2026 Stata Sociology Virtual Symposium

geoplot is a powerful Stata command for drawing maps from shape files and other datasets. Multiple layers of elements such as regions, borders, lakes, roads, labels, and symbols can be freely combined and the look of elements (e.g. their color) can be varied depending on the values of variables. Compared to previous solutions in Stata, geoplot provides more user convenience, more functionality, and more flexibility. In this talk I will give an overview of the command and illustrate its use with examples.

Abstract TBA.

Comparing effects is a common task for the applied data analyst. For example, tests of interaction involve comparing the effects of one variable at multiple levels of another variable. Comparisons of effect sizes within models involve quantifying each focal variable?s effect and testing their equality. Cross-model comparisons are also common, for example when comparing processes across different groups. Tests of attenuation like mediation similarly involve comparisons across multiple models. Despite their ubiquity, such tests have challenges: coefficients that don?t quantify effects in the metric of interest, rescaling of coefficients in nonlinear/categorical models, and predictors on differing metrics. In this presentation, I detail the statistical underpinnings of within- and across-model comparisons, present a marginal effects framework that allows for comparisons across most any regression model or predictor type, and show how to automate these tests using the new suest2, mecompare, meinequality, and totalme commands. Compared to existing software, the new commands can be used for: (1) single or multiple models; (2) single-level, multilevel, and longitudinal models; (3) any amount of change for continuous predictors; (4) summary measures for nominal and ordinal variables; (5) comparisons across variables on different metrics; (6) comparisons across different model types; and (7) custom and nonstandard tests of effects. I demonstrate the utility of the approach and the new commands using publicly available social science data.

Abstract TBA.

Standard panel data models assume homoscedasticity, estimating only how covariates shift the mean of an outcome while treating variance as constant across observations. This assumption can obscure important dimensions of inequality, particularly when the dispersion of outcomes?not just their average level?differs systematically across groups or individuals. This presentation introduces xtvfreg, a user-written Stata command that jointly estimates mean and variance equations for panel data using an iterative weighted generalized least squares (GLS) procedure. Building on the framework proposed and demonstrated for answering a substantive question in Mooi-Reci and Liao (2025, European Sociological Review), xtvfreg allows researchers to model not only who has higher or lower outcomes (via the mean equation) but also who experiences greater or lesser dispersion in those outcomes (via the variance equation), separately by group when necessary. The presentation demonstrates the command?s syntax, walks through its iterative estimation algorithm, and provides a step-by-step application using the NLSY women?s wage panel data. Attendees will leave with the knowledge to implement xtvfreg in their own research and to think more carefully about variance as a substantive outcome of interest, not merely a nuisance parameter..