AbstractpoLCA is a software package for the estimation of latent class and latent class regression models for polytomous outcome variables, implemented in the R statistical computing environment. Both models can be called using a single simple command line. The basic latent class model is a finite mixture model in which the component distributions are assumed to be multi-way cross-classification tables with all variables mutually independent. The latent class regression model further enables the researcher to estimate the effects of covariates on predicting latent class membership. poLCA uses expectation-maximization and Newton-Raphson algorithms to find maximum likelihood estimates of the model parameters.
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.
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.
Why do some women in Muslim countries 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 either to marriage or employment to achieve financial security. Using cross-national public opinion data from eighteen countries with significant Muslim populations, we 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 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.
T he 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, eliteled 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.
The global food crisis of 2008 led to renewed interest in global food insecurity and how macro-level food prices impact household and individual level wellbeing. There is debate over the extent to which food price increases in 2008 eroded food security, the extent to which this effect was distributed across rural and urban locales, and the extent to which rural farmers might have benefited. Ethiopia’s food prices increased particularly dramatically between 2005 and 2008 and here we ask whether there was a concomitant increase in household food insecurity, whether this decline was distributed equally across rural, urban, and semi-urban locales, and to what extent pre-crisis household capacities and vulnerabilities impacted 2008 household food insecurity levels. Data are drawn from a random sample of 2610 households in Southwest Ethiopia surveyed 2005/6 and again in mid to late 2008. Results show broad deterioration of household food insecurity relative to baseline but declines were most pronounced in the rural areas. Wealthier households and those that were relatively more food secure in 2005/6 tended to be more food secure in 2008, net of other factors, and these effects were most pronounced in urban areas. External shocks, such as a job loss or loss of crops, experienced by households were also associated with worse food insecurity in 2008 but few other household variables were associated with 2008 food insecurity. Our results also showed that rural farmers tended to produce small amounts for sale on markets, and thus were not able to enjoy the potential benefits that come from greater crop prices. We conclude that poverty, and not urban/rural difference, is the important variable for understanding the risk of food insecurity during a food crisis and that many rural farmers are too poor to take advantage of rapid rises in food prices.
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