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The relationship between a party's popular vote share and legislative seat share—its seats—votesswing ratio—is a key characteristic of democratic representation. This article introduces a general approach to estimating party-specific swing ratios in multiparty legislative elections, given results from only a single election. I estimate the joint density of party vote shares across districts using a finite mixture model for compositional data and then computationally evaluate this distribution to produce parties' expected change in legislative seats for plausible changes in their vote share. The method easily extends to systems with any number of parties, employing both majoritarian and proportional electoral rules. Applications to legislative elections in the United States, United Kingdom, Canada, and Botswana demonstrate how parties' swing ratios vary both within countries and over time, indicating that parties under majoritarian electoral rules are subject to unique and possibly divergent geographic—political constraints.

Contingency tables are among the most basic and useful techniques available for analyzing categorical data, but they produce highly imprecise estimates in small samples or for population subgroups that arise following repeated stratification. I demonstrate that preprocessing an observed set of categorical variables using a latent class model can greatly improve the quality of table-based inferences. As a density estimator, the latent class model closely approximates the underlying joint distribution of the variables of interest, which enables reliable estimation of conditional probabilities and marginal effects, even among subgroups containing fewer than 40 observations. Though here focused on applications to public opinion, the procedure has a wide range of potential uses. I illustrate the benefits of the latent class model—based approach for greatly improved accuracy in estimating and forecasting vote preferences within small demographic subgroups using survey data from the 2004 and 2008 U.S. presidential election campaigns.

The battle for public opinion in the Islamic world is an ongoing priority for U.S. diplomacy. The current debate over why many Muslims hold anti-American views revolves around whether they dislike fundamental aspects of American culture and government, or what Americans do in international affairs. We argue, instead, that Muslim anti-Americanism is predominantly a domestic, elite-led phenomenon that intensifies when there is greater competition between Islamist and secular-nationalist political factions within a country. Although more observant Muslims tend to be more anti-American, paradoxically the most anti-American countries are those in which Muslim populations are less religious overall, and thus more divided on the religious–secular issue dimension. We provide case study evidence consistent with this explanation, as well as a multilevel statistical analysis of public opinion data from nearly 13,000 Muslim respondents in 21 countries.

Why do some Muslim women adopt fundamentalist Islamic value systems that promote gender-based inequalities while others do not? This article considers the economic determinants of fundamentalist beliefs in the Muslim world, as women look to either marriage or employment to achieve financial security. Using cross-national public opinion data from eighteen countries with significant Muslim populations, the authors apply a latent class model to characterize respondents according to their views on gender norms, political Islam, and personal religiosity. Among women, lack of economic opportunity is a stronger predictor of fundamentalist belief systems than socioeconomic class. Cross-nationally, fundamentalism among women is most prevalent in poor countries and in those with a large male-female wage gap. These findings have important implications for the promotion of women's rights, the rise of political Islam, and the development of democracy in the Muslim world.

Empirical analyses in social science frequently confront quantitative data that are clustered or grouped. To account for group-level variation and improve model fit, researchers will commonly specify either a fixed- or random-effects model. But current advice on which approach should be preferred, and under what conditions, remains vague and sometimes contradictory. This study performs a series of Monte Carlo simulations to evaluate the total error due to bias and variance in the inferences of each model, for typical sizes and types of datasets encountered in applied research. The results offer a typology of dataset characteristics to help researchers choose a preferred model. Adapted from the source document.

Empirical analyses in social science frequently confront quantitative data that are clustered or grouped. To account for group-level variation and improve model fit, researchers will commonly specify either a fixed- or random-effects model. But current advice on which approach should be preferred, and under what conditions, remains vague and sometimes contradictory. This study performs a series of Monte Carlo simulations to evaluate the total error due to bias and variance in the inferences of each model, for typical sizes and types of datasets encountered in applied research. The results offer a typology of dataset characteristics to help researchers choose a preferred model.

Researchers often use as dependent variables quantities estimated from auxiliary data sets. Estimated dependent variable (EDV) models arise, for example, in studies where counties or states are the units of analysis and the dependent variable is an estimated mean, proportion, or regression coefficient. Scholars fitting EDV models have generally recognized that variation in the sampling variance of the observations on the dependent variable will induce heteroscedasticity. We show that the most common approach to this problem, weighted least squares, will usually lead to inefficient estimates and underestimated standard errors. In many cases, OLS with White's or Efron heteroscedastic consistent standard errors yields better results. We also suggest two simple alternative FGLS approaches that are more efficient and yield consistent standard error estimates. Finally, we apply the various alternative estimators to a replication of Cohen's (2004) cross-national study of presidential approval.