What do we really know about applicants to graduate school? How much information is in an applicant's file? What do we learn by having graduate admissions committees read and score applicant files? In this article, I develop a statistical model for measuring applicant quality, combining the information in the committee members' ordinal ratings with the information in applicants' GRE scores. The model produces estimates of applicant quality purged of the influence of committee members' preferences over ostensibly extraneous applicant characteristics, such as gender and intended field of study. An explicitly Bayesian approach is adopted for estimation and inference, making it straightforward to obtain confidence intervals not only on latent applicant quality but over rank orderings of applicants and the probability of belonging in a set of likely admittees. Using data from applications to a highly ranked political science graduate program, I show that there is considerable uncertainty in estimates of applicant quality, making it impossible to make authoritative distinctions as to quality among large portions of the applicant pool. The multiple rater model I develop here is extremely flexible and has applications in fields as diverse as judicial politics, legislative politics, international relations, and public opinion.

This article investigates the effect of social conformity on voting behavior. Past research shows that many people vote to conform with the social norm that voting is a civic duty. The hypothesis here is that when conformity motivates people to vote, it also stimulates conformist behavior among some voters when they decide which party to vote for. This produces a distinctive relationship between voter turnout and the distribution of votes among parties—a relationship not anticipated by rational choice theory. I test a mathematical model of this behavior with linear and nonlinear regression analyses of state-level data for presidential elections in the United States from 1904 to 1996, longitudinal data on parliamentary elections in Western Europe over most of the twentieth century, and cross-sectional data for recent elections in Western Europe, Eastern Europe, and Russia. The results generally validate the model.

While many areas of research in political science draw inferences from temporally aggregated data, rarely have researchers explored how temporal aggregation biases parameter estimates. With some notable exceptions (Freeman 1989, Political Analysis 1:61–98; Alt et al. 2001, Political Analysis 9:21–44; Thomas 2002, “Event Data Analysis and Threats from Temporal Aggregation”) political science studies largely ignore how temporal aggregation affects our inferences. This article expands upon others' work on this issue by assessing the effect of temporal aggregation decisions on vector autoregressive (VAR) parameter estimates, significance levels, Granger causality tests, and impulse response functions. While the study is relevant to all fields in political science, the results directly apply to event data studies of conflict and cooperation. The findings imply that political scientists should be wary of the impact that temporal aggregation has on statistical inference.

Research designs that call for the estimation of time trends in legislator ideal points must allow for changes in underlying policy spaces. Simply put, a set of legislator ideal points from one such space cannot necessarily be compared with a set of ideal points from another. Rothenberg and Sanders's (2000, American Journal of Political Science 44:316–325) analysis of shirking in Congress does not internalize this point insofar as it assumes that legislator ideal points from adjoining congressional sessions lie in a common space. While it may be the case that members of Congress shirk ideologically when approaching retirement, the regression estimates at the heart of Rothenberg and Sanders neither support this possibility nor constitute evidence against it.

Multimodal, high-dimension posterior distributions are well known to cause mixing problems for standard Markov chain Monte Carlo (MCMC) procedures; unfortunately such functional forms readily occur in empirical political science. This is a particularly important problem in applied Bayesian work because inferences are made from finite intervals of the Markov chain path. To address this issue, we develop and apply a new MCMC algorithm based on tempered transitions of simulated annealing, adding a dynamic element that allows the chain to self-tune its annealing schedule in response to current posterior features. This important feature prevents the Markov chain from getting trapped in minor modal areas for long periods of time. The algorithm is applied to a probabilistic spatial model of voting in which the objective function of interest is the candidate's expected return. We first show that such models can lead to complex target forms and then demonstrate that the dynamic algorithm easily handles even large problems of this kind.

While Herron (2004, Political Analysis 12:182–190) is correct that sensitivity to changes in underlying scale and how they affect estimates and inferences is generally important, our assumption in Rothenberg and Sanders (2000, American Journal of Political Science 44:310–319) that W-NOMINATE scales can be directly compared from one Congress to another to study legislative shirking is quite defensible because scale variability is not a substantial problem. Not only are the assumptions in our original analysis regarding variability very reasonable, because any variability is quite small, but effects on consistency are marginal and, to the degree that they are relevant, indicate that our test of the shirking hypothesis is conservative. Furthermore, even generous estimates of variability in W-NOMINATE between one immediate Congress and another have little impact on results. In addition, Herron's analysis includes an unaddressed censoring problem that again, while unlikely to have much substantive relevance, indicates that Rothenberg and Sanders have worked against themselves in trying to find shirking. In conclusion, the issues that Herron highlights are of marginal consequence for the original analysis and, to the extent they matter, only buttress the findings generated and the inferences drawn.

This study replicates and extends Groseclose's (1994) tests of Congressional committee composition hypotheses for the 99th Congress. Alternative hypotheses pit partisan explanations of committee organization against the informational roles committees can play in producing “good” public policy. Other hypotheses explore the likelihood that committees reflect (rather than diverge from) floor preferences. Predictably, empirical tests of such hypotheses have produced no scholarly consensus. Groseclose (1994) enters the debate by using Monte Carlo simulations to test alternative hypotheses of Congressional committee organization, and in so doing, he makes few assumptions (and specifically avoids problematic ones) about the ideological distribution of committee and floor members. For example, difference of means tests, often used to evaluate the significance of floorcommittee divergence, assume that preferences are distributed normally and that the mean scores of a committee and floor are the correct test statistics. Groseclose asserts that the normality assumption is less convincing for small committees and that the median score is more appropriate.

It is impossible to overstate the importance of doing original research based on primary source material in scientific analysis (see, for example, King 1995). Failing to consult primary sources, and by definition relying on secondary sources, can lead to incorrect analyses and mistaken conclusions. An exemplary case is the apparent mystery of whether justices strategically retire and exactly when Congress passed legislation allowing federal judges to retire with benefits upon reaching age 65. One study cited 1937 as the year and based its analysis of judicial retirement on this “fact” (Abraham 1975). Subsequent studies cited the original piece without ever checking the original data—in this case a federal statute—and “replicated” the same mistaken analyses (see Atkinson 1999). In fact, the actual year this legislation was passed was 1954. This 17-year error not only makes a significant difference in the analysis of judicial retirement decisions, but also shows the importance of verification and replication in political research.