anyway. This procedure makes parametric models produce more accurate and considerably less model-dependent causal inferences.

We introduce the conditional frailty model, an event history model that separates and accounts for both event dependence and heterogeneity in repeated events processes. Event dependence and heterogeneity create within-subject correlation in event times thereby violating the assumptions of standard event history models. Simulations show the advantage of the conditional frailty model. Specifically they demonstrate the model's ability to disentangle the sources of within-subject correlation as well as the gains in both efficiency and bias of the model when compared to the widely used alternatives, which often produce conflicting conclusions. Two substantive political science problems illustrate the usefulness and interpretation of the model: state policy adoption and terrorist attacks.

Although political scientists have begun to investigate the properties of Internet surveys, much remains to be learned about the utility of the Internet mode for conducting major survey research projects such as national election studies. This paper addresses this topic by presenting the results of an extensive survey comparison experiment conducted as part of the 2005 British Election Study. Analyses show statistically significant, but generally small, differences in distributions of key explanatory variables in models of turnout and party choice. Estimating model parameters reveals that there are few statistically significant differences between coefficients generated using the in-person and Internet data, and the relative explanatory power of rival models is virtually identical for the two types of data. In general, the in-person and Internet data tell very similar stories about what matters for turnout and party preference in Britain. Determining if similar findings obtain in other countries should have high priority on the research agenda for national election studies.

Since the inception of the American National Election Study (ANES) in the 1940s, data have been collected via face-to-face interviewing in the homes of members of area probability samples of American adults, the same gold-standard approach used by the U.S. Census Bureau, other federal agencies, and some nongovernment researchers for many of the most high-profile surveys conducted today. This paper explores whether comparable findings about voters and elections would be obtained by a different, considerably less expensive method: Internet data collection from nonprobability samples of volunteer respondents. Comparisons of the 2000 and 2004 ANES data (collected via face-to-face interviewing with national probability samples) with simultaneous Internet surveys of volunteer samples yielded many differences in the distributions of variables and in the associations between variables (even controlling for differences between the samples in reported interest in politics). Accuracy was higher for the face-to-face/probability sample data than for the Internet/volunteer sample data in 88% of the possible comparisons. This suggests that researchers interested in assuring the accuracy of their findings in describing populations should rely on face-to-face surveys of probability samples rather than Internet samples of volunteer respondents.

We construct the complete network of 26,681 majority opinions written by the U.S. Supreme Court and the cases that cite them from 1791 to 2005. We describe a method for using the patterns in citations within and across cases to create importance scores that identify the most legally relevant precedents in the network of Supreme Court law at any given point in time. Our measures are superior to existing network-based alternatives and, for example, offer information regarding case importance not evident in simple citation counts. We also demonstrate the validity of our measures by showing that they are strongly correlated with the future citation behavior of state courts, the U.S. Courts of Appeals, and the U.S. Supreme Court. In so doing, we show that network analysis is a viable way of measuring how central a case is to law at the Court and suggest that it can be used to measure other legal concepts.

This paper considers a simple distribution-free test for nonnested model selection. The new test is shown to be asymptotically more efficient than the well-known Vuong test when the distribution of individual log-likelihood ratios is highly peaked. Monte Carlo results demonstrate that for many applied research situations, this distribution is indeed highly peaked. The simulation further demonstrates that the proposed test has greater power than the Vuong test under these conditions. The substantive application addresses the effect of domestic political institutions on foreign policy decision making. Do domestic institutions have effects because they hold political leaders accountable, or do they simply promote political norms that shape elite bargaining behavior? The results indicate that the latter model has greater explanatory power.