Theories that posit complex causation, or multiple causal paths, pervade the study of politics but have yet to find accurate statistical expression. To remedy this situation I derive new econometric procedures, Boolean probit and logit, based on the logic of complexity. The solution provides an answer to a puzzle in the rational deterrence literature: the divergence between theory and case-study findings, on the one hand, and the findings of quantitative studies, on the other, on the issue of the role of capabilities and willingness in the initiation of disputes. It also makes the case that different methodological traditions, rather than settling into “separate but equal” status, can instead inform and enrich one another.

Recent work in survey research has made progress in estimating models involving selection bias in a particularly difficult circumstance—all nonrespondents are unit nonresponders, meaning that no data are available for them. These models are reasonably successful in circumstances where the dependent variable of interest is continuous, but they are less practical empirically when it is latent and only discrete outcomes or choices are observed. I develop a method in this article to estimate these models that is much more practical in terms of estimation. The model uses a small amount of auxiliary information to estimate the selection equation parameters, which are then held fixed while estimating the equation of interest parameters in a maximum-likelihood setting. After presenting Monte Carlo analyses to support the model, I apply the technique to a substantive problem: Which interest groups are likely to to be involved in support of potential initiatives to achieve their policy goals?

Selection bias is an important but often neglected problem in comparative research. While comparative case studies pay some attention to this problem, this is less the case in broader cross-national studies, where this problem may appear through the way the data used are generated. The article discusses three examples: studies of the success of newly formed political parties, research on protest events, and recent work on ethnic conflict. In all cases the data at hand are likely to be afflicted by selection bias. Failing to take into consideration this problem leads to serious biases in the estimation of simple relationships. Empirical examples illustrate a possible solution (a variation of a Tobit model) to the problems in these cases. The article also discusses results of Monte Carlo simulations, illustrating under what conditions the proposed estimation procedures lead to improved results.

This article builds a nonparametric method for inference from roll-call cohesion scores. Cohesion scores have been a staple of legislative studies since the publication of Rice's 1924 thesis. Unfortunately, little effort has been dedicated to understanding their statistical properties or relating them to existing models of legislative behavior. I show how a common use of cohesion scores, testing for distinct voting blocs, is severely biased toward Type I error, practically guaranteeing significant findings even when the null hypothesis is correct. I offer a nonparametric method—permutation analysis—that solves the bias problem and provides for simple and intuitive inference. I demonstrate with an examination of roll-call voting data from the Brazilian National Congress.

In public opinion research, response latency is a measure of attitude accessibility, which is the ease or swiftness with which an attitude comes to mind when a respondent is presented with a survey question. Attitude accessibility represents the strength of the association in memory between an attitude object and an evaluation of the object. Recent research shows that attitude accessibility, as measured by response latency, casts light on a wide range of phenomena of public opinion and political behavior. We discuss response latency methodology for survey research and advocate the use of latent response latency timers (which are invisible both to respondents and interviewers) as a low cost, low-maintenance alternative to traditional methods of measuring response latency in public opinion surveys. We show that with appropriate model specification latent response latency timers may provide a suitable alternative to the more complicated and expensive interviewer-activated timers.

Numerical issues matter in statistical analysis. Small errors occur when numbers are translated from paper and pencil into the binary world of computers. Surprisingly, these errors may be propagated and magnified through binary calculations, eventually producing statistical estimates far from the truth. In this replication and extension article, we look at one method of verifying the accuracy of statistical estimates by running these same data and models on multiple statistical packages. We find that for two published articles, Nagler (1994, American Journal of Political Science 38:230-255) and Alvarez and Brehm (1995, American Journal of Political Science 39:1055-1089), results are dependent on the statistical package used. In the course of our replications, we uncover other pitfalls that may prevent accurate replication, and make recommendations to ensure the ability for future researchers to replicate results.